The goal of 'ggcoverage' is to simplify the process of visualizing genome coverage. It contains functions to 
    load data from BAM, BigWig or BedGraph files, create genome coverage plot, add various annotations to 
    the coverage plot, including base and amino acid annotation, GC annotation, gene annotation, transcript annotation, ideogram annotation and peak annotation.
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'.
Methods for searching through genealogical data and displaying the results. Plotting algorithms assist with data exploration and publication-quality image generation. Includes interactive genealogy visualization tools. Provides parsing and calculation methods for variables in descendant branches of interest. Uses the Grammar of Graphics.
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.
Implements the functionality of the 'fanplot' package as 'geoms' 
  for 'ggplot2'. Designed for summarising MCMC samples from a posterior 
  distribution, where a visualisation is desired for several values of a 
  continuous covariate. Increasing posterior intervals of the sampled quantity 
  are mapped to a continuous colour scale.
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.
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.
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>.
The extensive range of functions provided by this package makes it possible to draw highly versatile sequence logos. Features include, but not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other plots 'ggplot2' plots.
A 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.
Adds north symbols (18 options) and scale bars in kilometers,
    meters, nautical miles, or statue miles, to maps in geographic
    or metric coordinates created with 'ggplot2' or 'ggmap'.
The 'ggplot2' package provides a powerful set of tools
  for visualising and investigating data. The 'ggsoccer' package provides a
  set of functions for elegantly displaying and exploring soccer event data
  with 'ggplot2'. Providing extensible layers and themes, it is designed to
  work smoothly with a variety of popular sports data providers.
An implementation of 'ggplot2'-methods to present the composition of Solvency II Solvency Capital Requirement (SCR) as a series of concentric circle-parts. 
 Solvency II (Solvency 2) is European insurance legislation, coming in force by the delegated acts of October 10, 2014.
 <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ%3AL%3A2015%3A012%3ATOC>. 
 Additional files, defining the structure of the Standard Formula (SF) method of the SCR-calculation are provided.
 The structure files can be adopted for localization or for insurance companies who use Internal Models (IM).
 Options are available for combining smaller components, horizontal and vertical scaling, rotation, and plotting only some circle-parts.
 With outlines and connectors several SCR-compositions can be compared, for example in ORSA-scenarios (Own Risk and Solvency Assessment).
The aim of this package is to offer more variability of graphics based on the self-organizing maps.
Spatial data plus the power of the ggplot2 framework means easier mapping when input 
  data are already in the form of spatial objects.
Additional annotations, stats, geoms and scales for plotting 
    "light" spectra with 'ggplot2', together with specializations of ggplot()
    and autoplot() methods for spectral data and waveband definitions
    stored in objects of classes defined in package 'photobiology'. Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
A 'ggplot2' extension that provides flipped components:
    horizontal versions of 'Stats' and 'Geoms', and vertical versions
    of 'Positions'. This package is now superseded by 'ggplot2' itself
    which now has full native support for horizontal layouts. It
    remains available for backward compatibility.
To create the 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.
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'.
Easy creation and manipulation of Voronoi diagrams using 'deldir' with visualization in 'ggplot2'. Convenient functions are provided to create nearest neighbor diagrams and heatmaps. Diagrams are computed with 'deldir' and processed to work with the 'sp' framework. Results are provided in a convenient spatial data structure and displayed with 'ggplot2'. An outline can be provided by the user to specify the spatial domain of interest.
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.
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>.
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.
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.
Optimal design analysis algorithms for any study design that can be represented or
  modelled as a generalised linear mixed model including cluster randomised trials,
  cohort studies, spatial and temporal epidemiological studies, and split-plot designs.
  See <https://github.com/samuel-watson/glmmrBase/blob/master/README.md> for a
  detailed manual on model specification. A detailed discussion of the methods in this
  package can be found in Watson and Pan (2022) <arXiv:2207.09183>.
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).
Simulation of correlation matrices possibly constrained by a given undirected or acyclic directed graph. In particular, the package provides functions that implement the simulation methods described in Córdoba et al. (2018) <doi:10.1007/978-3-030-03493-1_13> and Córdoba et al. (2020) <doi:10.1016/j.ijar.2020.07.007>.
Unsupervised Clustering and Meta-analysis using Gaussian Mixture
    Copula Models.
Functions and a graphical user interface for graphical described
    multiple test procedures.
A 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.
An implementation of an all-in-one function for a wide range of meta-analysis problems. It contains three functions. The gmeta() function unifies all standard meta-analysis methods and also several newly developed ones under a framework of combining confidence distributions (CDs). Specifically, the package can perform classical p-value combination methods (such as methods of Fisher, Stouffer, Tippett, etc.), fit meta-analysis fixed-effect and random-effects models, and synthesizes 2x2 tables. Furthermore, it can perform robust meta-analysis, which provides protection against model-misspecifications, and limits the impact of any unknown outlying studies. In addition, the package implements two exact meta-analysis methods from synthesizing 2x2 tables with rare events (e.g., zero total event). The np.gmeta() function summarizes information obtained from multiple studies and makes inference for study-level parameters with no distributional assumption. Specifically, it can construct confidence intervals for unknown, fixed study-level parameters via confidence distribution. Furthermore, it can perform  estimation via asymptotic confidence distribution whether tie or near tie condition exist or not. The plot.gmeta() function to visualize individual and combined CDs through extended forest plots is also available. Compared to version 2.2-6, version 2.3-0 contains a new function np.gmeta().
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.
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://projects.gnome.org/gnumeric/>) to convert
        the requested part to CSV. From 'gnumeric' files (but not other
        formats) can list sheet names and sheet sizes or read all
        sheets.
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.
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>.
A small package for calculating lots of different price indexes, and by extension quantity indexes. Provides tools to build and work with any type of bilateral generalized-mean index (of which most price indexes are), along with a few important indexes that don't belong to the generalized-mean family (e.g., superlative quadratic-mean indexes, GEKS). 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) for bilateral price indexes.
Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.
Convert the chip ID of GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> 
    to GeneBank Accession and ENTREZID <http://www.ncbi.nlm.nih.gov/gene>.
Implements the most common Gaussian process (GP) models using Laplace and
  expectation propagation (EP) approximations, maximum marginal likelihood
  (or posterior) inference for the hyperparameters, and sparse approximations
  for larger datasets.
Provides functions for estimating a generalized partial
	     linear model, a semiparametric variant of the generalized linear model
	     (GLM) which replaces the linear predictor by the sum of a linear
	     and a nonparametric function.
Various R programming tools for plotting data, including:
  - calculating and plotting locally smoothed summary function as
    ('bandplot', 'wapply'),
  - enhanced versions of standard plots ('barplot2', 'boxplot2',
    'heatmap.2', 'smartlegend'),
  - manipulating colors ('col2hex', 'colorpanel', 'redgreen',
    'greenred', 'bluered', 'redblue', 'rich.colors'),
  - calculating and plotting two-dimensional data summaries ('ci2d',
    'hist2d'),
  - enhanced regression diagnostic plots ('lmplot2', 'residplot'),
  - formula-enabled interface to 'stats::lowess' function ('lowess'),
  - displaying textual data in plots ('textplot', 'sinkplot'),
  - plotting a matrix where each cell contains a dot whose size
    reflects the relative magnitude of the elements ('balloonplot'),
  - plotting "Venn" diagrams ('venn'),
  - displaying Open-Office style plots ('ooplot'),
  - plotting multiple data on same region, with separate axes
    ('overplot'),
  - plotting means and confidence intervals ('plotCI', 'plotmeans'),
  - spacing points in an x-y plot so they don't overlap ('space').
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.
Motivation: GPU power is a great resource for computational biology specifically in statistics and linear algebra. Unfortunately, very few packages connect R with the GPU and none of them are transparent enough to perform the computations on the GPU without substantial changes to the code. Most of them lack proper maintenance: several of the previous attempts were removed from the corresponding repositories. It would be desirable to have an R package, properly maintained, that exploits the use of the GPU with minimal changes in the existing code. Results: We have developed the 'GPUMatrix' package. 'GPUMatrix' mimics the behavior of the Matrix package and extends R to use the GPU for computations. It is easy to learn and very few changes in the code are required to work on the GPU. 'GPUMatrix' relies on either 'Tensorflow' or 'Torch' R packages to perform the GPU operations. Its vignette shows some toy examples on non-negative factorization and other factorization used in 'bioinformatics'.
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>.
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 \italic{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.
Routines to produce and export publication-ready scientific plots and movies (e.g., used in Obreschkow et al. (2020) <doi:10.1093/mnras/staa445>). These include special color scales, projection routines, and bitmap handling routines.
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. 
    NOTICE  'gRbase' requires that the packages graph,
    'Rgraphviz' and 'RBGL' are installed from 'bioconductor'. For
    installation instructions please refer to the web page given below.
Estimation, model selection and other aspects of
    statistical inference in Graphical Gaussian models with edge and
    vertex symmetries (Graphical Gaussian models with colours).
    Documentation about 'gRc' is provided in the paper by Hojsgaard and
    Lauritzen (2007, <doi:10.18637/jss.v023.i06>) and the paper by
    Hojsgaard and Lauritzen (2008, <doi:10.1111/j.1467-9868.2008.00666.x>).
We implement two main functions.
    The first function uses a given grouped and/or
    right-censored grouping scheme and empirical data to infer parameters,
    and implements chi-square goodness-of-fit tests.
    The second function searches for the global optimal grouping
    scheme of grouped and/or right-censored count responses in surveys.
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>.
Allows the user to incorporate multiple sources of co-data 	
	(e.g., 	previously obtained p-values, published gene lists, and annotation) in the	estimation of a logistic regression model to enhance predictive performance and 	feature selection, as described in Münch, Peeters, van der Vaart, and van de Wiel 	(2018) <arXiv:1805.00389>.
Contains many functions useful for
    monitoring and reporting the results of clinical trials and other
    experiments in which treatments are compared.  LaTeX is
    used to typeset the resulting reports, recommended to be in the
    context of 'knitr'. The 'Hmisc', 'ggplot2', and 'lattice' packages are used
    by 'greport' for high-level graphics.
Write statistical models in R and fit them by MCMC and
    optimisation on CPUs and GPUs, using Google 'TensorFlow'.  greta lets
    you write your own model like in BUGS, JAGS and Stan, except that you
    write models right in R, it scales well to massive datasets, and it’s
    easy to extend and build on.  See the website for more information,
    including tutorials, examples, package documentation, and the greta
    forum.
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.
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>.
Group subset selection for linear regression models is provided in this package. Given response variable, and explanatory variables, which are organised in groups, group subset selection selects a small number of groups to explain response variable linearly using least squares.
Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.
Provides an expectation-maximization (EM) algorithm using the approach introduced in Xie (2001) <doi:10.1002/sim.817>. 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.
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). Regularization paths are computed using groupwise majorization descent. Cross-validation routines are available for tuning the regularization parameters.
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 predictions is implemented
  for incorporating all available observations at both GRS
  construction and GxE testing.
Functions to generate and analyze data for psychology experiments based on the General Recognition Theory.
Offers functions for the comparison of Gutenberg-Richter 
	b-values. Several functions in GRTo are helpful for the assessment of the
	quality of seismicity catalogs.
River hydrograph separation and daily runoff time series analysis. Provides
  various filters to separate baseflow and quickflow using methods by 
  Lyne and Hollick (1979) <https://www.researchgate.net/publication/272491803_Stochastic_Time-Variable_Rainfall-Runoff_Modeling>, 
  Chapman (1991) <doi:10.1029/91WR01007>, 
  Boughton (1993) <https://cir.nii.ac.jp/crid/1572543026556977024>, 
  Jakeman and Hornberger (1993) <doi:10.1029/93WR00877>, 
  Chapman and Maxwell (1996) <https://search.informit.org/doi/10.3316/informit.360361071346753>,
  and Kudelin (1960) <https://www.worldcat.org/title/printsipy-regionalnoi-otsenki-estestvennykh-resursov-podzemnykh-vod/>.
  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".
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.
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').
In hierarchical time series (HTS) forecasting, the hierarchical relation between multiple time series is exploited to make better forecasts. This hierarchical relation implies one or more aggregate consistency constraints that the series are known to satisfy. Many existing approaches, like for example bottom-up or top-down forecasting, therefore attempt to achieve this goal in a way that guarantees that the forecasts will also be aggregate consistent. This package provides with an implementation of the Game-Theoretically OPtimal (GTOP) reconciliation method proposed in van Erven and Cugliari (2015), which is guaranteed to only improve any given set of forecasts. This opens up new possibilities  for constructing the forecasts. For example, it is not necessary to assume that bottom-level forecasts are unbiased, and aggregate forecasts may be constructed by regressing both on bottom-level forecasts and on other covariates that may only be available at the aggregate level.
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.
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>.
Computes a elliptical regression model or a geographically weighted regression model with elliptical errors using Fisher's score algorithm. Provides diagnostic measures, residuals and analysis of variance. Cysneiros, F. J. A., Paula, G. A., and Galea, M. (2007) <doi:10.1016/j.spl.2007.01.012>.
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>) and a repeated holdout validation WQS (Tanner et al. (2019) <doi:10.1016/j.mex.2019.11.008>) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.
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.
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.
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>.
Process handwriting document into letters, words, and lines. Provides measurements at all levels.
             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>.
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.
This package calculates the interval estimates for the parameters of 
 linear models heteroscedastic regression using bootstrap - (Wild Bootstrap) and double
 bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using
 the percentile bootstrap and percentile bootstrap double. It is possible to calculate
 consistent estimates of the covariance matrix of the parameters of linear regression models
 with heteroskedasticity of unknown form. The package also provides function to calculate
 consistently the covariance matrix of the parameters of linear models with heteroskedasticity
 of unknown form.
Simulate and analyze hierarchical composite endpoints. Win odds is the main analysis method. See Gasparyan SB et al (2022) <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.
Hierarchical clustering for spatial data, which requires clustering 
    results not only homogeneous in non-geographical features among samples but 
    also geographically close to each other within a cluster. It modified typically 
    used hierarchical agglomerative clustering algorithms for introducing the spatial 
    homogeneity, by considering geographical locations as vertices and converting 
    spatial adjacency into whether a shared edge exists between a pair of vertices 
    (Tzeng & Hsu, 2022) <arXiv:2201.08302>. The constraints of the vertex links 
    automatically enforce the spatial contiguity property at each step of iterations. 
    In addition, methods to find an appropriate number of clusters and to report 
    cluster members are also provided.
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>.
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.
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>.
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>.
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, see the details in He et al. (2023)<arXiv:2303.02817>. In this package we also provide the code for traditional PCA by Bai, J., (2003)<doi:10.1111/1468-0262.00392>, the Robust Two Step (RTS) method by He et al. (2022)<doi:10.1080/07350015.2020.1811101>, the Quantile Factor Analysis (QFA) method by Chen et al. (2021)<doi:10.3982/ECTA15746>.
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., 2021), a novel approach 
             based on the penalized fusion technique is developed to fully 
             data-dependently determine the number and structure of subgroups in 
             Gaussian graphical model-based heterogeneity analysis. It opens the door for utilizing 
             the Gaussian graphical model technique in more practical settings. Beyond 
             Ren et al. (2021), more estimations and functions are added, so 
             that the package is self-contained and more comprehensive and can 
             provide "more direct" insights to practitioners (with the 
             visualization function). Reference: 
             Ren, M., Zhang S., Zhang Q. and Ma S. (2021). Gaussian Graphical 
             Model-based Heterogeneity Analysis via Penalized Fusion. 
             Biometrics, <doi:10.1111/biom.13426>.
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 paper 
    "Analyzing Intraday Financial Data in R: The highfrequency Package" 
    by Boudt, Kleen, and Sjoerup (2022, <doi:10.18637/jss.v104.i08>). 
    The DOI in the CITATION is for a new Journal of Statistical Software publication that will be registered after publication on CRAN.
    A working paper version can be found on SSRN: <doi:10.2139/ssrn.3917548>.
Syntax highlighter for R code based on the results of the R
    parser. Rendering in HTML and latex markup. Custom Sweave driver
    performing syntax highlighting of R code chunks.
A tool to format R markdown with CSS ids for HTML output. 
    The tool may be most helpful for those using markdown to create reproducible
    documents. The biggest limitations in formatting is the knowledge of CSS
    by the document authors.
Provides various tests for comparing high-dimensional mean vectors in two sample populations.
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,
    High Mach finds the best route between airports. Allows for coastal buffer and
    potentially closed regions. Uses 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>.
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, 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>).
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.
Using a simulation-based approach, the 'HostSwitch' package provides functions to investigate host switches by consumers.
    The individual-based model is based on the concept of ecological fitting.
    The mockup model is published by Araujo et al. 2015. Understanding Host-Switching by Ecological Fitting (<doi:10.1371/journal.pone.0139225>).
    The package provides an R-friendly and modified version of this model which can be applied to different consumer-resource scenarios.
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.
Provides bindings to the National Geodetic Survey (NGS) Horizontal
 Time Dependent Positioning (HTDP) utility, v3.2.5, written by Richard Snay,
 Chris Pearson, and Jarir Saleh of NGS. HTDP is a utility that allows users to
 transform positional coordinates across time and between spatial reference
 frames. See <https://www.ngs.noaa.gov/TOOLS/Htdp/Htdp.shtml> for more
 information.
A collection of reweighted marginal hypothesis tests for clustered data, based
    on reweighting methods of Williamson, J., Datta, S., and Satten, G. (2003) <doi:10.1111/1541-0420.00005>.
    The tests in this collection are clustered analogs to well-known hypothesis tests
    in the classical setting, and are appropriate for data with cluster- and/or group-size 
    informativeness. The syntax and output of functions are modeled after common, 
    recognizable functions native to R. Methods used in the package refer to 
    Gregg, M., Datta, S., and Lorenz, D. (2020) <doi:10.1177/0962280220928572>, 
    Nevalainen, J., Oja, H., and Datta, S. (2017) <doi:10.1002/sim.7288>
    Dutta, S. and Datta, S. (2015) <doi:10.1111/biom.12447>, 
    Lorenz, D., Datta, S., and Harkema, S. (2011) <doi:10.1002/sim.4368>, 
    Datta, S. and Satten, G. (2008) <doi:10.1111/j.1541-0420.2007.00923.x>, 
    Datta, S. and Satten, G. (2005) <doi:10.1198/016214504000001583>.
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.
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.
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>.
Provides tools to analyze and visualize the relationships between host-associated microbiomes of hybrid organisms and those of their progenitor species.
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.
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.
This version 2 of the HydroMe v.1 package estimates the parameters
  in infiltration and water retention models by curve-fitting methods <doi:10.1016/j.cageo.2008.08.011>. The
  models considered are those commonly used in soil science.  It has new models
  for water retention and characteristic curves.
State-of-the-art Multi-Objective Particle Swarm Optimiser (MOPSO), based on the algorithm developed by Lin et al. (2018) <doi:10.1109/TEVC.2016.2631279> with improvements described by Marinao-Rivas & Zambrano-Bigiarini (2020) <doi:10.1109/LA-CCI48322.2021.9769844>. This package is inspired by and closely follows the philosophy of the single objective 'hydroPSO' R package ((Zambrano-Bigiarini & Rojas, 2013) <doi:10.1016/j.envsoft.2013.01.004>), and can be used for global optimisation of non-smooth and non-linear R functions and R-base models (e.g., 'TUWmodel', 'GR4J', 'GR6J'). However, the main focus of 'hydroMOPSO' is optimising environmental and other real-world models that need to be run from the system console (e.g., 'SWAT+'). 'hydroMOPSO' communicates with the model to be optimised through its input and output files, without requiring modifying its source code. Thanks to its flexible design and the availability of several fine-tuning options, 'hydroMOPSO' can tackle a wide range of multi-objective optimisation problems (e.g., multi-objective functions, multiple model variables, multiple periods). Finally, 'hydroMOPSO' is designed to run on multi-core machines or network clusters, to alleviate the computational burden of complex models with long execution time.
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>.
State-of-the-art version of the Particle Swarm Optimisation (PSO) algorithm (SPSO-2011 and SPSO-2007 capable). hydroPSO can be used as a replacement of the 'optim' R function for (global) optimization of non-smooth and non-linear functions. However, the main focus of hydroPSO is the calibration of environmental and other real-world models that need to be executed from the system console. hydroPSO is model-independent, allowing the user to easily interface any computer simulation model with the calibration engine (PSO). hydroPSO  communicates with the model through the model's own input and output files, without requiring access to the model's source code. Several PSO variants and controlling options are included to fine-tune the performance of the calibration engine to different calibration problems. An advanced sensitivity analysis function together with user-friendly plotting summaries facilitate the interpretation and assessment of the calibration results. hydroPSO is parallel-capable, to alleviate the computational burden of complex models with "long" execution time. Bugs reports/comments/questions are very welcomed (in English, Spanish or Italian). See Zambrano-Bigiarini and Rojas (2013) <doi:10.1016/j.envsoft.2013.01.004> for more details.
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.
S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.
Focuses on data processing and visualization in hydrology and
    climate forecasting. Main function includes data extraction, data downscaling,
    data resampling, gap filler of precipitation, bias correction of forecasting
    data, flexible time series plot, and spatial map generation. It is a good pre-
    processing and post-processing tool for hydrological and hydraulic modellers.
Includes two main high level codes for hyperplane fitting (hyper.fit) and visualising (hyper.plot2d / hyper.plot3d). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.
A suite of routines for the hyperdirichlet distribution;
   supersedes the 'hyperdirichlet' package.  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.
Read and execute preprocessing procedures on hyper-spectral images. 
    These type of sensor data are usually recorded in a raw format. This package
    contains some easy-to-use functions to promptly build the image with
    some basic radiometric calibrations, setting up the geographic information. 
    Geometric correction can be done with band-to-band registration (translation 
    and rotation). Further functionalities allow to compute sliding windows
    statistics over the image.
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.
Create customized, web-based dashboards for data presentation, exploration and sharing. 'i2dash' integrates easily into existing data analysis pipelines and can organize scientific findings thematically across different pages and layouts.
Provides functions to work with 'incidence2' objects, including a
  simplified interface for trend fitting and peak estimation. This package is
  part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for 
  outbreak analysis (<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>.
Functions for Water and Steam Properties based on 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 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.
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>.
Set of formal methods for studying archaeological iconographic datasets (rock-art, pottery decoration, stelae, etc.) using network and spatial analysis (Alexander 2008 <doi:10.11588/propylaeumdok.00000512>; Huet 2018 <https://hal.archives-ouvertes.fr/hal-02913656>).
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.
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.
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 [Engineering Economics, Environmental/
    Water Resources Engineering, US Presidential Elections].
The eclectic collection includes the following written pieces:
    "The War Prayer" by Mark Twain, "War Is A Racket" by Major General
    Smedley Butler, "The Mask of Anarchy: Written on the Occasion of the
    Massacre at Manchester" by Percy Bysshe Shelley, "Connect the D.O.T.S." by
    Obiora Embry, "Untitled: Climate Strange" by Irucka Ajani Embry, and
    "Untitled: Us versus Them or People Screwing over Other People (as we all
    live on one Earth and there is no "us versus them" in the actual Ultimate
    Reality}" by Irucka Ajani Embry.
Computes characteristics of independent rainfall events (duration, total rainfall depth, and intensity) 
  extracted from a sub-daily rainfall time series based on the inter-event time definition (IETD) method. To have a 
  reference value of IETD, it also analyzes/computes IETD values through three methods: autocorrelation analysis, the 
  average annual number of events analysis, and coefficient of variation analysis. Ideal for analyzing the sensitivity 
  of IETD to characteristics of independent rainfall events.
  Adams B, Papa F (2000) <ISBN: 978-0-471-33217-6>.
  Joo J et al. (2014) <doi:10.3390/w6010045>.
  Restrepo-Posada P, Eagleson P (1982) <doi:10.1016/0022-1694(82)90136-6>.
Tools, tutorials, and demos of Item Factor Analysis using 'OpenMx'.
    This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.
Contains several tools to treat imaging flow cytometry data from 'ImageStream®' and 'FlowSight®' cytometers ('Amnis®', part of 'Luminex®'). Provides an easy and simple way to read 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.
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.
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.
Provides ability to create color palettes from image files. 
    It offers control over the type of color palette to derive from an image (qualitative, sequential or divergent) and other palette properties.
    Quantiles of an image color distribution can be trimmed. 
    Near-black or near-white colors can be trimmed in RGB color space independent of trimming brightness or saturation distributions in HSV color space.
    Creating sequential palettes also offers control over the order of HSV color dimensions to sort by.
    This package differs from other related packages like 'RImagePalette' in approaches to quantizing and extracting colors in images to assemble color palettes 
    and the level of user control over palettes construction.
Provides an interface for image recognition using the 'Google Vision API' <https://cloud.google.com/vision/> .  Converts API data for features such as object detection and optical character recognition to data frames. The package also includes functions for analyzing image annotations.
A complete API client for the image hosting service Imgur.com, including the an imgur graphics device, enabling the easy upload and sharing of plots.
Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
A multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. 'IMIX' can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. 'IMIX' uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. 'IMIX' features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) <doi:10.1093/bioinformatics/btaa1001>.
Interpretability methods to analyze the behavior and
    predictions of any machine learning model.  Implemented methods are:
    Feature importance described by Fisher et al. (2018)
    <arXiv:1801.01489>, accumulated local effects plots described by Apley
    (2018) <arXiv:1612.08468>, partial dependence plots described by
    Friedman (2001) <www.jstor.org/stable/2699986>, individual conditional
    expectation ('ice') plots described by Goldstein et al.  (2013)
    <doi:10.1080/10618600.2014.907095>, local models (variant of 'lime')
    described by Ribeiro et. al (2016) <arXiv:1602.04938>, the Shapley
    Value described by Strumbelj et. al (2014)
    <doi:10.1007/s10115-013-0679-x>, feature interactions described by
    Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.
Send emails using the 'mailgun' api. To use this package you will need an account from <https://www.mailgun.com> .
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.
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>).
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.
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.
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.
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>.
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.
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>.
This program analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment. Reference to software development can be found at <doi:10.1021/acs.jproteome.0c00872>.
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 
  <http://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>.
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'.
Analysis of the initialization for numerical optimization of 
    real-valued functions, including likelihood functions of statistical models.
    See <https://loelschlaeger.de/ino/> for more details.
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 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.
Calculate metrics of change intensity for category, transition and interval levels in categorical maps from sequential intervals. For more information please consult: Aldwaik,Safaa Zakaria and Robert Gilmore Pontius Jr. (2012). "Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition". Landscape and Urban Planning. 106, 103-114. <doi:10.1016/j.landurbplan.2012.02.010>.
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.
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.
Efficient computation of intrinsic and adaptive intrinsic factor 
    risk premia and their standard errors. Intrinsic factor risk premia are
    defined as the negative factor covariance with the SDF projection on 
    test asset returns. As opposed to benchmark notions of factor risk premia,
    they are well-defined even in presence of useless and weak factors, they do
    not depend on the degree of misspecification of the factor model, and are
    one-to-one linked to two-pass mimicking factor risk premia coefficients,
    whenever the latter are also well-defined.
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>.
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.
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 an R package on GitHub, but you can't 
    access GitHub, this function helps you download and install the package 
    from another website <https://hub.fastgit.xyz/>, 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 
    (Li, 2022 <http://www.riss.kr/link?id=T16374105>).
    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).
Functions for predicting and mapping potential and realized distributions of invasive species within the invaded range.
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>.
A series of functions that allow an easy and fast correction for drift and carry-over in continuous isotopic measurements. This implementation provides queries allowing users to perform the implemented corrections according to their needs. These functions further enable the processing of large datasets and can provides apt visualizations of the corrections performed.
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>).
Read and process isotopocule data from an Orbitrap Isotope Solutions mass spectrometer. Hilkert et al. (2021) <doi:10.1021/acs.analchem.1c00944>.
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 'raster' 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 standard errors, effective F-statistics, 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.
Compute the instrumental variables probit model using  the Amemiya's Generalized Least Squares estimators (Amemiya, Takeshi, (1978) <doi: 10.2307/1911443>).
Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.
Allows you to create an evidence factor (EX analysis) in an 
	instrumental variables regression model. Additionally, performs Sensitivity 
	analysis for OLS analysis, 2SLS analysis and EX analysis with interpretable 
	plotting and printing features.
Provides a library for generic interval manipulations using a
    new interval vector class. Capabilities include: locating various
    kinds of relationships between two interval vectors, merging overlaps
    within a single interval vector, splitting an interval vector on its
    overlapping endpoints, and applying set theoretical operations on
    interval vectors. Many of the operations in this package were inspired
    by James Allen's interval algebra, Allen (1983)
    <doi:10.1145/182.358434>.
An instrumental variable estimator under structural cumulative additive intensity model is fitted, that leverages initial randomization as the IV. The estimator can be used to fit an additive hazards model under time to event data which handles treatment switching (treatment crossover) correctly. We also provide a consistent variance estimate.
Contains tools for instrumental variables estimation. Currently, non-parametric bounds, two-stage estimation and G-estimation are implemented. Balke, A. and Pearl, J. (1997) <doi:10.2307/2965583>, Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) <doi:10.1214/11-STS360>.
Drawing statistical inference on the coefficients
    of a short- or long-horizon predictive regression with persistent
    regressors by using the IVX method of Magdalinos and Phillips (2009)
    <doi:10.1017/S0266466608090154> and Kostakis, Magdalinos and
    Stamatogiannis (2015) <doi:10.1093/rfs/hhu139>.
For a single variable, the IVY Plot stacks tied values in the form of leaflets. Five leaflets join to form a leaf. 
    Leaves are stacked vertically. At most twenty leaves are shown; For high frequency, each leaflet may represent more than 
    one observation with multiplicity declared in the subtitle.
Improved methods based on inverse probability weighting
        and outcome regression for causal inference and missing data
        problems.
Create and view tickets in 'gitea', a self-hosted git service <https://gitea.io>, using an 'RStudio' addin, and use helper functions to publish documentation and use git.
Code to specify, run, and then visualize and analyze the results of 
    Ixodidae (hard-bodied ticks) population and infection dynamics models. Such 
    models exist in the literature, but the source code to run them is not 
    always available. 'IxPopDyMod' provides an easy way for these models to be 
    written and shared.
Computes bootstrapped Monte Carlo estimate of p value of Kolmogorov-Smirnov (KS) test and 
    likelihood ratio test for zero-inflated count data, based on the work of Aldirawi et al. (2019) 
    <doi:10.1109/BHI.2019.8834661>. With the package, user can also find tools to simulate 
    random deviates from zero inflated or hurdle models and obtain  maximum likelihood estimate
    of unknown parameters in these models.
Call the data wrappers for Izmir Metropolitan Municipality's Open Data Portal. This will return all datasets formatted as Excel files (.csv or .xlsx), as well as datasets that require an API key.
An estimation method that can use computer simulations to
    approximate maximum-likelihood estimates even when the likelihood function can not
    be evaluated directly. It can be applied whenever it is feasible to conduct many
    simulations, but works best when the data is approximately Poisson distributed.
    It was originally designed for demographic inference in evolutionary
    biology (Naduvilezhath et al., 2011 <doi:10.1111/j.1365-294X.2011.05131.x>,
    Mathew et al., 2013 <doi:10.1002/ece3.722>).
    It has optional support for conducting coalescent simulation using
    the 'coala' package.
Provides data about the possible adverse events/reactions
    resulting from being injected with a vaccine/experimental gene therapy.
    Currently, this data set only includes information from six reference
    sources. Refer to the CITATION.cff file for the complete citations of
    the reference sources. For information about vaccination$/immunization$
    hazards, visit <https://www.questionuniverse.com/rethink.html#vaccine>,
    <https://www.ecoccs.com/healing.html#vaccines>,
    <https://www.questionuniverse.com/rethink_current_crisis.html#cov_vaccin>,
    and <https://www.questionuniverse.com/vaccination.html>.
Calculate statistical significance of Jaccard/Tanimoto similarity
    coefficients for binary data.
Symbolic calculation and evaluation of the Jack polynomials,
    zonal polynomials, and Schur polynomials. Mainly based on Demmel &
    Koev's paper (2006) <doi:10.1090/S0025-5718-05-01780-1>. Zonal
    polynomials and Schur polynomials are particular cases of Jack
    polynomials. Zonal polynomials appear in random matrix theory. Schur
    polynomials appear in the field of combinatorics.
Simply and efficiently
    simulates (i) variants from reference genomes and (ii) reads from both Illumina 
    <https://www.illumina.com/>
    and Pacific Biosciences (PacBio) <https://www.pacb.com/> platforms. 
    It can either read reference genomes from FASTA files or simulate new ones.
    Genomic variants can be simulated using summary statistics, phylogenies, 
    Variant Call Format (VCF) files, and coalescent simulations—the latter of which
    can include selection, recombination, and demographic fluctuations.
    'jackalope' can simulate single, paired-end, or mate-pair Illumina reads, 
    as well as PacBio reads.
    These simulations include sequencing errors, mapping qualities, multiplexing,
    and optical/polymerase chain reaction (PCR) duplicates.
    Simulating Illumina sequencing is based on ART
    by Huang et al. (2012) <doi:10.1093/bioinformatics/btr708>.
    PacBio sequencing simulation is based on 
    SimLoRD  by Stöcker et al. (2016) <doi:10.1093/bioinformatics/btw286>.
    All outputs can be written to standard file formats.
Computing the original and modified jackknife estimates of Kaplan-Meier estimators.
This function creates jackknife samples from the data by
    sequentially removing d observations from the data, performs
    estimation using the jackknife samples and calculates the jackknife
    coefficients, bias, standard error and confidence intervals based on
    the methodology discussed by Quenouille (1956) <doi:10.2307/2332914>,
    Tukey (1958) <doi:10.1214/aoms/1177706647> and Shi (1988)
    <doi:10.1016/0167-7152(88)90011-9>.
Provides method used to check whether data have outlier in efficiency measurement 
  of big samples with data envelopment analysis (DEA). In this jackstrap method, the package 
  provides two criteria to define outliers: heaviside and k-s test. The technique was developed 
  by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting 
  Nonparametric Frontier Measurements for Outliers." <doi:10.1007/s11123-005-4702-4>.
Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq.
Evaluation of the Jacobi theta functions and related
    functions: Weierstrass elliptic function, Weierstrass sigma function,
    Weierstrass zeta function, Klein j-function, Dedekind eta function,
    lambda modular function, Jacobi elliptic functions, Neville theta
    functions, Eisenstein series, lemniscate elliptic functions, and 
    Dixon elliptic functions. Complex values of the variable are
    supported.
Implements the classical Jacobi algorithm for the
    eigenvalues and eigenvectors of a real symmetric matrix, both in 
    pure 'R' and in 'C++' using 'Rcpp'. Mainly as a programming example 
    for teaching purposes.
Uses the Jaccard similarity index to account for population
    structure in sequencing studies. This method was specifically
    designed to detect population stratification based on rare variants, hence it
    will be especially useful in rare variant analysis.
Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.
Jade Lizard and Reverse Jade Lizard Option Strategies are presented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Russell A. Stultz (“The option strategy desk reference: an essential reference for option traders (First edition.)”, 2019, ISBN: 9781949443912).
All the data and functions used to produce the book. We do not expect
    most people to use the package for any other reason than to get simple access to the
    'JAGS' model files, the data, and perhaps run some of the simple examples.
    The authors of the book are David Lucy (now sadly deceased) and James Curran. It is 
    anticipated that a manuscript will be provided to Taylor and Francis around February 2020, with
    bibliographic details to follow at that point. Until such time, further information
    can be obtained by emailing James Curran.
Tools are provided to streamline Bayesian analyses in 'JAGS' using 
    the 'jagsUI' package.  Included are functions for extracting output in 
    simpler format, functions for streamlining assessment of convergence, and 
    functions for producing summary plots of output.  Also included is a 
    function that provides a simple template for running 'JAGS' from 'R'.
    Referenced materials can be found at <DOI:10.1214/ss/1177011136>.
Bayesian data analysis usually incurs long runtimes
  and cumbersome custom code.
  A pipeline toolkit tailored to Bayesian statisticians,
  the 'jagstargets' R package is leverages
  'targets' and 'R2jags' to ease this burden.
  'jagstargets' makes it super easy to set up scalable
  JAGS pipelines that automatically parallelize the computation
  and skip expensive steps when the results are already up to date.
  Minimal custom code is required, and there is no need to manually
  configure branching, so usage is much easier than 'targets' alone.
  For the underlying methodology, please refer
  to the documentation of 'targets' <doi:10.21105/joss.02959> and 'JAGS'
  (Plummer 2003) <https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf>.
A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags').  A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers.
Jalali, also known as Persian, Solar Hijri and Hijri Shamsi calendar is 
    the official calendar of Iran and Afghanistan. It begins on Nowruz, the March equinox, 
    as determined by astronomical calculation and has years of 365 or 366 days. 
    Adapting the algorithms in <https://jdf.scr.ir/>, this package provides tools 
    for converting the Jalali and Gregorian dates.
Provides an interface to 'Jamendo' API <https://developer.jamendo.com/v3.0>.
              Pull audio, features and other information for a given 
              'Jamendo' user (including yourself!) or enter an artist's -, album's -, 
              or track's name and retrieve the available information in seconds.
Full texts for Jane Austen's 6 completed novels, ready for
    text analysis. These novels are "Sense and Sensibility", "Pride and
    Prejudice", "Mansfield Park", "Emma", "Northanger Abbey", and
    "Persuasion".
The main janitor functions can: perfectly format data.frame column
    names; provide quick counts of variable combinations (i.e., frequency
    tables and crosstabs); and explore duplicate records. Other janitor functions
    nicely format the tabulation results. These tabulate-and-report functions
    approximate popular features of SPSS and Microsoft Excel. This package
    follows the principles of the "tidyverse" and works well with the pipe function
    %>%. janitor was built with beginning-to-intermediate R users in mind and is
    optimized for user-friendliness.
Proposes a coarse-to-fine optimization of a recommending system based on deep-neural networks using 'tensorflow'.
Provides tools for using the API of 'e-Stat' (<https://www.e-stat.go.jp/>), a portal site for Japanese government statistics.
    Includes functions for automatic query generation, data collection and formatting.
Provides a function collection to extract metadata, sectioned text and study characteristics from scientific articles in 'NISO-JATS' format. Articles in PDF format can be converted to 'NISO-JATS' with the 'Content ExtRactor and MINEr' ('CERMINE', <https://github.com/CeON/CERMINE>). For convenience, two functions bundle the extraction heuristics: JATSdecoder() converts 'NISO-JATS'-tagged XML files to a structured list with elements title, author, journal, history, 'DOI', abstract, sectioned text and reference list. study.character() extracts multiple study characteristics like number of included studies, statistical methods used, alpha error, power, statistical results, correction method for multiple testing, software used. An estimation of the involved sample size is performed based on reports within the abstract and the reported degrees of freedom within statistical results. In addition, the package contains some useful functions to process text (text2sentences(), text2num(), ngram(), strsplit2(), grep2()). See Böschen, I. (2021) <doi:10.1007/s11192-021-04162-z> Böschen, I. (2021) <doi:10.1038/s41598-021-98782-3> and Böschen, I (2023) <doi:10.1038/s41598-022-27085-y>.
Graphics device routing all graphics commands to a Java
	     program. The actual functionality of the JavaGD depends
	     on the Java-side implementation. Simple AWT and Swing
	     implementations are included.
Maximization or Minimization of a fitness function using Jaya Algorithm (JA).
   A population based method which repeatedly modifies a population of individual solutions.
   Capable of solving both constrained and unconstrained optimization problems.
   It does not contain any hyperparameters. 
   For further details: R.V. Rao (2016) <doi:10.5267/j.ijiec.2015.8.004> .
Provides an R interface to the JBrowse 2 genome browser.
    Enables embedding a JB2 genome browser in a Shiny app or R Markdown
    document. The browser can also be launched from an interactive R console.
    The browser can be loaded with a variety of common genomics data types,
    and can be used with a custom theme.
Provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather 
             types during the period considered for the analysis. See Otero et al., (2017) <doi:10.1007/s00382-017-3705-y> for more information.
Procedures for joint detection of changes in both expectation and variance in univariate sequences. Performs a statistical test of the null hypothesis of the absence of change points. In case of rejection performs an algorithm for change point detection. Reference - Bivariate change point detection - joint detection of changes in expectation and variance, Scandinavian Journal of Statistics, DOI 10.1111/sjos.12547.
The Impact Factor of a journal reported by Journal Citation Reports ('JCR') of  'Clarivate' 'Analytics' is provided. The impact factor is available for those journals only that were included Journal Citation Reports 'JCR'.
A Wrapper for the Node.js 'Jdenticon' <https://jdenticon.com/> Library. Uses 'esbuild' <https://esbuild.github.io/> to reduce user dependencies.
Option is a one of the financial derivatives and its pricing is an important problem in practice. The process of stock prices are represented as Geometric Brownian motion [Black (1973) <doi:10.1086/260062>] or jump diffusion processes [Kou (2002) <doi:10.1287/mnsc.48.8.1086.166>]. In this package, algorithms and visualizations are implemented by Monte Carlo method in order to calculate European option price for three equations by Geometric Brownian motion and jump diffusion processes and furthermore a model that presents jumps among companies affect each other.
Customized R Markdown templates for authoring articles
    for Journal of Data Science.
Provides a fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian Graphical Models (JEEK). The JEEK algorithm can be used to fast estimate multiple related precision matrices in a large-scale. For instance, it can identify multiple gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogeneous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(jeek) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models" (ICML 2018) <arXiv:1806.00548>.
Fast extrapolation of univariate and multivariate time features using K-Nearest Neighbors. The compact set of hyper-parameters is tuned via grid or random search.
Manage project dependencies from your DESCRIPTION file. Create a reproducible virtual environment with minimal additional files in your project. Provides tools to add, remove, and update dependencies as well as install existing dependencies with a single function.
On Affymetrix gene expression microarrays, a single gene may be measured by multiple probe sets. This can present a mild conundrum when attempting to evaluate a gene "signature" that is defined by gene names rather than by specific probe sets. This package provides a one-to-one mapping from gene to "best" probe set for four Affymetrix human gene expression microarrays: hgu95av2, hgu133a, hgu133plus2, and u133x3p. This package also includes the pre-calculated probe set quality scores that were used to define the mapping.
Estimates networks of conditional dependencies (Gaussian graphical models) from multiple classes of data (similar but not exactly, i.e. measurements on different equipment, in different locations or for various sub-types). Package also allows to generate simulation data and evaluate the performance. Implementation of the method described in Angelini, De Canditiis and Plaksienko (2022) <doi:10.3390/math10213983>.
Provides statistical methods for auditing as implemented in JASP
    for Audit (Derks et al., 2021 <doi:10.21105/joss.02733>). First, the package
    makes it easy for an auditor to plan a statistical sample, select the sample
    from the population, and evaluate the misstatement in the sample compliant
    with international auditing standards. Next to classical audit sampling
    methodology, the package implements Bayesian equivalents of these methods
    whose statistical underpinnings are described in Derks et al. (2021)
    <doi:10.1111/ijau.12240>, Derks et al. (2021) <doi:10.31234/osf.io/kzqp5>,
    and Derks et al. (2022) <doi:10.31234/osf.io/8nf3e>. Second, the package
    provides statistical methods for auditing data, including (Bayesian) tests
    of digit distributions and tests for repeated values.
Offers a menu-driven GUI to support financial and economic time series modelling and enhanced procedures for computing the investment performance indices of Bacon (2004) <DOI:10.1002/9781119206309>.
Provides functions to extract joint planes from 3D triangular mesh derived
             from point cloud and makes data available for structural analysis.
Color palettes used by the Pacific Northwest National Laboratory - Joint Global Change Research Institute for maps and charts.
The Joint Graphical Lasso is a generalized method for
        estimating Gaussian graphical models/ sparse inverse covariance
        matrices/ biological networks on multiple classes of data.  We
        solve JGL under two penalty functions: The Fused Graphical
        Lasso (FGL), which employs a fused penalty to encourage inverse
        covariance matrices to be similar across classes, and the Group
        Graphical Lasso (GGL), which encourages similar network
        structure between classes.  FGL is recommended over GGL for
        most applications. Reference: Danaher P, Wang P, Witten DM. (2013) 
        <doi:10.1111/rssb.12033>.
Java GUI for R - cross-platform, universal and unified Graphical User Interface for R. For full functionality on Windows and Mac OS X JGR requires a start application which depends on your OS.
All datasets and functions used in the german book "Statistik mit R und RStudio" by grosse Schlarmann (2022) <https://www.produnis.de/R/>.
Implements the JICO algorithm [Wang, P., Wang, H., Li, Q., Shen, D., & Liu, Y. (2022). <arXiv:2209.12388>], which solves the multi-group regression problem. The algorithm decomposes the responses from multiple groups into shared and group-specific
        components, which are driven by low-rank approximations of joint and individual structures from the covariates respectively. It provides the implementation of
        the algorithm so solve the iterative continuum regression problem with fixed rank selection, as well as the cross-validation function to perform hyperparameter tuning.
Chinese text segmentation, keyword extraction and speech tagging
    For R.
jiebaR is a package for Chinese text segmentation, keyword extraction
   and speech tagging. This package provides the data files required by jiebaR.
Template engine powered by the 'inja' C++ library. Users
    write a template document, using syntax inspired by the 'Jinja' Python
    package, and then render the final document by passing data from R.
    The template syntax supports features such as variables, loops,
    conditions and inheritance.
Approximate joint-inclusion probabilities in Unequal Probability Sampling, or compute Monte Carlo approximations of the first and second-order inclusion probabilities of a general sampling design as in Fattorini (2006) <doi:10.1093/biomet/93.2.269>.
Set of common functions used for manipulating colors,
    detecting and interacting with 'RStudio', modeling, formatting, determining
    users' operating system, feature scaling, and more!
Implements the tree-based approach to joint modeling of time-to-event and longitudinal data. This approach looks for a tree-based partitioning such that within each estimated latent class defined by a terminal node, the time-to-event and longitudinal responses display a lack of association. See Zhang and Simonoff (2018) <arXiv:1812.01774>.
An implementation of fast cluster-based permutation analysis
    (CPA) for densely-sampled time data developed in Maris & Oostenveld,
    2007 <doi:10.1016/j.jneumeth.2007.03.024>. Supports (generalized,
    mixed-effects) regression models for the calculation of timewise
    statistics. Provides both a wholesale and a piecemeal interface to the
    CPA procedure with an emphasis on interpretability and diagnostics.
    Integrates 'Julia' libraries 'MixedModels.jl' and 'GLM.jl' for
    performance improvements, with additional functionalities for
    interfacing with 'Julia' from 'R' powered by the 'JuliaConnectoR'
    package.
Estimation of extended joint models with shared random effects. Longitudinal data are handled in latent process models for continuous (Gaussian or curvilinear) and ordinal outcomes while proportional hazard models are used for the survival part. We propose a frequentist approach using maximum likelihood estimation. See Saulnier et al, 2021 <arXiv:2110.02612>.
Shared parameter models for the joint modeling of longitudinal and time-to-event data.
A mainly instrumental package meant to allow other packages whose core is written in 'C++' to read, write
        and manipulate matrices in a binary format so that the memory used for them is no more than strictly needed. Its functionality
        is already inside 'parallelpam' and 'scellpam', so if you have installed any of these, you do not need to install 'jmatrix'.
        Using just the needed memory is not always true with 'R' matrices or vectors, since by default they are of double type. Trials
        like the 'float' package have been done, but to use them you have to coerce a matrix already loaded in 'R' memory to a float matrix,
        and then you can delete it. The problem comes when your computer has not memory enough to hold the matrix in the first place, so
        you are forced to load it by chunks. This is the problem this package tries to address (with partial success, but this is a
        difficult problem since 'R' is not a strictly typed language, which is anyway quite hard to get in an interpreted language).
	This package allows the creation and manipulation of full, sparse and symmetric matrices of any standard data type.
Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.
Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).
Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models,
             which allow for the simultaneous analysis of longitudinal and time-to-event data.
             This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a 
             longitudinal biomarker and a clinical outcome, such as survival or disease progression.
             This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time.
             Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes 
             with both Bayesian and non-Bayesian approaches.
             Its versatility and flexibility make it a valuable resource for researchers in many different fields,particularly in the medical and health sciences.
Fit joint mean-covariance models for longitudinal data. The models
    and their components are represented using S4 classes and methods. The core
    computational algorithms are implemented using the 'Armadillo' C++ library
    for numerical linear algebra and 'RcppArmadillo' glue.
Joint mean and dispersion effects models fit the mean and dispersion parameters of a response variable by two separate linear models, the mean and dispersion submodels, simultaneously. It also allows the users to choose either the deviance or the Pearson residuals as the response variable of the dispersion submodel. Furthermore, the package provides the possibility to nest the submodels in one another, if one of the parameters has significant explanatory power on the other. Wu & Li (2016) <doi:10.1016/j.csda.2016.04.015>.
Performs power calculations for joint modeling of longitudinal 
    and survival data with k-th order trajectories when the variance-covariance
    matrix, Sigma_theta, is unknown.
The main purpose of this package is to make it easy for userR's to interact with 'jMetrik' an open source application for psychometric analysis. For example it allows useR's to write data frames to file in a format that can be used by 'jMetrik'. It also allows useR's to read *.jmetrik files (e.g. output from an analysis) for follow-up analysis in R. The *.jmetrik format is a flat file that includes a multiline header and the data as comma separated values. The header includes metadata about the file and one row per variable with the following information in each row: variable name, data type, item scoring, special data codes, and variable label.
Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data in the presence of heterogeneous within-subject variability, proposed by Li and colleagues (2023) <arXiv:2301.06584>.
             The proposed method models the within-subject variability of the biomarker and associates it with the risk of the competing risks event. The time-to-event data is modeled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. 
             The longitudinal outcome is modeled using a mixed-effects location and scale model. The association is captured by shared random effects. The model 
             is estimated using an Expectation Maximization algorithm.
Computes the Jackknife Mutual Information (JMI) between two random vectors and provides the p-value for dependence tests. See Zeng, X., Xia, Y. and Tong, H. (2018) <doi:10.1073/pnas.1715593115>.
Implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA
    algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly),
    and characteristic pattern discovery along with interpretable time series classification.
Performs a permutation test on the difference between two location parameters, a permutation correlation test, a permutation F-test, the Siegel-Tukey test, a ratio mean deviance test.  Also performs some graphing techniques, such as for confidence intervals, vector addition, and Fourier analysis; and includes functions related to the Laplace (double exponential) and triangular distributions.  Performs power calculations for the binomial test.
A suite of common statistical methods such as descriptives,
    t-tests, ANOVAs, regression, correlation matrices, proportion tests,
    contingency tables, and factor analysis. This package is also useable from
    the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more
    information).
Methods to access data sets from the 'jamovi' statistical
  spreadsheet (see <https://www.jamovi.org> for more information) from R.
A framework for creating rich interactive analyses for the jamovi
    platform (see <https://www.jamovi.org> for more information).
The free and open a statistical spreadsheet 'jamovi'
    (<https://www.jamovi.org>) aims to make statistical analyses easy and
    intuitive. 'jamovi' produces syntax that can directly be used in R (in
    connection with the R-package 'jmv'). Having import / export routines for
    the data files 'jamovi' produces ('.omv') permits an easy transfer of
    data and analyses between 'jamovi' and R.
Call job::job({<code here>}) to run R code as an RStudio job and keep your console free in the meantime. This allows for a productive workflow while testing (multiple) long-running chunks of code. It can also be used to organize results using the RStudio Jobs GUI or to test code in a clean environment. Two RStudio Addins can be used to run selected code as a job.
Computing and plotting joint confidence regions and intervals. Regions include classical ellipsoids, minimum-volume or minimum-length regions, and an empirical Bayes region. Intervals include the TOST procedure with ordinary or expanded intervals and a fixed-sequence procedure. Such regions and intervals are useful e.g., for the assessment of multi-parameter (bio-)equivalence. Joint confidence regions for the mean and variance of a normal distribution are available as well.
In a typical experiment for the intuitive judgment of frequencies (JoF) different stimuli with different frequencies are presented. The participants consider these stimuli with a constant duration and give a judgment of frequency. These judgments can be simulated by formal models: PASS 1 and PASS 2 based on Sedlmeier (2002, ISBN:978-0198508632), MINERVA 2 baesd on Hintzman (1984) <doi:10.3758/BF03202365> and TODAM 2 based on Murdock, Smith & Bai (2001) <doi:10.1006/jmps.2000.1339>. The package provides an assessment of the frequency by determining the core aspects of these four models (attention, decay, and presented frequency) that can be compared to empirical results.
Analysis of repeated measurements and time-to-event data via random
    effects joint models. Fits the joint models proposed by Henderson and colleagues
    <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and
    colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a
    single continuous repeated measure. The time-to-event data is modelled using a 
    (cause-specific) Cox proportional hazards regression model with time-varying 
    covariates. The longitudinal outcome is modelled using a linear mixed effects
    model. The association is captured by a latent Gaussian process. The model is 
    estimated using am Expectation Maximization algorithm. Some plotting functions 
    and the variogram are also included. This project is funded by the Medical 
    Research Council (Grant numbers G0400615 and MR/M013227/1).
Fits the joint model proposed by Henderson and colleagues (2000) 
    <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple 
    continuous longitudinal measures. The time-to-event data is modelled using a 
    Cox proportional hazards regression model with time-varying covariates. The 
    multiple longitudinal outcomes are modelled using a multivariate version of the 
    Laird and Ware linear mixed model. The association is captured by a multivariate
    latent Gaussian process. The model is estimated using a Monte Carlo Expectation 
    Maximization algorithm. This project was funded by the Medical Research Council 
    (Grant number MR/M013227/1).
Implements high-dimensional multivariate regression by stacked generalisation (Rauschenberger 2021 <doi:10.1093/bioinformatics/btab576>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. If required, install MRCE or remMap from GitHub (<https://github.com/cran/MRCE>, <https://github.com/cran/remMap>).
Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death.
 Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model.
 The methods are applicable for meta-analytic data containing individual-patient information from several studies.
 Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression).
 Methodologies were published in
 Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>,
 Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>,
 Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>.
 See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>.
 Survival data from ovarian cancer patients are also available.
Joint analysis and imputation of incomplete data in the Bayesian
    framework, using (generalized) linear (mixed) models and extensions there of,
    survival models, or joint models for longitudinal and survival data, as
    described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>.
    Incomplete covariates, if present, are automatically imputed.
    The package performs some preprocessing of the data and creates a 'JAGS'
    model, which will then automatically be passed to 'JAGS' 
    <https://mcmc-jags.sourceforge.io/> with the help of 
    the package 'rjags'.
Different algorithms to perform approximate joint diagonalization
	of a finite set of square matrices. Depending on the algorithm,
	orthogonal or non-orthogonal diagonalizer is found. These algorithms
	are particularly useful in the context of blind source separation. 
	Original publications of the algorithms can be found in 
	Ziehe et al. (2004), Pham and Cardoso (2001) <doi:10.1109/78.942614>, 
	Souloumiac (2009) <doi:10.1109/TSP.2009.2016997>, 
	Vollgraff and Obermayer <doi:10.1109/TSP.2006.877673>. An example of 
	application in the context of Brain-Computer Interfaces EEG denoising
	can be found in Gouy-Pailler et al (2010) <doi:10.1109/TBME.2009.2032162>.
Jointly estimates two-group means and covariances
    for matrix-variate data and calculates test statistics.  
    This package implements the algorithms defined in 
    Hornstein, Fan, Shedden, and Zhou (2018) 
    <doi:10.1080/01621459.2018.1429275>.
Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.
Estimate risk caused by two extreme and dependent forcing variables using bivariate extreme value models as described in Zheng, Westra, and Sisson (2013) <doi:10.1016/j.jhydrol.2013.09.054>; Zheng, Westra and Leonard (2014) <doi:10.1002/2013WR014616>; Zheng, Leonard and Westra (2015) <doi:10.2166/hydro.2015.052>.
Methods for fast segmentation of multivariate
    signals into piecewise constant profiles and for generating realistic
    copy-number profiles. A typical application is the joint segmentation of total
    DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism
    (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, 
    Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.
In the observational study design stage, matching/weighting methods are
    conducted. However, when many background variables are present, the decision as to 
    which variables to prioritize for matching/weighting is not trivial. Thus, the 
    joint treatment-outcome variable importance plots are created to guide variable
    selection. The joint variable importance plots enhance variable comparisons via
    unadjusted bias curves derived under the omitted variable bias framework. The 
    plots translate variable importance into recommended values for tuning parameters 
    in existing methods. Post-matching and/or weighting plots can also be used to 
    visualize and assess the quality of the observational study design. The method 
    motivation and derivation is presented in "Using Joint Variable Importance Plots 
    to Prioritize Variables in Assessing the Impact of Glyburide on Adverse Birth 
    Outcomes" by Liao et al. (2023) <arXiv:2301.09754>. See the package paper by Liao 
    and Pimentel (2023) <arxiv:2302.10367> for a beginner friendly user introduction.
Performs Joins and Minus Queries on 'Excel' Files
    fulljoinXL() Merges all rows of 2 'Excel' files based upon a common column in the files.
    innerjoinXL() Merges all rows from base file and join file when the join condition is met.
    leftjoinXL() Merges all rows from the base file, and all rows from the join file
    if the join condition is met.
    rightjoinXL() Merges all rows from the join file, and all rows from the base file if the join
    condition is met.
    minusXL() Performs 2 operations source-minus-target and target-minus-source
    If the files are identical all output files will be empty.
    Choose two 'Excel' files via a dialog box, and then follow prompts at the console to
    choose a base or source file and columns to merge or minus on.
Similarly to package 'pan', 'jomo' is a package for multilevel joint modelling multiple imputation (Carpenter and Kenward, 2013) <doi:10.1002/9781119942283>.
 Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.
Functions and data to reproduce all plots in the book "Practical Smoothing. 
 The Joys of P-splines" by Paul H.C. Eilers and Brian D. Marx (2021, ISBN:978-1108482950).
A Jordan algebra is an algebraic object originally
   designed to study observables in quantum mechanics.  Jordan
   algebras are commutative but non-associative; they satisfy the
   Jordan identity.  The package follows the ideas and notation of
   K. McCrimmon (2004, ISBN:0-387-95447-3) "A Taste of Jordan
   Algebras".
Implementation of some unit and area level EBLUP estimators as well as the estimators of their MSE also under heteroscedasticity. The package further documents the publications Breidenbach and Astrup (2012) <DOI:10.1007/s10342-012-0596-7>, Breidenbach et al. (2016) <DOI:10.1016/j.rse.2015.07.026> and Breidenbach et al. (2018 in press). The vignette further explains the use of the implemented functions.
Josa in Korean is often determined by judging the previous word. 
            When writing reports using Rmd, a function that prints the 
            appropriate investigation for each case is helpful. 
            The 'josaplay' package then evaluates the previous word 
            to determine which josa is appropriate.
Read and write JSON Web Keys (JWK, rfc7517), generate and verify JSON
    Web Signatures (JWS, rfc7515) and encode/decode JSON Web Tokens (JWT, rfc7519).
    These standards provide modern signing and encryption formats that are natively
    supported by browsers via the JavaScript WebCryptoAPI, and used by services 
    like OAuth 2.0, LetsEncrypt, and Github Apps.
Reproducible work requires a record of where every statistic originated.
    When writing reports, some data is too big to load in the same environment and some statistics take a while to compute.
    This package offers a way to keep notes on statistics, simple functions, and small objects.
    Notepads can be locked to avoid accidental updates.
    Notepads keep track of who added the notes and when the notes were added.
    A simple text representation is used to allow for clear version histories.
Since the reference management software (such as 'Zotero', 'Mendeley') exports Bib file journal abbreviation is not detailed enough, the 'journalabbr' package only abbreviates the journal field of Bib file, and then outputs a new Bib file for generating reference format with journal abbreviation on other software (such as 'texstudio'). The abbreviation table is from 'JabRef'. At the same time, 'Shiny' application is provided to generate 'thebibliography', a reference format that can be directly used for latex paper writing based on 'Rmd' files.
Implements under/oversampling for probability estimation.  To be
    used with machine learning methods such as AdaBoost, random forests, etc.
Tool for diagnosing table joins. It combines the speed
    `data.table`, the flexibility of `dplyr`, and the diagnosis and features of
    the `merge` command in `Stata`.
Read Japanese city codes (<https://www.e-stat.go.jp/municipalities/cities>) 
    to get city and prefecture names, or convert to city codes at different 
    points in time. In addition, it merges or splits wards of designated cities
    and gets all city codes at a specific point in time.
This package provides an easy and simple way to read, write and display bitmap images stored in the JPEG format. It can read and write both files and in-memory raw vectors.
A Joint PENalty Estimation of Covariance and Inverse Covariance Matrices.
Provides functions for grid square codes in Japan
    (<https://www.stat.go.jp/english/data/mesh/index.html>).
    Generates the grid square codes from longitude/latitude, geometries, and 
    the grid square codes of different scales, and vice versa.
Helpful functions for using mesh code (80km to 100m) data in Japan. Visualize mesh code using 'ggplot2' and 'leaflet', etc.
Provides tools to use API such as 'e-Stat' (<https://www.e-stat.go.jp/>), 
    the portal site for Japanese government statistics, and 
    'RESAS' (Regional Economy and Society Analyzing System, <https://resas.go.jp>).
A highly configurable 'jQuery' plugin offering a simple
    interface to create complex queries/filters in 'Shiny'. The outputted
    rules can easily be parsed into a set of 'R' and/or 'SQL' queries and
    used to filter data. Custom parsing of the rules is also supported.
    For more information about 'jQuery QueryBuilder' see
    <https://querybuilder.js.org/>.
We provide tools to estimate the individualized interval-valued dose rule (I2DR) that maximizes the expected beneficial clinical outcome for each individual and returns an optimal interval-valued dose, by using the jump Q-learning (JQL) method. The jump Q-learning method directly models the conditional mean of the response given the dose level and the baseline covariates via jump penalized least squares regression under the framework of Q learning. We develop a searching algorithm by dynamic programming in order to find the optimal I2DR with the time complexity O(n2) and spatial complexity O(n). To alleviate the effects of misspecification of the Q-function, a residual jump Q-learning is further proposed to estimate the optimal I2DR. The outcome of interest includes the best partition of the entire dosage of interest, the regression coefficients of each partition, and the value function under the estimated I2DR as well as the Wald-type confidence interval of value function constructed through the Bootstrap.
Client for 'jq', a 'JSON' processor (<https://stedolan.github.io/jq/>), 
    written in C. 'jq' allows the following with 'JSON' data: index into, parse, 
    do calculations, cut up and filter, change key names and values, perform 
    conditionals and comparisons, and more.
Obtain any major version of 'jQuery' (<https://code.jquery.com/>) and use it in any webpage generated by 'htmltools' (e.g. 'shiny', 'htmlwidgets', and 'rmarkdown').
    Most R users don't need to use this package directly, but other R packages (e.g. 'shiny', 'rmarkdown', etc.) depend on this package to avoid bundling redundant copies of 'jQuery'.
An 'httpuv' based bridge between R and 'JavaScript'. Provides an easy way to exchange commands and data between a web page and a currently running R session.
These functions calculate the taxonomic measures presented in Miranda-Esquivel (2016). 
   The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, 
   as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area 
   in a conservation analysis.
   The algorithm is described in: Miranda-Esquivel, D (2016) <DOI:10.1007/978-3-319-22461-9_11>.
Methods to perform Joint graph Regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization ('jrSiCKLSNMF', pronounced "junior sickles NMF") on quality controlled single-cell multimodal omics count data. 'jrSiCKLSNMF' specifically deals with dual-assay scRNA-seq and scATAC-seq data. This package contains functions to extract meaningful latent factors that are shared across omics modalities. These factors enable accurate cell-type clustering and facilitate visualizations. Methods for pre-processing, clustering, and mini-batch updates and other adaptations for larger datasets are also included. For further details on the methods used in this package please see Ellis, Roy, and Datta (2023) <doi:10.3389/fgene.2023.1179439>.
Psychometric analysis and scoring of judgment data using polytomous Item-Response Theory (IRT) models, as described in Myszkowski and Storme (2019) <doi:10.1037/aca0000225> and Myszkowski (2021) <doi:10.1037/aca0000287>. A function is used to automatically compare and select models, as well as to present a variety of model-based statistics. Plotting functions are used to present category curves, as well as information, reliability and standard error functions.
Implements the basic financial analysis
    functions similar to (but not identical to) what
    is available in most spreadsheet software. This
    includes finding the IRR and NPV of regularly
    spaced cash flows and annuities. Bond pricing and
    YTM calculations are included. In addition, Black
    Scholes option pricing and Greeks are also
    provided.
A set of utilities for working with JavaScript syntax in R.
    Includes tools to parse, tokenize, compile, validate, reformat, optimize 
    and analyze JavaScript code.
Provides a 'RStudio' addin to send some 'JavaScript' code to the 'V8' console. The user can send an entire 'JavaScript' file or only some selected lines. This is useful to test the code.
The jscore() function in the package calculates the J-Score metric between two clustering
    assignments. The score is designed to address some problems with existing common metrics such 
    as problem of matching. The details of J-score is described in Ahmadinejad and Liu. (2021) <arXiv:2109.01306>.
Fits joint species distribution models ('jSDM')
    in a hierarchical Bayesian framework (Warton and al. 2015
    <doi:10.1016/j.tree.2015.09.007>). The Gibbs sampler is written
    in 'C++'. It uses 'Rcpp', 'Armadillo' and 'GSL' to maximize computation
    efficiency.
Allow to run 'jshint' on 'JavaScript' files with a 'R'
    command or a 'RStudio' addin. The report appears in the 'RStudio'
    viewer pane.
The function 'jskm()' creates publication quality Kaplan-Meier plot with at risk tables below. 'svyjskm()' provides plot for weighted Kaplan-Meier estimator.
Maximum likelihood estimation for the semiparametric joint modeling of survival and longitudinal data. Refer to the Journal of Statistical Software article: <doi:10.18637/jss.v093.i02>.
A set of helper functions to conduct joint-significance tests
    for mediation analysis, as recommended by Yzerbyt, Muller, Batailler,
    & Judd. (2018) <doi:10.1037/pspa0000132>.
'RStudio' addins and 'Shiny' modules for descriptive statistics, regression and survival analysis.
The function get_parameters() is intended to be used within a docker container to read keyword arguments from a .json file automagically. A tool.yaml file contains specifications on these keyword arguments, which are then passed as input to containerized R tools in the [tool-runner framework](<https://github.com/hydrocode-de/tool-runner>). A template for a containerized R tool, which can be used as a basis for developing new tools, is available at the following URL: <https://github.com/VForWaTer/tool_template_r>.
Encode/Decode 'base64', with support for JSON format, using two functions: j_encode() and j_decode().    'Base64' is a group of similar binary-to-text encoding schemes that represent binary data in an ASCII string format by translating it into a radix-64 representation, used when there is a need to encode binary data that needs to be stored and transferred over media that are designed to deal with textual data, ensuring that the data will remain intact and without modification during transport. <https://developer.mozilla.org/en-US/docs/Web/API/WindowBase64/Base64_encoding_and_decoding>    On the other side, JSON (JavaScript Object Notation) is a lightweight data-interchange format. Easy to read, write, parse and generate. It is based on a subset of the JavaScript Programming Language. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. JSON structure is built around name:value pairs and ordered list of values. <https://www.json.org>    The first function, j_encode(), let you transform a data.frame or list to a 'base64' encoded JSON (or JSON string).    The j_decode() function takes a 'base64' string (could be an encoded JSON) and transform it to a data.frame (or list, depending of the JSON structure).
Conversions between 'R' objects and Javascript Object Notation (JSON) using 
    the 'rapidjsonr' library <https://CRAN.R-project.org/package=rapidjsonr>.
JSON-LD is a light-weight syntax for expressing linked data. It is primarily
    intended for web-based programming environments, interoperable web services and for 
    storing linked data in JSON-based databases. This package provides bindings to the 
    JavaScript library for converting, expanding and compacting JSON-LD documents.
A reasonably fast JSON parser and generator, optimized for statistical 
    data and the web. Offers simple, flexible tools for working with JSON in R, and
    is particularly powerful for building pipelines and interacting with a web API. 
    The implementation is based on the mapping described in the vignette (Ooms, 2014).
    In addition to converting JSON data from/to R objects, 'jsonlite' contains 
    functions to stream, validate, and prettify JSON data. The unit tests included 
    with the package verify that all edge cases are encoded and decoded consistently 
    for use with dynamic data in systems and applications.
Provides a function allowing to normalize a 'JSON' string,
    for example by adding double quotes around the keys when they are
    missing. Also provides 'RStudio' addins for the same purpose.
Interface to 'JSON-stat' <https://json-stat.org/>,
  a simple lightweight 'JSON' format for data dissemination.
Fast manipulation of JSON strings. Allows to extract or
    delete an element in a JSON string, merge two JSON strings, and more.
Uses the node library 'is-my-json-valid' or 'ajv' to
    validate 'JSON' against a 'JSON' schema.  Drafts 04, 06 and 07 of
    'JSON' schema are supported.
Implementation of joint sparse optimization (JSparO) to infer the gene regulatory network for cell fate conversion. The proximal gradient method is implemented to solve different low-order regularization models for JSparO.
Create regression tables from generalized linear model(GLM), generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.
Functions and helpers to import metadata, ngrams and full-texts 
    delivered by Data for Research by JSTOR.
Create and customize interactive trees using the
       'jQuery' 'jsTree' <https://www.jstree.com/> plugin
       library and the 'htmlwidgets' package. These trees can
       be used directly from the R console, from 'RStudio', in
       Shiny apps and R Markdown documents.
Creates interactive trees that can be included in 'Shiny'
    apps and R markdown documents. A tree allows to represent hierarchical
    data (e.g. the contents of a directory). Similar to the 'shinyTree'
    package but offers more features and options, such as the grid
    extension, restricting the drag-and-drop behavior, and settings for
    the search functionality. It is possible to attach some data to the
    nodes of a tree and then to get these data in 'Shiny' when a node is
    selected. Also provides a 'Shiny' gadget allowing to manipulate one or
    more folders, and a 'Shiny' module allowing to navigate in the server
    side file system.
Uses least squares optimisation to estimate the parameters of the best-fitting JohnsonSU distribution for a given dataset, with the possibility of the distributions corresponding to the limiting cases of the JohnsonSU distribution. The code for the Golden Section Search used in the optimisation has been adapted from E. Cai. This package has been created as an extension of my Master's thesis. E. Cai (2013, "Scripts and Functions: Using R to Implement the Golden Section Search Method for Numerical Optimization", <https://chemicalstatistician.wordpress.com/2013/04/22/using-r-to-implement-the-golden-bisection-method/>).
Fitting and analyzing a Joint Trait Distribution Model. The Joint Trait Distribution Model is implemented in the Bayesian framework using conjugate priors and posteriors, thus guaranteeing fast inference. In particular the package computes joint probabilities and multivariate confidence intervals, and enables the investigation of how they depend on the environment through partial response curves.
Minimal and memory efficient implementation of the junction tree
	     algorithm using the Lauritzen-Spiegelhalter scheme;
	     S. L. Lauritzen and D. J. Spiegelhalter (1988) 
	     <https://www.jstor.org/stable/2345762?seq=1>.
This is a collection of tools for more efficiently understanding 
  and sharing the results of (primarily) regression analyses. There are also a
  number of miscellaneous functions for statistical and programming purposes. 
  Support for models produced by the survey and lme4 packages are points of 
  emphasis.
A long-term forecast model called "Jubilee-Tectonic model" is implemented to forecast future returns of the U.S. stock market, Treasury yield, and gold price. The five-factor model forecasts the 10-year and 20-year future equity returns with high R-squared above 80 percent. It is based on linear growth and mean reversion characteristics in the U.S. stock market. This model also enhances the CAPE model by introducing the hypothesis that there are fault lines in the historical CAPE, which can be calibrated and corrected through statistical learning. In addition, it contains a module for business cycles, optimal interest rate, and recession forecasts.
A convenience tool to create 'HTML' with inline styles using 
  'juicyjuice' and 'markdown' packages. It is particularly useful when working 
  on a content management system (CMS) whose code editor eliminates style and 
  link tags. The main use case of the package is the learning management system, 
  'Moodle'. Additional helper functions for teaching purposes are provided. Learn 
  more about 'juicedown' at <https://kenjisato.github.io/juicedown/>.
Provides a GUI interface for automating data extraction from 
  multiple images containing scatter and bar plots, semi-automated tools to tinker 
  with extraction attempts, and a fully-loaded point-and-click manual extractor 
  with image zoom, calibrator, and classifier. Also provides detailed and 
  R-independent extraction reports as fully-embedded .html records.
There are occasions where you need a piece of HTML with integrated
    styles. A prime example of this is HTML email. This transformation
    involves moving the CSS and associated formatting instructions from the
    style block in the head of your document into the body of the HTML. Many
    prominent email clients require integrated styles in HTML email; otherwise a
    received HTML email will be displayed without any styling. This package will
    quickly and precisely perform these CSS transformations when given HTML text
    and it does so by using the JavaScript 'juice' library.
Generates image data for fractals (Julia and Mandelbrot sets) on the complex plane in the given region and resolution. Benoit B Mandelbrot (1982).
Provides an R interface to 'Julia',
    which is a high-level, high-performance dynamic programming language
    for numerical computing, see <https://julialang.org/> for more information.
    It provides a high-level interface as well as a low-level interface.
    Using the high level interface, you could call any 'Julia' function just like
    any R function with automatic type conversion. Using the low level interface,
    you could deal with C-level SEXP directly while enjoying the convenience of
    using a high-level programming language like 'Julia'.
Allows to import functions and whole packages from 'Julia' in R.
    Imported 'Julia' functions can directly be called as R functions.
    Data structures can be translated between 'Julia' and R.
    More details can also be found in the corresponding article
    <doi:10.18637/jss.v101.i06>.
Implementing a computationally scalable false discovery rate control procedure for replicability analysis based on maximum of p-values. Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), <https://www.biorxiv.org/content/10.1101/2023.02.13.528417v2>].
Individual based simulations of hybridizing populations,
    where the accumulation of junctions is tracked. Furthermore,
    mathematical equations are provided to verify simulation outcomes.
    Both simulations and mathematical equations are based on Janzen
    (2018, <doi:10.1101/058107>) and Janzen (2020,
    <doi:10.1101/2020.09.10.292441>).
Install packages without attaching them. If a package it is already installed, it will be skipped.
Leverages the 'yum' package to
             implement a 'YAML' ('YAML Ain't Markup Language', a human
             friendly standard for data serialization; see <https:yaml.org>)
             standard for documenting justifications, such as for decisions
             taken during the planning, execution and analysis of a study
             or during the development of a behavior change intervention
             as illustrated by Marques & Peters (2019)
             <doi:10.17605/osf.io/ndxha>. These justifications are both
             human- and machine-readable, facilitating efficient extraction
             and organisation.
Functions to justify alpha levels for statistical hypothesis tests by avoiding Lindley's paradox, or by minimizing or balancing error rates. For more information about the package please read the following: Maier & Lakens (2021) <doi:10.31234/osf.io/ts4r6>).
Provides functions to standardize and whiten data, and to perform
  Principal Component Analysis (PCA).  The main advantage of this
  package over alternatives like prcomp() is, that jvcoords makes it
  easy to convert (additional) data between the original and the
  transformed coordinates.  The package also provides a class coords,
  which can represent affine coordinate transformations.  This class
  forms the basis of the transformations provided by the package, but
  can also be used independently.  The implementation has been
  optimized to be of comparable speed (and sometimes even faster) than
  existing alternatives.
Many method to compute, predict and back-test VaR. For more detail, see the report: Value at Risk <researchgate.net>.
Miscellaneous tools and functions,
    including: generate descriptive statistics tables,
    format output, visualize relations among variables or check
    distributions, and generic functions for residual and
    model diagnostics.
Quality of life functions for interactive programming.
    Shortcuts for common combinations of functions or different default
    arguments. Not to be used in production level scripts, but useful for
    exploring and quickly manipulating data for easy analysis. Also
    imports a variety of packages to facilitate the installation of those
    imported packages on the host machine.
Build complex HTML or 'LaTeX' tables using 'kable()' from 'knitr' 
    and the piping syntax from 'magrittr'. Function 'kable()' is a light weight 
    table generator coming from 'knitr'. This package simplifies the way to 
    manipulate the HTML or 'LaTeX' codes generated by 'kable()' and allows 
    users to construct complex tables and customize styles using a readable 
    syntax.
Implementation of various kernel adaptive methods in nonparametric curve
    estimation like density estimation as introduced in Stute and Srihera (2011)
    <doi:10.1016/j.spl.2011.01.013> and Eichner and Stute (2013)
    <doi:10.1016/j.jspi.2012.03.011> for pointwise estimation, and like regression
    as described in Eichner and Stute (2012) <doi:10.1080/10485252.2012.760737>.
Search and download data from the API for Japanese Diet Proceedings (see the reference at <https://kokkai.ndl.go.jp/api.html>).
A toolkit for absolute and relative dating and analysis of
    chronological patterns. This package includes functions for
    chronological modeling and dating of archaeological assemblages from
    count data. It provides methods for matrix seriation. It also allows
    to compute time point estimates and density estimates of the
    occupation and duration of an archaeological site.
'Rcpp' implementation of the multivariate Kalman filter for state space models that can handle missing values and exogenous data in the observation and state equations.
  Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.
Implements methods for clustering mixed-type data,
  specifically combinations of continuous and nominal data. Special attention
  is paid to the often-overlooked problem of equitably balancing the
  contribution of the continuous and categorical variables. This package
  implements KAMILA clustering, a novel method for clustering
  mixed-type data in the spirit of k-means clustering. It does not require
  dummy coding of variables, and is efficient enough to scale to rather large
  data sets. Also implemented is Modha-Spangler clustering, which uses a
  brute-force strategy to maximize the cluster separation simultaneously in the
  continuous and categorical variables. For more information, see Foss, Markatou,
  Ray, & Heching (2016) <doi:10.1007/s10994-016-5575-7> and Foss & Markatou
  (2018) <doi:10.18637/jss.v083.i13>.
Methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel (Friedrichs et al., 2017, <doi:10.1155/2017/6742763>).
Various tools and data sets that support the study of kanji, including their morphology, decomposition and concepts of distance and similarity between them.
Computes the Kantorovich distance between two probability
    measures on a finite set. The Kantorovich distance is also known as
    the Monge-Kantorovich distance or the first Wasserstein distance.
Sequences encoding by using the chaos game representation.
    Löchel et al. (2019) <doi:10.1093/bioinformatics/btz493>.
Tests the homogeneity of intraclass kappa statistics obtained from independent studies or a stratified study with binary results. It is desired to compare the kappa statistics obtained in multi-center studies or in a single stratified study to give a common or summary kappa using all available information. If the homogeneity test of these kappa statistics is not rejected, then it is possible to make inferences over a single kappa statistic that summarizes all the studies. Muammer Albayrak, Kemal Turhan, Yasemin Yavuz, Zeliha Aydin Kasap (2019) <doi:10.1080/03610918.2018.1538457> Jun-mo Nam (2003) <doi:10.1111/j.0006-341X.2003.00118.x> Jun-mo Nam (2005) <doi:10.1002/sim.2321>Mousumi Banerjee, Michelle Capozzoli, Laura McSweeney,Debajyoti Sinha (1999) <doi:10.2307/3315487> Allan Donner, Michael Eliasziw, Neil Klar (1996) <doi:10.2307/2533154>.
Offers a graphical user interface for the evaluation of inter-rater agreement with Cohen's and Fleiss' Kappa. The calculation of kappa statistics is done using the R package 'irr', so that 'KappaGUI' is essentially a Shiny front-end for 'irr'.
S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Möbius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory.
Contains basic tools for sample size estimation in studies of interobserver/interrater agreement (reliability).  Includes functions for both the power-based and confidence interval-based methods, with binary or multinomial outcomes and two through six raters.
The palette includes motifs from Japanese landscape and culture.
    And it provides commands for color manipulation and 'ggplot2' color scales.
Attempts to remove vocals from a stereo '.wav' recording of a song.
This is the R implementation of Karel the robot, a programming 
  language created by Dr. R. E. Pattis at Stanford University in 1981. Karel is 
  an useful tool to teach introductory concepts about general programming, such 
  as algorithmic decomposition, conditional statements, loops, etc., in an 
  interactive and fun way, by writing programs to make Karel the robot achieve 
  certain tasks in the world she lives in. Originally based on Pascal, Karel 
  was implemented in many languages through these decades, including 'Java', 'C++', 
  'Ruby' and 'Python'. This is the first package implementing Karel in R.
This is a stochastic framework that combines biochemical reaction networks with extended Kalman filter and Rauch-Tung-Striebel smoothing. 
  This framework allows to investigate the dynamics of cell differentiation from high-dimensional clonal tracking data subject to measurement noise, false negative errors, and systematically unobserved cell types. 
  Our tool can provide statistical support to biologists in gene therapy clonal tracking studies for a deeper understanding of clonal reconstitution dynamics. Further details on the methods can be found in L. Del Core et al., (2022) <doi:10.1101/2022.07.08.499353>.
An R code with a GUI for microclimate time series, with an emphasis on underground environments. 'KarsTS' provides linear and nonlinear methods, including recurrence analysis (Marwan et al. (2007) <doi:10.1016/j.physrep.2006.11.001>) and filling methods (Moffat et al. (2007) <doi:10.1016/j.agrformet.2007.08.011>), as well as tools to manipulate easily time series and gap sets.
Convert latex math expressions to HTML and 'MathML' for use in 
    markdown documents or package manual pages. The rendering is done in 
    R using the V8 engine (i.e. server-side), which eliminates the need 
    for embedding the 'MathJax' library into your web pages. In addition
    a 'math-to-rd' wrapper is provided to automatically render beautiful
    math in R documentation files.
Provides data for Kaya identity variables (population, gross 
             domestic product, primary energy consumption, and energy-related 
             CO2 emissions) for the world and for individual nations, and 
             utility functions for looking up data,  plotting trends of 
             Kaya variables, and plotting the fuel mix for a given country
             or region. The Kaya identity (Yoichi Kaya and Keiichi Yokobori, 
             "Environment, Energy, and Economy: Strategies for Sustainability" 
             (United Nations University Press, 1998) and 
             <https://en.wikipedia.org/wiki/Kaya_identity>) expresses a nation's 
             or region's greenhouse gas emissions in terms of its population, 
             per-capita Gross Domestic Product, the energy intensity of its 
             economy, and the carbon-intensity of its energy supply.
Many data science problems reduce to operations on very tall,
    skinny matrices.  However, sometimes these matrices can be so tall that they
    are difficult to work with, or do not even fit into main memory.  One
    strategy to deal with such objects is to distribute their rows across
    several processors.  To this end, we offer an 'S4' class for tall, skinny,
    distributed matrices, called the 'shaq'.  We also provide many useful
    numerical methods and statistics operations for operating on these
    distributed objects.  The naming is a bit "tongue-in-cheek", with the class
    a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he
    starred in the film 'Kazaam'.
Computes Khattree-Bahuguna's univariate and multivariate skewness, principal-component-based Khattree-Bahuguna's multivariate skewness. It also provides several measures of univariate or multivariate skewnesses including, Pearson’s coefficient of skewness, Bowley’s univariate skewness and Mardia's multivariate skewness. See Khattree, R. and Bahuguna, M. (2019) <doi: 10.1007/s41060-018-0106-1>.
Implements approaches of non-parametric smooth test to 
            compare simultaneously K(K>1) copulas and non-parametric clustering 
            of multivariate populations with arbitrary sizes. 
            See Yves I. Ngounou Bakam and Denys Pommeret (2022) <arXiv:2112.05623> and
            Yves I. Ngounou Bakam and Denys Pommeret (2022) <arXiv:2211.06338>.
The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) <arXiv:1202.3878> is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, <doi:10.1016/j.ins.2018.03.010>). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.
It predicts any attribute (categorical) given a set of input numeric predictor values. Note that only numeric input predictors should be given.
             The k value can be chosen according to accuracies provided. The attribute to be predicted can be selected from the dropdown provided (select categorical attribute).
             This is because categorical attributes cannot be given as inputs here. A 'handsontable' is also provided to enter the input predictor values.
Predicts any variable in any categorical dataset for given values of predictor variables.
    If a dataset contains 4 variables, then any variable can be predicted based on the values of the other three variables given by the user. 
    The user can upload their own datasets and select what variable they want to predict.
    A 'handsontable' is provided to enter the predictor values and also accuracy of the prediction is also shown.
Provides an efficient implementation of univariate local polynomial
    kernel density estimators that can handle bounded and discrete data. See 
    Geenens (2014) <arXiv:1303.4121>, 
    Geenens and Wang (2018) <arXiv:1602.04862>, 
    Nagler (2018a) <arXiv:1704.07457>, 
    Nagler (2018b) <arXiv:1705.05431>.
Provides fast implementations of kernel smoothing techniques for
    bivariate copula densities, in particular density estimation and resampling,
    see Nagler (2018) <doi:10.18637/jss.v084.i07>.
Handles univariate non-parametric density estimation with 
    parametric starts and asymmetric kernels in a simple and flexible way. 
    Kernel density estimation with parametric starts involves fitting a
    parametric density to the data before making a correction with kernel 
    density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>.
    Asymmetric kernels make kernel density estimation more efficient on bounded
    intervals such as (0, 1) and the positive half-line. Supported asymmetric 
    kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>,
    the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the
    copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>.
    User-supplied kernels, parametric starts, and bandwidths are supported.
Implements the vine copula based kernel density estimator of
    Nagler and Czado (2016) <doi:10.1016/j.jmva.2016.07.003>. The estimator does
    not suffer from the curse of dimensionality and is therefore well suited for
    high-dimensional applications.
Density, distribution function, quantile function and random generation
             for the K-distribution. A plotting function that plots data on Weibull
             paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc.
Estimate entropy of multidimensional data set.
A personal collection of helper functions and code snippets
    to avoid redundancy in the spirit of the "Don't repeat yourself"
    principle of software development
    (<https://en.wikipedia.org/wiki/Don%27t_repeat_yourself>).
Provides arrays with flexible control over dimension dropping when subscripting.
Collection of utility functions used in the KEHRA project (see http://www.brunel.ac.uk/ife/britishcouncil). It refers to the multidimensional analysis of air pollution, weather and health data.
Uses Bessel functions to calculate the
    fundamental and complementary analytic solutions to the
    Kelvin differential equation.
Computes the Kendall rank correlation and Mann-Kendall
        trend test. See documentation for use of block bootstrap when
        there is autocorrelation.
Kendall random walks are a continuous-space Markov chains generated
             by the Kendall generalized convolution. This package provides tools
             for simulating these random walks and studying distributions
             related to them. For more information about Kendall random walks see Jasiulis-Gołdyn (2014) <arXiv:1412.0220>.
Demo and dataset accompaying the books :
	De l'analyse des réseaux expérimentaux à la méta-analyse: Méthodes et applications avec le logiciel R pour les sciences agronomiques et environnementales (Published 2018-06-28, Quae, for french version) by David Makowski, Francois Piraux and Francois Brun - <https://www.quae.com/produit/1514/9782759228164/de-l-analyse-des-reseaux-experimentaux-a-la-meta-analyse>
	Knowledge Synthesis in Agriculture : from Experimental Network to Meta-Analysis (in preparation for 2018-06, Springer , for English version) by David Makowski, Francois Piraux and Francois Brun
	A full description of all the material is in both books.
	ACKNOWLEDGMENTS : The French network "RMT modeling and data analysis for agriculture" (<http://www.modelia.org>) have contributed to the development of this R package. This project and network are lead by ACTA (French Technical Institute for Agriculture) and was funded by a grant from the Ministry of Agriculture and Fishing of France.
Implements the kernel method of test equating as defined in von Davier, A. A., Holland, P. W. and Thayer, D. T. (2004) <doi:10.1007/b97446> and Andersson, B. and Wiberg, M. (2017) <doi:10.1007/s11336-016-9528-7> using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting Gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.
Interface to 'Keras' <https://keras.io>, a high-level neural
  networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation,
  supports both convolution based networks and recurrent networks (as well as
  combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
'Keras Tuner' <https://keras-team.github.io/keras-tuner/> is a hypertuning framework made for humans. 
             It aims at making the life of AI practitioners, hypertuner 
             algorithm creators and model designers as simple as possible by 
             providing them with a clean and easy to use API for hypertuning. 
             'Keras Tuner' makes moving from a base model to a hypertuned one quick and 
             easy by only requiring you to change a few lines of code.
A new practical method to evaluate whether relationships between two sets of high-dimensional variables are different or not across two conditions.
    Song, H. and Wu, M.C. (2023) 
    <arXiv:2307.15268>.
An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.
Gaussian process regression with an emphasis on kernels.
    Quantitative and qualitative inputs are accepted. Some pre-defined
    kernels are available, such as radial or tensor-sum for
    quantitative inputs, and compound symmetry, low rank, group kernel
    for qualitative inputs. The user can define new kernels and
    composite kernels through a formula mechanism. Useful methods
    include parameter estimation by maximum likelihood, simulation,
    prediction and leave-one-out validation.
A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) <doi:10.1111/rssb.12244>.
Smoothed bootstrap and functions for random generation from
             univariate and multivariate kernel densities. It does not
             estimate kernel densities.
Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913"). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data.
In self-reported or anonymised data the user often encounters
    heaped data, i.e. data which are rounded (to a possibly different degree
    of coarseness). While this is mostly a minor problem in parametric density
    estimation the bias can be very large for non-parametric methods such as kernel
    density estimation. This package implements a partly Bayesian algorithm treating
    the true unknown values as additional parameters and estimates the rounding
    parameters to give a corrected kernel density estimate. It supports various
    standard bandwidth selection methods. Varying rounding probabilities (depending
    on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (<doi:10.1093/jssam/smw011>).
    Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (<doi:10.1111/rssa.12179>),
    as well as data aggregated on areas is supported.
Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.
Contains kernel smoothing tools designed for use by historical
    dialectologists and philologists for exploring spatial and temporal patterns
    in noisy historical language data, such as that obtained from historical texts.
    The main way in which these might differ from other implementations of kernel
    smoothing is that they assume that the function (linguistic variable) being
    explored has the form of the relative frequency of a series of discrete
    possibilities (linguistic variants). This package also offers a way of exploring
    distributions in 2-dimensional space and in time with separate kernels, and
    tools for identifying appropriate bandwidths for these.
Efficient implementation of Kernel SHAP, see Lundberg and Lee
    (2017) <https://dl.acm.org/doi/10.5555/3295222.3295230>, and Covert
    and Lee (2021) <http://proceedings.mlr.press/v130/covert21a>.  For
    models with up to eight features, the results are exact regarding the
    selected background data.  Otherwise, an almost exact hybrid algorithm
    involving iterative sampling is used.  The package plays well together
    with meta-learning packages like 'tidymodels', 'caret' or 'mlr3'.
    Visualizations can be done using the R package 'shapviz'.
Producing kernel estimates of the unconditional and conditional hazard
        function for right-censored data including methods of bandwidth selection.
Kernel-based machine learning methods for classification,
        regression, clustering, novelty detection, quantile regression
        and dimensionality reduction.  Among other methods 'kernlab'
        includes Support Vector Machines, Spectral Clustering, Kernel
        PCA, Gaussian Processes and a QP solver.
Provides wind energy practitioners with an effective machine learning-based
    tool that estimates a multivariate power curve and predicts the wind power output
    for a specific environmental condition.
Kernel Machine Score Test for Pathway Analysis in the Presence of 
    Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018) 
    <doi: 10.1177/0962280216653427>.
Functions for kernel smoothing (and density estimation)
  corresponding to the book: 
  Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
Fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools. The kernel smoothing is based on methods described in Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.
Adaptive estimation of the first-order intensity function of a spatio-temporal point process using kernels and variable bandwidths. The methodology used for estimation is presented in González and Moraga (2022). <arXiv:2208.12026>.
New kernel-based test and fast tests for detecting change-points or changed-intervals where the distributions abruptly change. They work well particularly for high-dimensional data.
    Song, H. and Chen, H. (2022)
    <arXiv:2206.01853>.
New kernel-based test and fast tests for testing whether two samples are from the same distribution. They work well particularly for high-dimensional data.
      Song, H. and Chen, H. (2023) 
      <arXiv:2011.06127>.
A shiny app to visualize the knowledge networks for the code concepts. Using co-occurrence matrices of EHR codes from Veterans Affairs (VA) and Massachusetts General Brigham (MGB), the knowledge extraction via sparse embedding regression (KESER) algorithm was used to construct knowledge networks for the code concepts. Background and details about the method can be found at Chuan et al. (2021) <doi:10.1038/s41746-021-00519-z>.
Fits keyword assisted topic models (keyATM) using collapsed Gibbs samplers. The keyATM combines the latent dirichlet allocation (LDA) models with a small number of keywords selected by researchers in order to improve the interpretability and topic classification of the LDA. The keyATM can also incorporate covariates and directly model time trends. The keyATM is proposed in Eshima, Imai, and Sasaki (2023) <doi:10.1111/ajps.12779>.
We developed a package 'Keyboard' for designing single-agent, drug-combination, or phase I/II dose-finding clinical trials. The 'Keyboard' designs are novel early phase trial designs that can be implemented simply and transparently, similar to the 3+3 design, but yield excellent performance, comparable to those of more-complicated, model-based designs (Yan F, Mandrekar SJ, Yuan Y (2017) <doi:10.1158/1078-0432.CCR-17-0220>, Li DH, Whitmore JB, Guo W, Ji Y. (2017) <doi:10.1158/1078-0432.CCR-16-1125>,  Liu S, Johnson VE (2016) <doi:10.1093/biostatistics/kxv040>, Zhou Y, Lee JJ, Yuan Y (2019) <doi:10.1002/sim.8475>, Pan H, Lin R, Yuan Y (2020) <doi:10.1016/j.cct.2020.105972>). The 'Keyboard' package provides tools for designing, conducting, and analyzing single-agent, drug-combination, and phase I/II dose-finding clinical trials. For more details about how to use this packge, please refer to Li C, Sun H, Cheng C, Tang L, and Pan H. (2022) "A software tool for both the maximum tolerated dose and the optimal biological dose finding trials in early phase designs". Manuscript submitted for publication.
Control your keyboard and mouse with R code by simulating key presses and mouse clicks. The input simulation is implemented with the Windows API.
Tools for keeping track of information, named
    "keys", about rows of data frame like objects. This is done by
    creating special attribute "keys" which is updated after every change
    in rows (subsetting, ordering, etc.).  This package is designed to
    work tightly with 'dplyr' package.
Efficient implementation of permutation tests for keyword analysis in corpus linguistics as described in Mildenberger (2023) <arXiv:2308.13383>.
Wait for a single key press at the 'R' prompt.
    This works in terminals, but does not currently work
    in the 'Windows' 'GUI', the 'OS X' 'GUI' ('R.app'),
    in 'Emacs' 'ESS', in an 'Emacs' shell buffer or in
    'R Studio'. In these cases 'keypress' stops with an
    error message.
Platform independent 'API' to access the operating system's
    credential store. Currently supports: 'Keychain' on 'macOS', Credential
    Store on 'Windows', the Secret Service 'API' on 'Linux', and a simple,
    platform independent store implemented with environment variables.
    Additional storage back-ends can be added easily.
Decrypts passwords stored in the Gnome Keyring, macOS Keychain and
    strings encrypted with the Windows Data Protection API.
Assign and listen to keyboard shortcuts in 'shiny' using the 
  'Mousetrap' Javascript library.
Convert keys and other values to memorable phrases. 
  Includes some methods to build lists of words.
Provides functions to identify plausible and replicable factor
  structures for a set of variables via k-fold cross validation. The process
  combines the exploratory and confirmatory factor analytic approach to scale
  development (Flora & Flake, 2017) <doi:10.1037/cbs0000069> with a cross validation
  technique that maximizes the available data (Hastie, Tibshirani, & Friedman, 2009)
  <isbn:978-0-387-21606-5>. Also available are functions to determine k by drawing 
  on power analytic techniques for covariance structures (MacCallum, Browne, &
  Sugawara, 1996) <doi:10.1037/1082-989X.1.2.130>, generate model syntax, and
  summarize results in a report.
State space modelling is an efficient and flexible framework for 
    statistical inference of a broad class of time series and other data. KFAS 
    includes computationally efficient functions for Kalman filtering, smoothing, 
    forecasting, and simulation of multivariate exponential family state space models, 
    with observations from Gaussian, Poisson, binomial, negative binomial, and gamma 
    distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA).
    There are some similar packages. First, 'lfda' is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions.
    In particular, 'lfda' seems to be impossible to test because it needs the label information of the data in the function argument. Also, the 'ks' package has a limited dimension, which makes it difficult to analyze properly.
    This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) <DOI:10.1016/j.patcog.2003.10.015>.
A streamlined cross-referencing system for R Markdown documents
    generated with 'knitr'. R Markdown is  an authoring format for generating
    dynamic content from R. 'kfigr' provides a hook for anchoring code
    chunks and a function to cross-reference document elements generated from
    said chunks, e.g. figures and tables.
A method for detecting outliers with a Kalman filter on impulsed 
  noised outliers and prediction on cleaned data. 'kfino' is a robust 
  sequential algorithm allowing to filter data with a large number of outliers. 
  This algorithm is based on simple latent linear Gaussian processes as in the 
  Kalman Filter method and is devoted to detect impulse-noised outliers. These 
  are data points that differ significantly from other observations. 'ML' 
  (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) 
  algorithms were implemented in 'kfino'. The method is described in full 
  details in the following arXiv e-Print: <arXiv:2208.00961>.
Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <arXiv:2102.01286>. Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.1016/j.jmva.2021.104864>.
Implementation for kernel functional partial least squares (KFPLS) method. KFPLS method is developed for functional nonlinear models, and the method does not require strict constraints for the nonlinear structures. The crucial function of this package is KFPLS().
Aids in identifying the Koeppen-Geiger (KG) climatic zone for 
    a given location. The Koeppen-Geiger climate zones were first published in 1884, as a system
    to classify regions of the earth by their relative heat and humidity through the year, for 
    the benefit of human health, plant and agriculture and other human activity [1]. This climate
    zone classification system, applicable to all of the earths surface, has continued to be 
    developed by scientists up to the present day.  Recently one of use (FZ) has published updated,
    higher accuracy KG climate zone definitions [2]. In this package we use these updated 
    high-resolution maps as the data source [3]. We provide functions that return the KG climate zone 
    for a given longitude and lattitude, or for a given United States zip code. In addition
    the CZUncertainty() function will check climate zones nearby to check if the given location
    is near a climate zone boundary. In addition an interactive shiny app is provided to define 
    the KG climate zone for a given longitude and lattitude, or United States zip code. 
    Digital data, as well as animated maps, showing the shift of the climate zones are provided 
    on the following website <http://koeppen-geiger.vu-wien.ac.at>.
    This work was supported by the DOE-EERE SunShot award DE-EE-0007140.
     [1] W. Koeppen, (2011) <doi:10.1127/0941-2948/2011/105>.
     [2] F. Rubel and M. Kottek, (2010) <doi:10.1127/0941-2948/2010/0430>.
     [3] F. Rubel, K. Brugger, K. Haslinger, and I. Auer, (2016) <doi:10.1127/metz/2016/0816>.
A unified software package simultaneously implemented in 'Python', 'R', and 'Matlab' providing a uniform and internally-consistent way of calculating stoichiometric equilibrium constants in modern and palaeo seawater as a function of temperature, salinity, pressure and the concentration of magnesium, calcium, sulphate, and fluorine.
The kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016) <https://proceedings.mlr.press/v48/niu16.html> and the warping algorithm proposed in Niu et al. (2017) <DOI:10.1007/s00180-017-0753-z> are implemented for parameter inference in differential equations. Four schemes are provided for improving parameter estimation in odes by using the odes regularisation and warping.
Metadata about populations and data about samples from the 1000 Genomes Project, including the 2,504 samples sequenced for the Phase 3 release and the expanded collection of 3,202 samples with 602 additional trios. The data is described in Auton et al. (2015) <doi:10.1038/nature15393> and Byrska-Bishop et al. (2022) <doi:10.1016/j.cell.2022.08.004>, and raw data is available at <http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/>. See Turner (2022) <doi:10.48550/arXiv.2210.00539> for more details.
Training and evaluating k-gram language models in R, 
        supporting several probability smoothing techniques, 
        perplexity computations, random text generation and more.
Restore underlining numeric data from rating history graph of 
    KGS (an online platform of the game of go, <http://www.gokgs.com/>). 
    A shiny application is also provided.
The King's Health Questionnaire (KHQ) is a disease-specific, 
  self-administered questionnaire designed specific to assess the impact of 
  Urinary Incontinence (UI) on Quality of Life. The questionnaire was developed 
  by Kelleher and collaborators (1997) <doi:10.1111/j.1471-0528.1997.tb11006.x>. 
  It is a simple, acceptable and reliable measure to use in the clinical setting 
  and a research tool that is useful in evaluating UI treatment outcomes. 
  The KHQ five dimensions (KHQ5D) is a condition-specific preference-based 
  measure developed by Brazier and collaborators (2008) <doi:10.1177/0272989X07301820>. 
  Although not as popular as the SF6D <doi:10.1016/S0895-4356(98)00103-6> and 
  EQ-5D <https://euroqol.org/>, the KHQ5D measures health-related quality of 
  life (HRQoL) specifically for UI, not general conditions like the others 
  two instruments mentioned. The KHQ5D ca be used in the clinical and economic 
  evaluation of health care. The subject self-rates their health in terms of 
  five dimensions: Role Limitation (RL), Physical Limitations (PL), Social 
  Limitations (SL), Emotions (E), and Sleep (S). Frequently the states on these 
  five dimensions are converted to a single utility index using country specific 
  value sets, which can be used in the clinical and economic evaluation of 
  health care as well as in population health surveys. This package provides 
  methods to calculate scores for each dimension of the KHQ; converts KHQ item 
  scores to KHQ5D scores; and also calculates the utility index of the KHQ5D.
Color schemes ready for each type of data (qualitative,
    diverging or sequential), with colors that are distinct for all
    people, including color-blind readers. This package provides an
    implementation of Paul Tol (2018) and Fabio Crameri (2018)
    <doi:10.5194/gmd-11-2541-2018> color schemes for use with 'graphics'
    or 'ggplot2'. It provides tools to simulate color-blindness and to
    test how well the colors of any palette are identifiable. Several
    scientific thematic schemes (geologic timescale, land cover, FAO
    soils, etc.) are also implemented.
An interface to store, retrieve, search, join and share datasets, based on Elasticsearch (ES) API. As a decentralized, FAIR and collaborative search engine and database effort, it proposes a simple push/pull/search mechanism only based on ES, a tool which can be deployed on nearly any hardware. It is a high-level R-ES binding to ease data usage using 'elastic' package (S. Chamberlain (2020)) <https://docs.ropensci.org/elastic/>, extends joins from 'dplyr' package (H. Wickham et al. (2020)) <https://dplyr.tidyverse.org/> and integrates specific biological format importation with Bioconductor packages such as 'rtracklayer' (M. Lawrence and al. (2009) <doi:10.1093/bioinformatics/btp328>) <http://bioconductor.org/packages/rtracklayer>, 'Biostrings' (H. Pagès and al. (2020) <doi:10.18129/B9.bioc.Biostrings>) <http://bioconductor.org/packages/Biostrings>, and 'Rsamtools' (M. Morgan and al. (2020) <doi:10.18129/B9.bioc.Rsamtools>) <http://bioconductor.org/packages/Rsamtools>, but also a long list of more common ones with 'rio' (C-h. Chan and al. (2018)) <https://cran.r-project.org/package=rio>.
Contains kidney care oriented functions.
			 Current version contains functions for calculation of:
			 - Kidney Donor Risk Index and Kidney Donor Profile Index for kidney transplant donors by Rao et al. (2009) <doi:10.1097/TP.0b013e3181ac620b>.
			 - Estimated glomerular filtration rate by CKD-EPI, MDRD and other equations.
			 Citation: Bikbov B. R open source programming code for calculation of the Kidney Donor Profile Index and Kidney Donor Risk Index. Kidney Diseases (2018) 4:269–272 <doi:10.1159/000492427> (the only citation for the whole package).
Caches and then connects to a 'sqlite' database containing half a million pediatric drug safety signals. 
    The database is part of a family of resources catalogued at <https://nsides.io>. The database
    contains 17 tables where the description table provides a map between the fields the field's details. 
    The database was created by Nicholas Giangreco during his PhD thesis which you can read in Giangreco (2022) <doi:10.7916/d8-5d9b-6738>. 
    The observations are from the Food and Drug Administration's Adverse Event Reporting System. Generalized additive models estimated
    drug effects across child development stages for the occurrence of an adverse event when exposed to a drug compared to other drugs.
    Read more at the methods detailed in Giangreco (2022) <doi:10.1016/j.medj.2022.06.001>.
A collection of functions for analyzing data typically collected 
    or used by behavioral scientists. Examples of the functions include
    a function that compares groups in a factorial experimental design,
    a function that conducts two-way analysis of variance (ANOVA),
    and a function that cleans a data set generated by Qualtrics surveys.
    Some of the functions will require installing additional package(s).
    Such packages and other references are cited within the section
    describing the relevant functions. Many functions in this package
    rely heavily on these two popular R packages:
    Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>.
    Wickham et al. (2021) <https://CRAN.R-project.org/package=ggplot2>.
'Rcpp' implementation of the multivariate Kim filter, which combines the Kalman and Hamilton filters for state probability inference. 
  The filter is designed for state space models and can handle missing values and exogenous data in the observation and state equations.
  Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.
A collection of useful functions not found anywhere else,
        mainly for programming: Pretty intervals, generalized lagged
        differences, checking containment in an interval, and an
        alternative interface to assign().
Analysis of kin-cohort studies. kin.cohort provides estimates of age-specific 
 cumulative risk of a disease for carriers and noncarriers of a mutation. The cohorts are
 retrospectively built from relatives of probands for whom the genotype is known. Currently 
 the method of moments and marginal maximum likelihood are implemented. Confidence intervals 
 are calculated from bootstrap samples.
 Most of the code is a translation from previous 'MATLAB' code by N. Chatterjee.
Functions for simulating and estimating kinship-related dispersal. Based
    on the methods described in M. Jasper, T.L. Schmidt., N.W. Ahmad, S.P. Sinkins & A.A. 
    Hoffmann (2019) <doi:10.1111/1755-0998.13043> "A genomic approach to inferring kinship 
    reveals limited intergenerational dispersal in the yellow fever mosquito".
    Assumes an additive variance model of dispersal in two dimensions, compatible with 
    Wright's neighbourhood area. Simple and composite dispersal simulations are supplied, 
    as well as the functions needed to estimate parent-offspring dispersal for simulated or 
    empirical data, and to undertake sampling design for future field studies of dispersal. 
    For ease of use an integrated Shiny app is also included.
Allows analyzing time series representing two-dimensional movements.
    It accepts a data frame with a time (t), horizontal (x) and vertical (y) coordinate as columns,
    and returns several dynamical properties such as speed, acceleration or curvature.
Data on houses in and around Seattle WA are included. Basic 
    characteristics are given along with sale prices.
Methods for inference about relationships between contributors to a DNA mixture and 
  other individuals of known genotype: a basic example would be testing whether a contributor to 
  a mixture is the father of a child of known genotype. This provides most of the functionality
  of the 'KinMix' package, but with some loss of efficiency and restriction on problem size,
  as the latter uses 'RHugin' as the Bayes net engine, while this package uses 'gRain'.
  The package implements the methods introduced in 
  Green, P. J. and Mortera, J. (2017) <doi:10.1016/j.fsigen.2017.02.001> and 
  Green, P. J. and Mortera, J. (2021) <doi:10.1111/rssc.12498>.
Routines to handle family data with a pedigree object. The initial purpose
	     was to create correlation structures that describe family relationships such 
	     as kinship and identity-by-descent, which can be used to model family data 
	     in mixed effects models, such as in the coxme function. Also includes a tool
	     for pedigree drawing which is focused on producing compact layouts without 
	     intervention. Recent additions include utilities to trim the pedigree object
	     with various criteria, and kinship for the X chromosome.
Machine learning, containing several algorithms for supervised and unsupervised classification, in addition to a function that plots the Receiver Operating Characteristic (ROC) and Precision-Recall (PRC) curve graphs, and also a function that returns several metrics used for model evaluation, the latter can be used in ranking results from other packs.
Multi-modal magnetic resonance imaging ('MRI')
    data from the 'Kirby21' 'reproducibility' study
    <https://www.nitrc.org/projects/multimodal/>, including functional
    and structural imaging.
Functional magnetic resonance imaging ('fMRI')
       data from the 'Kirby21' 'reproducibility' study
       <doi:10.1016/j.neuroimage.2010.11.047>.
Structural T1 magnetic resonance imaging ('MRI')
       data from the 'Kirby21' 'reproducibility' study
       <doi:10.1016/j.neuroimage.2010.11.047>.
Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, stochastic migration model. Providing accessibility from an origin or previous distribution for niche-based species distribution models. Nobis & Normand (2014) <doi:10.1111/ecog.00930>.
Basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.
Provides tools to calculate the theoretical hydrodynamic response
    of an aquifer undergoing harmonic straining or pressurization, or analyze
    measured responses. There are
    two classes of models here, designed for use with confined
    aquifers: (1) for sealed wells, based on the model of 
    Kitagawa et al (2011, <doi:10.1029/2010JB007794>), 
    and (2) for open wells, based on the models of
    Cooper et al (1965, <doi:10.1029/JZ070i016p03915>), 
    Hsieh et al (1987, <doi:10.1029/WR023i010p01824>), 
    Rojstaczer (1988, <doi:10.1029/JB093iB11p13619>), 
    Liu et al (1989, <doi:10.1029/JB094iB07p09453>), and
    Wang et al (2018, <doi:10.1029/2018WR022793>). Wang's 
    solution is a special exception which
    allows for leakage out of the aquifer 
    (semi-confined); it is equivalent to Hsieh's model
    when there is no leakage (the confined case).
    These models treat strain (or aquifer head) as an input to the
    physical system, and fluid-pressure (or water height) as the output. The
    applicable frequency band of these models is characteristic of seismic
    waves, atmospheric pressure fluctuations, and solid earth tides.
A wrapper for querying 'WISKI' databases via the 'KiWIS' 'REST' API. 'WISKI' is an 'SQL' relational database 
  used for the collection and storage of water data developed by KISTERS and 'KiWIS' is a 'REST' service that provides
  access to 'WISKI' databases via HTTP requests (<https://water.kisters.de/en/technology-trends/kisters-and-open-data/>). 
  Contains a list of default databases (called 'hubs') and also allows users to provide their own 'KiWIS' URL. 
  Supports the entire query process- from metadata to specific time series values. All data is returned as tidy tibbles.
Weighted k-Nearest Neighbors for Classification, Regression and Clustering.
A variable selection procedure, dubbed KKO, for nonparametric additive model with finite-sample false discovery rate control guarantee. The method integrates three key components: knockoffs, subsampling for stability, and random feature mapping for nonparametric function approximation. For more information, see the accompanying paper: Dai, X., Lyu, X., & Li, L. (2021). “Kernel Knockoffs Selection for Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>.
Miscellaneous functions for classification and visualization,
     e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, 
     an interface to 'svmlight' and stepclass() wrapper variable selection 
     for supervised classification, partimat() visualization of classification rules 
         and shardsplot() of cluster results as well as kmodes() clustering for categorical data, 
     corclust() variable clustering, variable extraction from different variable clustering models 
         and weight of evidence preprocessing.
Functions to search, retrieve and apply classifications 
  and codelists using Statistics Norway's API <https://www.ssb.no/klass> 
  from the system 'KLASS'. Retrieves classifications by date with options 
  to choose language, hierarchical level and formatting.
A set of functions designed to quickly generate results of a multiple choice
          test. Generates detailed global results, lists for anonymous feedback and
          personalised result feedback (in LaTeX and/or PDF format), as well as item
          statistics like Cronbach's alpha or disciminatory power. 'klausuR' also
          includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for
          its basic features. The respective R package 'rkward' cannot be installed
          directly from a repository, as it is a part of RKWard. To make full use of this
          feature, please install RKWard from <https://rkward.kde.org> (plugins are
          detected automatically). Due to some restrictions on CRAN, the full package
          sources are only available from the project homepage.
Most importantly, calculates Kullback-Leibler Divergence (KLD), Turing's perspective estimator
 and their confidence intervals.
Six relational 'tibbles' from the Kootenay Lake Large Trout Exploitation study.
  The study which ran from 2008 to 2014 caught, tagged and released large Rainbow Trout and Bull Trout
  in Kootenay Lake by boat angling. 
  The fish were tagged with internal acoustic tags and/or high reward external tags
  and subsequently detected by an acoustic receiver array as well as reported by anglers.
  The data are analysed by Thorley and Andrusak (1994) <doi:10.7717/peerj.2874>
  to estimate the natural and fishing mortality of both species.
Provides the function to calculate the kernel-lasso expansion, Z-score, and max-min-scale standardization.It can increase the dimension of existed dataset and remove abundant features by lasso. Z Dai, L Jiayi, T Gong, C Wang (2021) <doi:10.1088/1742-6596/1955/1/012047>.
Kernel Learning Integrative Clustering (KLIC) is an algorithm that allows to combine multiple kernels, each representing a different measure of the similarity between a set of observations. The contribution of each kernel on the final clustering is weighted according to the amount of information carried by it. As well as providing the functions required to perform the kernel-based clustering, this package also allows the user to simply give the data as input: the kernels are then built using consensus clustering. Different strategies to choose the best number of clusters are also available. For further details please see Cabassi and Kirk (2020) <doi:10.1093/bioinformatics/btaa593>.
A 'shiny' application for forensic kinship testing, based on
    the 'pedsuite' R packages. 'KLINK' is closely aligned with the (non-R)
    software 'Familias' and 'FamLink', but offers several unique features,
    including visualisations and automated report generation. The
    calculation of likelihood ratios supports pairs of linked markers, and
    all common mutation models.
An implementation of the blocking algorithm KLSH in Steorts, Ventura, Sadinle, Fienberg (2014) <DOI:10.1007/978-3-319-11257-2_20>, which is a k-means variant of locality sensitive hashing. The method is illustrated with examples and a vignette.
Used to create dynamic, interactive 'D3.js' 
  based parallel coordinates and principal component plots in 'R'.
  The plots make visualizing k-means or other clusters simple and informative.
Computes various confidence intervals for the Kaplan-Meier
        estimator, namely: Peto's CI, Rothman CI, CI's based on
        Greenwood's variance, Thomas and Grunkemeier CI and the
        simultaneous confidence bands by Nair and Hall and Wellner.
Given constraints for right censored data, we use a recursive computational algorithm to calculate the the "constrained" Kaplan-Meier estimator. The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data and accelerated failure time model with given coefficients. EM algorithm from emplik package is used to get the initial value. The properties and performance of the EM algorithm is discussed in Mai Zhou and Yifan Yang (2015)<doi: 10.1007/s00180-015-0567-9> and Mai Zhou and Yifan Yang (2017) <doi: 10.1002/wics.1400>. More applications could be found in Mai Zhou (2015) <doi: 10.1201/b18598>.
Implementations of the kernel measure of multi-sample dissimilarity (KMD) between
    several samples using K-nearest neighbor graphs and minimum spanning trees. The KMD 
    measures the dissimilarity between multiple samples, based on the observations from them.
    It converges to the population quantity (depending on the kernel) which is between 0 and 1.
    A small value indicates the multiple samples are from the same distribution, and a large value
    indicates the corresponding distributions are different. The population quantity is 0 if and only
    if all distributions are the same, and 1 if and only if all distributions are mutually singular.
    The package also implements the tests based on KMD for H0: the M distributions are equal
    against H1: not all the distributions are equal. Both permutation test and asymptotic test are
    available. These tests are consistent against all alternatives where at least two samples have
    different distributions. For more details on KMD and the associated tests, see Huang, Z. and
    B. Sen (2022) <arXiv:2210.00634>.
Algorithms of distance-based k-medoids clustering: simple and fast 
  k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids. 
  Calculate distances for mixed variable data such as Gower, Podani, Wishart, 
  Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and 
  relative criteria. The internal criteria includes silhouette index and shadow 
  values. The relative criterium applies bootstrap procedure producing a heatmap 
  with a flexible reordering matrix algorithm such as complete, ward, or average 
  linkages. The cluster result can be plotted in a marked barplot or pca biplot.
Online, Semi-online, and Offline K-medians algorithms are
    given. For both methods, the algorithms can be initialized
    randomly or with the help of a robust hierarchical
    clustering. The number of clusters can be selected with the
    help of a penalized criterion. We provide functions to provide
    robust clustering. Function gen_K() enables to generate a sample
    of data following a contaminated Gaussian mixture.
    Functions Kmedians() and Kmeans() consists in a K-median and a
    K-means algorithms while Kplot() enables to produce graph for both
    methods. 
    Cardot, H., Cenac, P. and Zitt, P-A. (2013). "Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm". Bernoulli, 19, 18-43. <doi:10.3150/11-BEJ390>.
    Cardot, H. and Godichon-Baggioni, A. (2017). "Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis". Test, 26(3), 461-480 <doi:10.1007/s11749-016-0519-x>.
    Godichon-Baggioni, A. and Surendran, S. "A penalized criterion for selecting the number of clusters for K-medians"     <arXiv:2209.03597> 
    Vardi, Y. and Zhang, C.-H. (2000). "The multivariate L1-median and associated data depth". Proc. Natl. Acad. Sci. USA, 97(4):1423-1426. <doi:10.1073/pnas.97.4.1423>.
Contains tools for rapidly computing distance matrices 
    and clustering large sequence datasets using fast alignment-free 
    k-mer counting and recursive k-means partitioning. 
    See Vinga and Almeida (2003) <doi:10.1093/bioinformatics/btg005> 
    for a review of k-mer counting methods and applications for 
    biological sequence analysis.
Performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions (Allignol and Beyersmann, 2010 <doi:10.1093/biostatistics/kxq018>).
An implementation of k-means specifically design
        to cluster longitudinal data. It provides facilities to deal with missing
        value, compute several quality criterion (Calinski and Harabatz,
        Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical
        interface for choosing the 'best' number of clusters.
An implementation of k-means specifically design
        to cluster joint trajectories (longitudinal data on
        several variable-trajectories).
        Like 'kml', it provides facilities to deal with missing
        value, compute several quality criterion (Calinski and Harabatz,
        Ray and Turie, Davies and Bouldin, BIC,...) and propose a graphical
        interface for choosing the 'best' number of clusters. In addition, the 3D graph
        representing the mean joint-trajectories of each cluster can be exported through
        LaTeX in a 3D dynamic rotating PDF graph.
An implementation of the 'k-means--' algorithm proposed
    by Chawla and Gionis, 2013 in their paper,
    "k-means-- : A unified approach to clustering and outlier detection.
    SIAM International Conference on Data Mining (SDM13)",
    <doi:10.1137/1.9781611972832.21>
    and using 'ordering' described by Howe, 2013 in the thesis,
    Clustering and anomaly detection in tropical cyclones".
    Useful for creating (potentially) tighter clusters than
    standard k-means and simultaneously finding outliers inexpensively
    in multidimensional space.
Data sets and functions for Klein and Moeschberger (1997),
        "Survival Analysis, Techniques for Censored and Truncated
        Data", Springer.
Produce Kaplan–Meier plots in the style recommended
    following the KMunicate study by Morris et al. (2019)
    <doi:10.1136/bmjopen-2019-030215>. The KMunicate style consists of
    Kaplan-Meier curves with confidence intervals to quantify uncertainty
    and an extended risk table (per treatment arm) depicting the number of
    study subjects at risk, events, and censored observations over time.
    The resulting plots are built using 'ggplot2' and can be further
    customised to a certain extent, including themes, fonts, and colour
    scales.
The sampl.mcmc() function creates samples of the feasible region of a knapsack problem with both equalities and inequalities constraints.
Given a set of points around a knee curve,
    analyzes first and second derivatives to find knee points.
Provides the ability to create dynamic citations
    in which the bibliographic information is pulled from the web rather
    than having to be entered into a local database such as 'bibtex' ahead of
    time. The package is primarily aimed at authoring in the R 'markdown'
    format, and can provide outputs for web-based authoring such as linked
    text for inline citations.  Cite using a 'DOI', URL, or
    'bibtex' file key.  See the package URL for details.
Provides several helper functions for working with 'knitr' and 'LaTeX'.
  It includes 'xTab' for creating traditional 'LaTeX' tables, 'lTab' for generating
  'longtable' environments, and 'sTab' for generating a 'supertabular' environment.
  Additionally, this package contains a knitr_setup() function which fixes a
  well-known bug in 'knitr', which distorts the 'results="asis"' command when used
  in conjunction with user-defined commands; and a com command (<<com=TRUE>>=)
  which renders the output from 'knitr' as a 'LaTeX' command.
Provides a general-purpose tool for dynamic report generation in R
    using Literate Programming techniques.
A framework to create Bootstrap <http://getbootstrap.com/> HTML reports from 'knitr'
    'rmarkdown'.
Implements a data language engine for incorporating data directly in 
    'rmarkdown' documents so that they can be made completely standalone.
Provides a progress bar similar to 'dplyr' that can write progress out to a 
    variety of locations, including stdout(), stderr(), or from file(). Useful when using 'knitr' or 'rmarkdown',
    and you still want to see progress of calculations in the terminal.
Convert an R Markdown documents into an '.xlsx' spreadsheet reports
    with the knitxl() function, which works similarly to knit() from the 
    'knitr' package. The generated report can be opened in 'Excel' or similar
    software for further analysis and presentation.
Similarly to the 'FNN' package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix.
    The implementation is based on cover trees introduced by
    Alina Beygelzimer, Sham Kakade, and John Langford (2006) <doi:10.1145/1143844.1143857>.
Two main functionalities are provided. One of them is predicting values with 
    k-nearest neighbors algorithm and the other is optimizing the parameters k and d of the algorithm.
    These are carried out in parallel using multiple threads.
An interactive document on  the topic of K-nearest neighbour (KNN) using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/KNNShiny/>.
k Nearest Neighbors with variable selection, combine grid search and forward selection to achieve variable selection in order to improve k Nearest Neighbors predictive performance.
Functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) <arXiv:2112.06266>.
The knockoff filter is a general procedure for controlling the false discovery rate (FDR)
  when performing variable selection. 
  For more information, see the website below and the accompanying paper: Candes et al., 
  "Panning for gold: model-X knockoffs for high-dimensional controlled variable selection", 
  J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577.
Identification of putative causal variants in genome-wide association studies using hybrid analysis of both the trio and population designs.
Functions for identification of putative causal loci in whole-genome sequencing data. The functions allow genome-wide association scan. It also includes an efficient knockoff generator for genetic data.
Identification of putative causal variants in genome-wide association studies with the trio design. The package implements the methods in the paper: Yang, Y., Wang, C., Liu, L., Buxbaum, J., He, Z., & Ionita-Laza, I. (2022). KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design. The American Journal of Human Genetics, 109(10), 1761-1776.
Makes nice pictures of knots using Bezier curves and
  numerical optimization.
It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user or polygons. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas.
Retrieve data from 'kintone' (<https://www.kintone.com/>) via its API.
    'kintone' is an enterprise application platform.
Wrapper for 'Kobotoolbox' APIs ver 2 mentioned at <https://support.kobotoolbox.org/api.html>, to download data from 'Kobotoolbox' to R. Small and simple package that adds immense convenience for the data professionals using 'Kobotoolbox'.
An unsupervised and semi-supervised learning algorithm that performs feature extraction 
  from noisy and high-dimensional data. It facilitates identification of patterns representing underlying 
  groups on all samples in a data set. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. 
  (2017) Bioinformatics <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014) 
  Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.
Read Swiss time series data from the 'KOF Data' API, <https://datenservice.kof.ethz.ch>. The API provides macro economic time series data mostly about Switzerland. The package itself is a set of wrappers around the 'KOF Datenservice' API. The 'kofdata' package is able to consume public information as well as data that requires an API token.
Provides a function that uses a genetic algorithm to search for a subset
 of size k from the integers 1:n, such that a user-supplied objective function 
 is minimized at that subset.  The selection step is done by tournament selection 
 based on ranks, and elitism may be used to retain a portion of the best solutions 
 from one generation to the next. Population objective function values may 
 optionally be evaluated in parallel.
Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.
Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.
Provides utilities for 'Kokudo Suuchi', the GIS data service of the Japanese government.
    See <https://nlftp.mlit.go.jp/index.html> for more information.
Write beautiful yet customizable letters in R Markdown and
  directly obtain the finished PDF. Smooth generation of PDFs is realized by
  'rmarkdown', the 'pandoc-letter' template and the 'KOMA-Script' letter class.
  'KOMA-Script' provides enhanced replacements for the standard 'LaTeX' classes
  with emphasis on typography and versatility. 'KOMA-Script' is particularly
  useful for international writers as it handles various paper formats well, 
  provides layouts for many common window envelope types (e.g. German, US, 
  French, Japanese) and lets you define your own layouts. The package comes 
  with a default letter layout based on 'DIN 5008B'.
Statistical methods that quantify the conditions necessary to alter
    inferences, also known as sensitivity analysis, are becoming increasingly
    important to a variety of quantitative sciences. A series of recent works,
    including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al.
    (2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses
    by considering the characteristics of omitted variables or unobserved cases
    that would change an inference if such variables or cases were observed. These
    analyses generate statements such as "an omitted variable would have to be
    correlated at xx with the predictor of interest (e.g., treatment) and outcome
    to invalidate an inference of a treatment effect". Or "one would have to replace
    pp percent of the observed data with null hypothesis cases to invalidate the
    inference". We implement these recent developments of sensitivity analysis and
    provide modules to calculate these two robustness indices and generate such
    statements in R. In particular, the functions konfound(), pkonfound() and 
    mkonfound() allow users to calculate the robustness of inferences for a user's 
    own model, a single published study and multiple studies respectively.
The K-sample omnibus non-proportional hazards (KONP) tests are powerful non-parametric tests for comparing K (>=2) hazard functions based on right-censored data (Gorfine, Schlesinger and Hsu, 2020, <doi:10.1177/0962280220907355>). These tests are consistent against any differences between the hazard functions of the groups. The KONP tests are often more powerful than other existing tests, especially under non-proportional hazard functions.
A set of tools to analyze texts. Includes, amongst others, functions for
          automatic language detection, hyphenation, several indices of lexical diversity
          (e.g., type token ratio, HD-D/vocd-D, MTLD) and readability (e.g., Flesch,
          SMOG, LIX, Dale-Chall). Basic import functions for language corpora are also
          provided, to enable frequency analyses (supports Celex and Leipzig Corpora
          Collection file formats) and measures like tf-idf. Note: For full functionality
          a local installation of TreeTagger is recommended. It is also recommended to
          not load this package directly, but by loading one of the available language
          support packages from the 'l10n' repository
          <https://undocumeantit.github.io/repos/l10n/>. 'koRpus' also includes a plugin
          for the R GUI and IDE RKWard, providing graphical dialogs for its basic
          features. The respective R package 'rkward' cannot be installed directly from a
          repository, as it is a part of RKWard. To make full use of this feature, please
          install RKWard from <https://rkward.kde.org> (plugins are detected
          automatically). Due to some restrictions on CRAN, the full package sources are
          only available from the project homepage. To ask for help, report bugs, request
          features, or discuss the development of the package, please subscribe to the
          koRpus-dev mailing list (<https://korpusml.reaktanz.de>).
Adds support for the English language to the 'koRpus' package. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the
                    package, please consider subscribing to the koRpus-dev mailing list (<https://korpusml.reaktanz.de>).
Performs variable selection for many types of L1-regularised regressions using the revisited knockoffs procedure. This procedure uses a matrix of knockoffs of the covariates independent from the response variable Y. The idea is to determine if a covariate belongs to the model depending on whether it enters the model before or after its knockoff. The procedure suits for a wide range of regressions with various types of response variables. Regression models available are exported from the R packages 'glmnet' and 'ordinalNet'. Based on the paper linked to via the URL below: Gegout A., Gueudin A., Karmann C. (2019) <arXiv:1907.03153>.
API wrapper to download statistical information from the
             Korean Statistical Information Service (KOSIS)
             <https://kosis.kr/openapi/index/index.jsp>.
Cubic spline fitting along with knot selection, includes support for additional variables.
Implementations of two empirical versions the kernel partial correlation (KPC) coefficient
    and the associated variable selection algorithms. KPC is a measure of the strength of conditional
    association between Y and Z given X, with X, Y, Z being random variables taking values in
    general topological spaces. As the name suggests, KPC is defined in terms of kernels on
    reproducing kernel Hilbert spaces (RKHSs). The population KPC is a deterministic number
    between 0 and 1; it is 0 if and only if Y is conditionally independent of Z given X, and it is 1 if
    and only if Y is a measurable function of Z and X. One empirical KPC estimator is based on
    geometric graphs, such as K-nearest neighbor graphs and minimum spanning trees, and is
    consistent under very weak conditions. The other empirical estimator, defined using conditional
    mean embeddings (CMEs) as used in the RKHS literature, is also consistent under suitable
    conditions. Using KPC, a stepwise forward variable selection algorithm KFOCI (using the graph
    based estimator of KPC) is provided, as well as a similar stepwise forward selection algorithm
    based on the RKHS based estimator. For more details on KPC, its empirical estimators and its
    application on variable selection, see Huang, Z., N. Deb, and B. Sen (2022). “Kernel partial
    correlation coefficient – a measure of conditional dependence” (URL listed below). When X is
    empty, KPC measures the unconditional dependence between Y and Z, which has been described
    in Deb, N., P. Ghosal, and B. Sen (2020), “Measuring association on topological spaces using
    kernels and geometric graphs” <arXiv:2010.01768>, and it is implemented in the functions
    KMAc() and Klin() in this package. The latter can be computed in near linear time.
Kernel PC (kPC) algorithm for causal structure learning and causal inference using graphical models. kPC is a version of PC algorithm that uses kernel based independence criteria in order to be able to deal with non-linear relationships and non-Gaussian noise.
The number of clusters (k) is needed to start all the partitioning clustering algorithms. An optimal value of this input argument is widely determined by using some internal validity indices. Since most of the existing internal indices suggest a k value which is computed from the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary,  the package 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set.
Functions that implement the known population median test.
Software for k-means clustering of partially 
    observed data from Chi, Chi, and Baraniuk (2016) <doi:10.1080/00031305.2015.1086685>.
Implements a quantified approach to the Kraljic Matrix (Kraljic, 1983, <https://hbr.org/1983/09/purchasing-must-become-supply-management>)
    for strategically analyzing a firm’s purchasing portfolio. It combines multi-objective decision analysis to measure purchasing characteristics and
    uses this information to place products and services within the Kraljic Matrix.
Provides basic functions for Continuation-Passing Style development.
Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) <doi:10.1177/1532440020930197>.
An implementation of a simple and highly optimized ordinary kriging algorithm to plot geographical data.
Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise.
Provides tools for applying Krippendorff's Alpha methodology <DOI:10.1080/19312450709336664>. Both the customary methodology and Hughes' methodology <DOI:10.48550/arXiv.2210.13265> are supported, the former being preferred for larger datasets, the latter for smaller datasets. The framework supports common and user-defined distance functions, and can accommodate any number of units, any number of coders, and missingness. Interval estimation can be done in parallel for either methodology.
Provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data.
Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).
Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence. This package is described in Fong et al. (2015) <DOI:10.1093/biostatistics/kxu056>.
Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.
The goal of 'kronos' is to provide an easy-to-use framework to analyse circadian or otherwise rhythmic data using the familiar R linear modelling syntax, while taking care of the trigonometry under the hood.
Kernel smoothers for univariate and multivariate data, with comprehensive visualisation and bandwidth selection capabilities, including for densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. Chacon & Duong (2018) <doi:10.1201/9780429485572>.
The Retained Component Criterion for Principal Component Analysis (RCC_PCA) is a tool to determine the optimal number of components to retain in PCA.
Compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests 
	with different rank score criteria, Steel's multiple comparison test, and the 
        Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact 
        P-values, all valid under randomization, with or without ties, or conditionally 
        under random sampling from populations, given the observed tie pattern.  Except for 
        Steel's test and the JT test it also combines these tests across several blocks of 
	samples.  Also analyzed are 2 x t contingency tables and their blocked combinations 
	using the Kruskal-Wallis criterion.  Steel's test is inverted to provide simultaneous 
	confidence bounds for shift parameters.  A plotting function compares tail probabilities
	obtained under asymptotic approximation with those obtained via simulation or exact 
	calculations.
Implements the Lilliefors-corrected Kolmogorov-Smirnov test for use
    in goodness-of-fit tests, suitable when population parameters are unknown and
    must be estimated by sample statistics. P-values are estimated by simulation.
    Can be used with a variety of continuous distributions, including normal,
    lognormal, univariate mixtures of normals, uniform, loguniform, exponential,
    gamma, and Weibull distributions. Functions to generate random numbers and
    calculate density, distribution, and quantile functions are provided for use
    with the log uniform and mixture distributions.
An adaptation of Kernelized Stein Discrepancy, this package provides a goodness-of-fit test of whether a given i.i.d. sample is drawn from a given distribution. It works for any distribution once its score function (the derivative of log-density) can be provided. This method is based on "A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation" by Liu, Lee, and Jordan, available at <arXiv:1602.03253>.
Infers relative kinase activity from phosphoproteomics data
    using the method described by Casado et al. (2013) <doi:10.1126/scisignal.2003573>.
Selection of k in k-means clustering based on Pham et al. paper
    ``Selection of k in k-means clustering''.
Computes a p-value of the one-sample two-sided (or one-sided, as a special case) Kolmogorov-Smirnov (KS) statistic, for any fixed critical level, and an arbitrary, possibly large sample size for a pre-specified purely discrete, mixed or continuous cumulative distribution function (cdf) under the null hypothesis. If a data sample is supplied, 'KSgeneral' computes the p-value corresponding to the value of the KS test statistic computed based on the user provided data sample. The package 'KSgeneral' implements a novel, accurate and efficient method named Exact-KS-FFT, expressing the p-value as a double-boundary non-crossing probability for a homogeneous Poisson process, which is then efficiently computed using Fast Fourier Transform (FFT). The package can also be used to compute and plot the complementary cdf of the KS statistic which is known to depend on the hypothesized distribution when the latter is discontinuous (i.e. purely discrete or mixed). To cite this package in publication use: Dimitrina S. Dimitrova, Vladimir K. Kaishev, and Senren Tan. Computing the Kolmogorov-Smirnov Distribution When the Underlying CDF is Purely Discrete, Mixed, or Continuous. Journal of Statistical Software. 2020; 95(10): 1--42. <doi:10.18637/jss.v095.i10>.
Clustering typically assigns data points into discrete groups, but the clusters can sometimes be indistinct. Cluster sharpening adjusts an existing clustering to create contrast between groups. This package provides a general interface for cluster sharpening along with several implementations based on different excision criteria.
Prediction with k* nearest neighbor algorithm 
    based on a publication by Anava and Levy (2016) <arXiv:1701.07266>.
To fit the kernel semi-parametric model and its extensions. It allows multiple kernels and unlimited interactions in the same model. Coefficients are estimated by maximizing a penalized log-likelihood; penalization terms and hyperparameters are estimated by minimizing leave-one-out error. It includes predictions with confidence/prediction intervals, statistical tests for the significance of each kernel, a procedure for variable selection and graphical tools for diagnostics and interpretation of covariate effects. Currently it is implemented for continuous dependent variables. The package is based on the paper of Liu et al. (2007), <doi:10.1111/j.1541-0420.2007.00799.x>.
Using this package you can combine known kinase substrate relationships with experimental data and determine active kinases and their substrates.
Implements the Known Sub-Sequence Algorithm <doi:10.1016/j.aaf.2021.12.013>, which helps to automatically identify and validate the best method for missing data imputation in a time series. Supports the comparison of multiple state-of-the-art algorithms.
Knowledge space theory by Doignon and Falmagne (1999) 
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework, which proposes mathematical formalisms to operationalize 
   knowledge structures in a particular domain. The 'kst' package provides 
   basic functionalities to generate, handle, and manipulate knowledge 
   structures and knowledge spaces.
Tools for estimate (joint) cumulants and (joint) products of cumulants of a random sample using (multivariate) k-statistics and (multivariate) polykays, unbiased estimators with minimum variance. Tools for generating univariate and multivariate Faa di Bruno's formula and related polynomials, such as Bell polynomials, generalized complete Bell polynomials, partition polynomials and generalized partition polynomials. For more details see Di Nardo E., Guarino G., Senato D. (2009) <arXiv:0807.5008>, <arXiv:1012.6008>.
Knowledge space theory by Doignon and Falmagne (1999) 
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework which proposes mathematical formalisms to operationalize 
   knowledge structures in a particular domain.  The 'kstIO' package 
   provides basic functionalities to read and write KST data from/to 
   files to be used together with the 'kst', 'kstMatrix', 'pks' or 
   'DAKS' packages.
Knowledge space theory by Doignon and Falmagne (1999)
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework, which proposes mathematical formalisms to operationalize
   knowledge structures in a particular domain. The 'kstMatrix' package 
   provides basic functionalities to generate, handle, and manipulate 
   knowledge structures and knowledge spaces. Opposed to the 'kst'
   package, 'kstMatrix' uses matrix representations for knowledge
   structures. Furthermore, 'kstMatrix' contains several knowledge spaces
   developed by the research group around Cornelia Dowling through
   querying experts.
A clustering algorithm similar to K-Means is implemented, it has two main advantages, 
    namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when
    there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, 
    if there are no outliers in the sample, results will be similar than those obtained by a classic algorithm (K-Means).
    Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale.
    (see Gonzalez, Yohai and Zamar (2019) <arxiv:1906.08198>).
Provides a function called COTUCKER3() (Co-Inertia Analysis
    + Tucker3 method) which performs a Co-Tucker3 analysis of two sequences of
    matrices, as well as other functions called PCA() (Principal Component Analysis)
    and BGA() (Between-Groups Analysis), which perform analysis of one matrix,
    COIA() (Co-Inertia Analysis), which performs analysis of two matrices, PTA()
    (Partial Triadic Analysis), STATIS(), STATISDUAL() and TUCKER3(), which perform analysis of a sequence
    of matrices, and BGCOIA() (Between-Groups Co-Inertia Analysis), STATICO()
    (STATIS method + Co-Inertia Analysis), COSTATIS() (Co-Inertia Analysis + STATIS
    method), which also perform analysis of two sequences of matrices.
This is designed for use with an arbitrary set of equations with
 an arbitrary set of unknowns.
 The user selects "fixed" values for enough unknowns to leave as many variables as
 there are equations, which in most cases means the system is properly
 defined and a unique solution exists. The function, the fixed values
 and initial values for the remaining unknowns are fed to a nonlinear backsolver.  
 The original version of "TK!Solver" , now a product of Universal Technical Systems (<https://www.uts.com>) was the inspiration for this function.
Kernel-based 'Tweedie' compound Poisson gamma model using high-dimensional predictors for the analyses of zero-inflated response variables. The package features built-in estimation, prediction and cross-validation tools and supports choice of different kernel functions. For more details, please see Yi Lian, Archer Yi Yang, Boxiang Wang, Peng Shi & Robert William Platt (2023) <doi:10.1080/00401706.2022.2156615>.
This function performs the two-sample Kuiper test to assess the anomaly of continuous, one-dimensional probability distributions. References used for this method are (1). Kuiper, N. H. (1960). <DOI:10.1016/S1385-7258(60)50006-0> and (2). Paltani, S. (2004). <DOI:10.1051/0004-6361:20034220>.
Computes measures of multivariate kurtosis, matrices of fourth-order moments and cumulants, kurtosis-based projection pursuit.
             Franceschini, C. and Loperfido, N. (2018, ISBN:978-3-319-73905-2). "An Algorithm for Finding Projections with Extreme Kurtosis".
             Loperfido, N. (2017,ISSN:0024-3795). "A New Kurtosis Matrix, with Statistical Applications".
Tools for data importation, recoding, and inspection.
    There are functions to create new project
    folders, R code templates, create uniquely named output
    directories, and to quickly obtain a visual summary for each
    variable in a data frame.  The main feature here is the systematic
    implementation of the "variable key" framework for data
    importation and recoding.  We are eager to have community feedback
    about the variable key and the vignette about it. In version 1.7,
    the function 'semTable' is removed. It was deprecated since 1.67.
    That is provided in a separate package, 'semTable'.
The format KVH is a lightweight format that can be read/written both by humans and machines.
   It can be useful in situations where XML or alike formats seem to be an overkill.
   We provide an ability to parse KVH files in R pretty fast due to 'Rcpp' use.
Calculation groundwater mounding beneath an
    infiltration basin based on the Hantush (1967) equation
    (<doi:10.1029/WR003i001p00227>). The correct implementation is shown
    with a verification example based on a USGS report (page 25,
    <https://pubs.usgs.gov/sir/2010/5102/support/sir2010-5102.pdf#page=35>).
Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments.
Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.
A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.
An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.
Highly optimized toolkit for approximately solving L0-regularized learning problems (a.k.a. best subset selection).
    The algorithms are based on coordinate descent and local combinatorial search.
    For more details, check the paper by Hazimeh and Mazumder (2020) <doi:10.1287/opre.2019.1919>.
Provides function for the l1-ball prior on high-dimensional regression. The main function, l1ball(), yields posterior samples for linear regression, as introduced by Xu and Duan (2020) <arXiv:2006.01340>.
LINCS L1000 is a high-throughput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution.
L1 estimation for linear regression using Barrodale and Roberts' method
    <doi:10.1145/355616.361024> and the EM algorithm <doi:10.1023/A:1020759012226>,
    density, distribution function, quantile function and random number generation
    for univariate and multivariate Laplace distribution <doi:10.1080/03610929808832115>.
Provides an l1-version of the spectral clustering algorithm devoted to robustly clustering highly perturbed graphs using l1-penalty. This algorithm is described with more details in the preprint C. Champion, M. Champion, M. Blazère, R. Burcelin and J.M. Loubes, "l1-spectral clustering algorithm: a spectral clustering method using l1-regularization" (2022).
Efficient implementation of Friedman's boosting algorithm with
    l2-loss function and coordinate direction (design matrix columns) basis
    functions.
Provides functions for the implementation of a density goodness-of-fit test, based on piecewise approximation of the L2 distance.
An implementation of a computational framework for performing robust structured regression with the L2 criterion 
             from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and 
             Lange (2022+) added in Version 2.0.
Provides a method for detecting multiple change points in high-dimensional time
    series, targeting dense or spatially clustered signals. See Li et al. (2023) 
    "L2 Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM". 
    arXiv preprint <arXiv:2208.13074>.
Contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <arXiv:2010.09154>.
Miscellaneous scripts, e.g. functionality to make and plot factor diagrams for the statistical design.
A variety of ordination and community analyses
   useful in analysis of data sets in community ecology.  
   Includes many of the common ordination methods, with 
   graphical routines to facilitate their interpretation, 
   as well as several novel analyses.
The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.
Functions which provide a range of axis labeling algorithms.
Work with labelled data imported from 'SPSS'
    or 'Stata' with 'haven' or 'foreign'. This package
    provides useful functions to deal with "haven_labelled" and
    "haven_labelled_spss" classes introduced by 'haven' package.
Assign meaningful labels to data frame columns.
    'labelmachine' manages your label assignment rules in 'yaml' files
    and makes it easy to use the same labels in multiple projects.
Labels are a common construct in statistical software providing a 
  human readable description of a variable. While variable names are succinct,
  quick to type, and follow a language's naming conventions, labels may 
  be more illustrative and may use plain text and spaces. R does not provide
  native support for labels. Some packages, however, have made this feature
  available.  Most notably, the 'Hmisc' package provides labelling methods
  for a number of different object. Due to design decisions, these methods
  are not all exported, and so are unavailable for use in package development.
  The 'labelVector' package supports labels for atomic vectors in a light-weight
  design that is suitable for use in other packages.
Imports a data frame containing a single time resolved laser ablation mass spectrometry analysis of a foraminifera (or other carbonate shell), then detects when the laser has burnt through the foraminifera test as a function of change in signal over time.
Provides functions for normalizing standard laboratory measurements (e.g. hemoglobin, cholesterol levels) according to age and sex, based on the algorithms described in "Personalized lab test models to quantify disease potentials in healthy individuals" (Netta Mendelson Cohen, Omer Schwartzman, Ram Jaschek, Aviezer Lifshitz, Michael Hoichman, Ran Balicer, Liran I. Shlush, Gabi Barbash & Amos Tanay, <doi:10.1038/s41591-021-01468-6>). Allows users to easily obtain normalized values for standard lab results, and to visualize their distributions. See more at <https://tanaylab.weizmann.ac.il/labs/>.
Produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography.
Allows the user to map multilingual free-text of occupations to a broad range
    of standardized classifications. The package facilitates automatic occupation coding
    (see, e.g., Gweon et al. (2017) <doi:10.1515/jos-2017-0006> and Turrell et al. (2019)
    <doi:10.3386/w25837>), where the ISCO to ESCO mapping is exploited to extend the
    occupations hierarchy, Le Vrang et al. (2014) <doi:10.1109/mc.2014.283>. Document
    vectorization is performed using the multilingual ESCO corpus. A method based on the 
    nearest neighbor search is used to suggest the closest ISCO occupation.
Dati, scripts e funzioni per il libro "Ricerca sociale con R. Concetti e funzioni base per la ricerca sociale" (Datasets, scripts and functions to support the book "Ricerca sociale con R. Concetti e funzioni base per la ricerca sociale").
Simplex optimization algorithms as firstly proposed by Spendley
    et al. (1962) <doi:10.1080/00401706.1962.10490033> and later modified by 
    Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308> for laboratory and 
    manufacturing processes. The package also provides tools for graphical 
    representation of the simplexes and some example response surfaces that 
    are useful in illustrating the optimization process.
Insieme di funzioni di supporto al volume "Laboratorio di
        Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006.
        This package contains sets of functions defined in "Laboratorio
        di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia,
        2006. Function names and docs are in italian as well.
Contains data sets to accompany the book: Lazic SE
    (2016). "Experimental Design for Laboratory Biologists: Maximising Information
    and Improving Reproducibility". Cambridge University Press.
Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2020) for details.
Connect to the 'Less Annoying CRM' API with ease to get your crm data in a clean and tidy format. 'Less Annoying CRM' is a simple CRM built for small businesses, more information is available on their website <https://www.lessannoyingcrm.com/>.
Set of tools for analyzing lactate thresholds from a step incremental test to exhaustion. Easily analyze
    the methods Log-log, Onset of Blood Lactate Accumulation (OBLA), Baseline plus (Bsln+), Dmax, Lactate Turning Point (LTP),
    and Lactate / Intensity ratio (LTratio) in cycling, running, or swimming.
    Beaver WL, Wasserman K, Whipp BJ (1985) <doi:10.1152/jappl.1985.59.6.1936>.
    Heck H, Mader A, Hess G, Mücke S, Müller R, Hollmann W (1985) <doi:10.1055/s-2008-1025824>.
    Kindermann W, Simon G, Keul J (1979) <doi:10.1007/BF00421101>.
    Skinner JS, Mclellan TH (1980) <doi:10.1080/02701367.1980.10609285>.
    Berg A, Jakob E, Lehmann M, Dickhuth HH, Huber G, Keul J (1990) <PMID:2408033>.
    Zoladz JA, Rademaker AC, Sargeant AJ (1995) <doi:10.1113/jphysiol.1995.sp020959>.
    Cheng B, Kuipers H, Snyder A, Keizer H, Jeukendrup A, Hesselink M (1992) <doi:10.1055/s-2007-1021309>.
    Bishop D, Jenkins DG, Mackinnon LT (1998) <doi:10.1097/00005768-199808000-00014>.
    Hughson RL, Weisiger KH, Swanson GD (1987) <doi:10.1152/jappl.1987.62.5.1975>.
    Jamnick NA, Botella J, Pyne DB, Bishop DJ (2018) <doi:10.1371/journal.pone.0199794>.
    Hofmann P, Tschakert G (2017) <doi:10.3389/fphys.2017.00337>.
    Hofmann P, Pokan R, von Duvillard SP, Seibert FJ, Zweiker R, Schmid P (1997) <doi:10.1097/00005768-199706000-00005>.
    Pokan R, Hofmann P, Von Duvillard SP, et al. (1997) <doi:10.1097/00005768-199708000-00009>.
    Dickhuth H-H, Yin L, Niess A, et al. (1999) <doi:10.1055/s-2007-971105>.
Functions for estimating the gliding box lacunarity (GBL),
    covariance, and pair-correlation of a random closed set (RACS) in 2D
    from a binary coverage map (e.g. presence-absence land cover maps).
    Contains a number of newly-developed covariance-based estimators of
    GBL (Hingee et al., 2019) <doi:10.1007/s13253-019-00351-9> and
    balanced estimators, proposed by Picka (2000)
    <http://www.jstor.org/stable/1428408>, for covariance, centred
    covariance, and pair-correlation.  Also contains methods for
    estimating contagion-like properties of RACS and simulating 2D Boolean
    models.  Binary coverage maps are usually represented as raster images
    with pixel values of TRUE, FALSE or NA, with NA representing
    unobserved pixels.  A demo for extracting such a binary map from a
    geospatial data format is provided.  Binary maps may also be
    represented using polygonal sets as the foreground, however for most
    computations such maps are converted into raster images.  The package
    is based on research conducted during the author's PhD studies.
Calculate mean statistics and leaf angle distribution type from measured leaf inclination angles. LAD distribution is fitted using a two-parameters (mu, nu) Beta distribution and compared with six theoretical LAD distributions. Additional information is provided in Chianucci and Cesaretti (2022) <doi:10.1101/2022.10.28.513998>.
Estimation of indicators on social exclusion and poverty, as well
    as Pareto tail modeling for empirical income distributions.
Methods for fast access to large ASCII files.  Currently the
    following file formats are supported: comma separated format (CSV) and fixed
    width format. It is assumed that the files are too large to fit into memory,
    although the package can also be used to efficiently access files that do
    fit into memory. Methods are provided to access and process files blockwise.
    Furthermore, an opened file can be accessed as one would an ordinary
    data.frame. The LaF vignette gives an overview of the functionality
    provided.
Provides classes and methods for objects, whose indexing
      naturally starts from zero. Subsetting, indexing and mathematical
      operations are defined naturally between lagged objects and lagged
      and base R objects. Recycling is not used, except for singletons.
      The single bracket operator doesn't drop dimensions by default.
Client for programmatic access to the Lake
    Multi-scaled Geospatial and Temporal database <https://lagoslakes.org>, with functions
    for accessing lake water quality and ecological context data for the US.
Performs approximate GP regression for large computer experiments and spatial datasets.  The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine.  OpenMP and GPU features may require special compilation.  An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided.  For details and tutorial, see Gramacy (2016 <doi:10.18637/jss.v072.i01>.
Model-based linear model trees adjusting for spatial correlation using a
             simultaneous autoregressive spatial lag, Wagner and Zeileis (2019)
	     <doi:10.1111/geer.12146>.
Lag-sequential analysis is a method of assessing of patterns (what tends to 
        follow what?) in sequences of codes. The codes are typically for discrete 
        behaviors or states. The functions in this package read a stream of codes,
        or a frequency transition matrix, and produce a variety of lag sequential  
        statistics, including transitional frequencies, expected transitional frequencies, 
        transitional probabilities, z values, adjusted residuals, Yule's Q values, 
        likelihood ratio tests of stationarity across time and homogeneity across groups 
        or segments, transformed kappas for unidirectional dependence, bidirectional 
        dependence, parallel and nonparallel dominance, and significance levels based on 
        both parametric and randomization tests. The methods are described in
  Bakeman & Quera (2011) <doi:10.1017/CBO9781139017343>,
  O'Connor (1999) <doi:10.3758/BF03200753>,
  Wampold & Margolin (1982) <doi:10.1037/0033-2909.92.3.755>, and
  Wampold (1995, ISBN:0-89391-919-5).
Provides the tables from the 'Sean Lahman Baseball Database' as
    a set of R data.frames. It uses the data on pitching, hitting and fielding
    performance and other tables from 1871 through 2022, as recorded in the 2023
    version of the database. Documentation examples show how many baseball
    questions can be investigated.
A collection of tools for the calculation of freewater metabolism
    from in situ time series of dissolved oxygen, water temperature, and,
    optionally, additional environmental variables. LakeMetabolizer implements
    5 different metabolism models with diverse statistical underpinnings:
    bookkeeping, ordinary least squares, maximum likelihood, Kalman filter,
    and Bayesian. Each of these 5 metabolism models can be combined with
    1 of 7 models for computing the coefficient of gas exchange across the
    air–water interface (k). LakeMetabolizer also features a variety of
    supporting functions that compute conversions and implement calculations
    commonly applied to raw data prior to estimating metabolism (e.g., oxygen
    saturation and optical conversion models).
Lake morphometry metrics are used by limnologists to understand,
    among other things, the ecological processes in a lake. Traditionally, these
    metrics are calculated by hand, with planimeters, and increasingly with
    commercial GIS products. All of these methods work; however, they are either
    outdated, difficult to reproduce, or require expensive licenses to use. The
    'lakemorpho' package provides the tools to calculate a typical suite
    of these metrics from an input elevation model and lake polygon. The metrics
    currently supported are: fetch, major axis, minor axis, major/minor axis 
    ratio, maximum length, maximum width, mean width, maximum depth, mean depth, 
    shoreline development, shoreline length, surface area, and volume.
Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution.
Includes some procedures for latent variable modeling with a 
    particular focus on multilevel data.
    The 'LAM' package contains mean and covariance structure modelling
    for multivariate normally distributed data (mlnormal(); Longford, 1987;
    <doi:10.1093/biomet/74.4.817>), a general Metropolis-Hastings algorithm 
    (amh(); Roberts & Rosenthal, 2001, <doi:10.1214/ss/1015346320>) and 
    penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 
    2014; <doi:10.1093/aje/kwt245>).
A language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety.
Time series analysis based on lambda transformer and variational seq2seq, built on 'Torch'.
Runtime for serving containers that can execute R code on the 
    'AWS Lambda' serverless compute service <https://aws.amazon.com/lambda/>.
    Provides the necessary functionality for handling the various endpoints
    required for accepting new input and sending responses.
Lambert W x F distributions are a generalized framework to analyze
    skewed, heavy-tailed data. It is based on an input/output system, where the
    output random variable (RV) Y is a non-linearly transformed version of an input
    RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed).
    The transformed RV Y has a Lambert W x F distribution. This package contains
    functions to model and analyze skewed, heavy-tailed data the Lambert Way:
    simulate random samples, estimate parameters, compute quantiles, and plot/
    print results nicely. The most useful function is 'Gaussianize',
    which works similarly to 'scale', but actually makes the data Gaussian.
    A do-it-yourself toolkit allows users to define their own Lambert W x
    'MyFavoriteDistribution' and use it in their analysis right away.
Approximate marginal maximum likelihood estimation of multidimensional
	latent variable models via adaptive quadrature or Laplace approximations to the 
	integrals in the likelihood function, as presented for confirmatory factor 
	analysis models in Jin, S., Noh, M., and Lee, Y. (2018) 
	<doi:10.1080/10705511.2017.1403287>, for item response theory models in 
	Andersson, B., and Xin, T. (2021) <doi:10.3102/1076998620945199>, and for 
	generalized linear latent variable models in Andersson, B., Jin, S., and 
	Zhang, M. (2023) <doi:10.1016/j.csda.2023.107710>. Models implemented include
	the generalized	partial credit model, the graded response model, and generalized 
	linear latent variable models for Poisson, negative-binomial and normal 
	distributions. Supports a combination of binary, ordinal, count and continuous 
	observed variables and multiple	group models.
Log-analytic methods intended for testing multiplicative effects.
Implements both real-valued branches of the Lambert-W function
    (Corless et al, 1996) <doi:10.1007/BF02124750> without the need for
    installing the entire GSL.
Provides functions to estimate survival and a treatment effect using a landmark estimation approach.
Provides a function for classifying a landscape into different categories based on the Topographic Position Index (TPI) and slope. It offers two types of classifications: Slope Position Classification, and Landform Classification. The function internally calculates the TPI for the given landscape and then uses it along with the slope to perform the classification. Optionally, descriptive statistics for every class are calculated and plotted. The classifications are useful for identifying the position of a location on a slope and for identifying broader landform types.
The landmark approach allows survival predictions to be
	updated dynamically as new measurements from an individual are recorded.
	The idea is to set predefined time points, known as "landmark times",
	and form a model at each landmark time using only the individuals in the
	risk set. This package allows the longitudinal data to be modelled
	either using the last observation carried forward or linear mixed
	effects modelling. There is also the option to model competing risks,
	either through cause-specific Cox regression or Fine-Gray regression.
	To find out more about the methods in this package, please see 
	<https://isobelbarrott.github.io/Landmarking/articles/Landmarking>.
Non-parametric prediction of survival outcomes for mixture data that incorporates covariates and a landmark time. Details are described in Garcia (2021) <doi:10.1093/biostatistics/kxz052>.
Provides functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307, <doi:10.1002/bimj.201000150>.
Processing of Landsat or other multispectral satellite imagery. Includes relative normalization, image-based radiometric correction, and topographic correction options. The original package description was published as Goslee (2011) <doi:10.18637/jss.v043.i04>, and details of the topographic corrections in Goslee (2012) <doi:10.14358/PERS.78.9.973>.
Functions for converted Landsat 8 multispectral satellite imagery
    rescaled to the top of atmosphere (TOA) reflectance, radiance
    and/or at satellite brightness temperature using radiometric
    rescaling coefficients provided in the metadata file (MTL file).
Calculates landscape metrics for categorical landscape patterns in 
    a tidy workflow. 'landscapemetrics' reimplements the most common metrics from
    'FRAGSTATS' (<https://www.umass.edu/landeco/>) 
    and new ones from the current literature on landscape metrics.
    This package supports 'raster' spatial objects and takes 
    RasterLayer, RasterStacks, RasterBricks or lists of RasterLayer from the
    'raster' package as input arguments. It further provides utility functions
    to visualize patches, select metrics and building blocks to develop new 
    metrics.
Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format.
Provides utility functions for some of the less-glamorous tasks involved
    in landscape analysis. It includes functions to coerce raster data to the
    common tibble format and vice versa, it helps with flexible reclassification
    tasks of raster data and it provides a function to merge multiple raster.
    Furthermore, 'landscapetools' helps landscape scientists to visualize their
    data by providing optional themes and utility functions to plot single
    landscapes, rasterstacks, -bricks and lists of raster.
A stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution 
    of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. 
    It is based on a spatial geometry for describing the landscape and allocation of different cultivars, 
    a dispersal kernel for the dissemination of the pathogen, and a SEIR 
    ('Susceptible-Exposed-Infectious-Removed’) structure with a discrete time step.
    It provides a useful tool to assess the performance of a wide range of deployment options with 
    respect to their epidemiological, evolutionary and economic outcomes.
    Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, 
    Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.
Estimate drift and diffusion functions from time series and
    generate synthetic time series from given drift and diffusion coefficients.
An HTML widget that randomly tours 2D projections of numerical data. A random walk through projections of the data is shown. The user can manipulate the plot to use specified axes, or turn on Guided Tour mode to find an informative projection of the data. Groups within the data can be hidden or shown, as can particular axes. Known projections of interest can be added as "extra axes" and also manipulated. The underlying method to produce the random walk and projection pursuit uses Langevin dynamics. The widget can be used from within R, or included in a self-contained Rmarkdown document, or used in a Shiny app.
Improve your text analysis with languagelayer
        <https://languagelayer.com>, a powerful language detection
        API.
Data sets exemplifying statistical methods, and some
        facilitatory utility functions used in ``Analyzing Linguistic
        Data: A practical introduction to statistics using R'',
        Cambridge University Press, 2008.
An implementation of the Language Server Protocol
    for R. The Language Server protocol is used by an editor client to
    integrate features like auto completion. See
    <https://microsoft.github.io/language-server-protocol/> for details.
Allows to install the R 'languageserver' with all dependencies
    into a separate library and use that independent installation
    automatically when R is instantiated as a language server process.
    Useful for making language server seamless to use without
    running into package version conflicts.
R interface for working with nanometer scale secondary ion mass 
    spectrometry (NanoSIMS) data exported from Look at NanoSIMS.
Solves the problem of Laplace deconvolution with noisy discrete
    non-equally spaced observations on a finite time interval based on expansions
    of the convolution kernel, the unknown function and the observed signal over
    Laguerre functions basis. It implements the methodology proposed in the paper
    "Laplace deconvolution on the basis of time domain data and its application to
    Dynamic Contrast Enhanced imaging" by F. Comte, C-A. Cuenod, M. Pensky and Y.
    Rozenholc in ArXiv (http://arxiv.org/abs/1405.7107).
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).
This package is intended for researchers studying historical labour relations (see http://www.historyoflabourrelations.org). The package allows for easy access of excel files in the standard defined by the Global Collaboratory on the History of Labour Relations. The package also allows for visualisation of labour relations according to the Collaboratory's format.
Auxiliary package for better/faster analytics, visualization, data mining, and machine
    learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling,
    Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust
    results, without the need of repetitive coding or extensive R programming skills.
Useful 'shiny' production-ready modules and helpers such as login window and visualization tools.
Provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes.
Compute lifetime attributable risk of 
    radiation-induced cancer reveals that it can be helpful with 
    enhancement of the flexibility in research with fast calculation 
    and various options. Important reference papers include
    Berrington de Gonzalez et al. (2012) <doi:10.1088/0952-4746/32/3/205>,
    National Research Council (2006, ISBN:978-0-309-09156-5).
Efficient procedures for fitting an entire lasso
		sequence with the cost of a single least squares
		fit. Least angle regression and infinitesimal forward
		stagewise regression are related to the lasso, as
		described in the paper below.
Implementation of the algorithm introduced in Shah, R. D.
    (2016) <https://www.jmlr.org/papers/volume17/13-515/13-515.pdf>.
    Data with thousands of predictors can be handled. The algorithm
    performs sequential Lasso fits on design matrices containing
    increasing sets of candidate interactions. Previous fits are used to greatly
    speed up subsequent fits, so the algorithm is very efficient.
Fits generalized estimating equations with L1 regularization to longitudinal data with high dimensional covariates. Use a efficient iterative composite gradient descent algorithm.
Contains functions to estimate a penalized regression model using 3CoSE algorithm, see Weber, Striaukas, Schumacher Binder (2018) <doi:10.2139/ssrn.3211163>.
Estimate the p-values for predictors x against target variable y in lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic. This is based on the assumption that predictors (of the same variance) that (first) become active earlier tend to be more significant. Three null distributions are supported: normal and spherical, which are computed separately for each predictor and analytically under approximation, which aims at efficiency and accuracy for small p-values.
L1 regularized regression (Lasso) solver using the Cyclic
        Coordinate Descent algorithm aka Lasso Shooting is fast. This
        implementation can choose which coefficients to penalize. It
        support coefficient-specific penalties and it can take X'X and
        X'y instead of X and y.
Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2017) <arxiv:1611.06655>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.
Various opportunities to evaluate the effects of including one or more control variable(s) in structural equation models onto model-implied variances, covariances, and parameter estimates. The derivation of the methodology employed in this package can be obtained from Blötner (2023) <doi:10.31234/osf.io/dy79z>.
A graph
  proposed by Rosenbaum is useful
  for checking some properties of various
  sorts of latent scale, this program generates commands
  to obtain the graph using 'dot' from 'graphviz'.
Estimating causal parameters in the presence of treatment spillover is of great interest in statistics.
    This package provides tools for instrumental variables estimation of average causal effects under network interference of unknown form.
    The target parameters are the local average direct effect, the local average indirect effect, the local average overall effect, and the local average spillover effect.
    The methods are developed by Hoshino and Yanagi (2023) <doi:10.48550/arXiv.2108.07455>.
The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated),
             comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation.
             The estimation is based on latent copula Gaussian models.
             For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017).
             For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <arXiv:1809.06255>.
             For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>.
             For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.
Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.
Three methods are provided to estimate graphical models with latent variables: (1) Jin, Y., Ning, Y., and Tan, K. M. (2020) (preprint available); (2) Chandrasekaran, V., Parrilo, P. A. & Willsky, A. S. (2012) <doi:10.1214/11-AOS949>; (3) Tan, K. M., Ning, Y., Witten, D. M. & Liu, H. (2016) <doi:10.1093/biomet/asw050>.
Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) <doi:10.18637/jss.v024.i05> and Krivitsky, Handcock, Raftery, and Hoff (2009) <doi:10.1016/j.socnet.2009.04.001>.
Executes arbitrary R or C functions some time after the current
    time, after the R execution stack has emptied. The functions are scheduled
    in an event loop.
Calculates and plots Handedness index (HI), absolute HI, mean HI and z-score which are commonly used indexes for the study of hand preference (laterality) in non-human primates.
Parses and converts LaTeX math formulas to R's plotmath
    expressions, used to enter mathematical formulas and symbols to be rendered as
    text, axis labels, etc. throughout R's plotting system.
Produces a PDF diff of two 'rmarkdown', Sweave or TeX files,  using
    the external 'latexdiff' utility.
Converts table-like objects to stand-alone PDF or PNG.
 Can be used to embed tables and arbitrary content in PDF or Word
 documents. Provides a low-level R interface for creating 'LaTeX'
 code, e.g. command() and a high-level interface for creating PDF
 documents, e.g. as.pdf.data.frame(). Extensive customization is
 available via mid-level functions, e.g. as.tabular(). See also 
 'package?latexpdf'. Support for PNG is experimental; see
 'as.png.data.frame'. Adapted from 'metrumrg' 
 <https://r-forge.r-project.org/R/?group_id=1215>.
 Requires a compatible installation of 'pdflatex',
 e.g. <https://miktex.org/>.
A framework for clustering longitudinal datasets in a standardized way. 
    The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. 
    Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. 
    The interface enables users to compare results between methods, and to implement and evaluate new methods with ease.
    The 'akmedoids' package is available from <https://github.com/MAnalytics/akmedoids>. The 'clusterCrit' package is available from the CRAN archive.
Back-end connections to 'LattE' (<https://www.math.ucdavis.edu/~latte>) 
	for counting lattice points and integration inside convex polytopes and 
	'4ti2' (<http://www.4ti2.de/>) for algebraic, geometric, and combinatorial 
	problems on linear spaces and front-end tools facilitating their use in the 
	'R' ecosystem.
A powerful and elegant high-level data visualization
  system inspired by Trellis graphics, with an emphasis on
  multivariate data. Lattice is sufficient for typical graphics needs,
  and is also flexible enough to handle most nonstandard requirements.
  See ?Lattice for an introduction.
Functions that compute the
  lattice-based density estimator of Barry and McIntyre, which
  accounts for point processes in two-dimensional regions with 
  irregular boundaries and holes.  The package also implements
  two-dimensional non-parametric regression for similar regions.
Lattice-based space-filling designs with fill or separation distance properties including
  interleaved lattice-based minimax distance designs proposed in Xu He (2017)
  <doi:10.1093/biomet/asx036>, interleaved lattice-based maximin distance designs proposed
  in Xu He (2018) <doi:10.1093/biomet/asy069>, (sliced) rotated sphere packing designs 
  proposed in Xu He (2017) <doi:10.1080/01621459.2016.1222289> and Xu He (2019) 
  <doi:10.1080/00401706.2018.1458655>, and densest packing-based maximum projections 
  designs proposed in Xu He (2020) <doi:10.1093/biomet/asaa057> and Xu He (2018) 
  <arXiv:1709.02062v2>.
Building on the infrastructure provided by the lattice
	     package, this package provides several new high-level
	     functions and methods, as well as additional utilities
	     such as panel and axis annotation functions.
A general implementation of Structural Equation Models
	with latent variables (MLE, 2SLS, and composite likelihood
	estimators) with both continuous, censored, and ordinal
	outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>).
	Mixture latent variable models and non-linear latent variable models
	(Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>).
	The package also provides methods for graph exploration (d-separation,
	back-door criterion), simulation of general non-linear latent variable
	models, and estimation of influence functions for a broad range of
	statistical models.
Fit a variety of latent variable models, including confirmatory
   factor analysis, structural equation modeling and latent growth curve models.
Interactive shiny application for working with different kinds of
    latent variable analysis, with the 'lavaan' package. Graphical output for models
    are provided and different estimators are supported.
Affords an alternative, vector-based syntax to 'lavaan', as well as other 
             convenience functions such as naming paths and defining indirect
             links automatically, in addition to convenience formatting optimized
             for a publication and script sharing workflow.
Plots path diagrams from models in 'lavaan' using the plotting
    functionality from the 'DiagrammeR' package. 'DiagrammeR' provides nice path diagrams 
    via 'Graphviz', and these functions make it easy to generate these diagrams from a
    'lavaan' path model without having to write the DOT language graph specification.
Estimation of a multi-group count regression models (i.e., Poisson, 
    negative binomial) with latent covariates. This packages provides two extensions
    compared to ordinary count regression models based on a generalized linear model:
    First, measurement models for the predictors can be specified allowing to account 
    for measurement error. Second, the count regression can be simultaneously estimated 
    in multiple groups with stochastic group weights. The marginal maximum likelihood 
    estimation is described in Kiefer & Mayer (2020) <doi:10.1080/00273171.2020.1751027>.
The lava estimation is used to recover signals that is the sum of a sparse signal and a dense signal. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) <doi:10.1214/16-AOS1434>.
Tools for model specification in the latent variable framework
    (add-on to the 'lava' package). The package contains three main functionalities:
    Wald tests/F-tests with improved control of the type 1 error in small samples,
    adjustment for multiple comparisons when searching for local dependencies,
    and adjustment for multiple comparisons when doing inference for multiple latent variable models.
The lava estimation is a new technique to recover signals that is the sum of a sparse and dense signals. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) <doi:10.1214/16-AOS1434>.
A variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>; Bayesian regularized EFA <DOI: 10.1080/10705511.2020.1854763>; Fully and partially EFA.
Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, and measures of relative variability, the package contains new robust tests of symmetry, omnibus and directional tests of normality, and their graphical counterparts such as robust QQ plot, robust trend tests for variances, etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal statistics, economics, and biostatistics.
Performs adjusted inferences based on model objects fitted, using 
    maximum likelihood estimation, by the extreme value analysis packages
    'eva' <https://cran.r-project.org/package=eva>, 
    'evd' <https://cran.r-project.org/package=evd>, 
    'evir' <https://cran.r-project.org/package=evir>, 
    'extRemes' <https://cran.r-project.org/package=extRemes>, 
    'fExtremes' <https://cran.r-project.org/package=fExtremes>, 
    'ismev' <https://cran.r-project.org/package=ismev>, 
    'mev' <https://cran.r-project.org/package=mev>, 
    'POT' <https://cran.r-project.org/package=POT> and
    'texmex' <https://cran.r-project.org/package=texmex>. 
    Adjusted standard errors and an adjusted loglikelihood are provided, using    
    the 'chandwich' package <https://cran.r-project.org/package=chandwich>
    and the object-oriented features of the 'sandwich' package 
    <https://cran.r-project.org/package=sandwich>. The adjustment is based on a 
    robust sandwich estimator of the parameter covariance matrix, based on the 
    methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This 
    can be used for cluster correlated data when interest lies in the 
    parameters of the marginal distributions, or for performing inferences that 
    are robust to certain types of model misspecification.  Univariate extreme 
    value models, including regression models, are supported.
By combining constant, linear, and quadratic local models,
        lazy estimates the value of an unknown multivariate function on
        the basis of a set of possibly noisy samples of the function
        itself.  This implementation of lazy learning automatically
        adjusts the bandwidth on a query-by-query basis through a
        leave-one-out cross-validation.
Multi-threaded serialization of compressed array that 
    fully utilizes modern solid state drives. It allows 
    to store and load extremely large data on demand within seconds
    without occupying too much memories. With data stored on hard drive, 
    a lazy-array data can be loaded, shared across multiple R sessions.
    For arrays with partition mode on, multiple R sessions can write to 
    a same array simultaneously along the last dimension (partition). 
    The internal storage format is provided by 'fstcore' package geared by 
    'LZ4' and 'ZSTD' compressors.
A simple progress bar showing estimated remaining time. 
    Multiple forecast methods and user defined forecast method for 
    the remaining time are supported.
Supplies a LazyData facility for packages which have data
		sets but do not provide LazyData: true.  A single function is
		is included, requireData, which is a drop-in replacement for
		base::require, but carrying the additional
		functionality. By default, it suppresses package
		startup messages as well.  See argument 'reallyQuitely'.
An alternative approach to non-standard evaluation using
    formulas. Provides a full implementation of LISP style 'quasiquotation',
    making it easier to generate code with other code.
Lazy read for drawings. A 'dplyr' back end for data sources supported by 
    'GDAL' vector drivers, that allows working with local or remote sources as if they 
    are in-memory data frames. Basic features works with any drawing format ('GDAL vector 
    data source') supported by the 'sf' package.
Helper functions to build SQL statements
    for dbGetQuery or dbSendQuery under program control.
    They are intended to increase speed of coding and
    to reduce coding errors. Arguments are carefully checked,
    in particular SQL identifiers such as names of tables or columns.
    More patterns will be added as required.
Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading.
    Main goal is to process information within "Decision Support System" to come up with analysis or predictions.
    There are several utilities such as dynamic and adaptive risk management using reinforcement learning
    and even functions to generate predictions of price changes using pattern recognition deep regression learning.
    Summary of Methods used: Awesome H2O tutorials: <https://github.com/h2oai/awesome-h2o>, 
    Market Type research of Van Tharp Institute: <https://www.vantharp.com/>,
    Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.
Provides the functionality to write LaTeX code from within R
    without having to learn LaTeX.  Functionality also exists to create HTML
    and Markdown code.  While the functionality still exists to write
    complete documents with lazyWeave, it is generally easier to do so with
    with markdown and knitr.  lazyWeave's main strength now is the ability
    to design custom and complex tables for reporting results.
Latent budget analysis is a method for the analysis of a two-way
    contingency table with an exploratory variable and a response variable. It is
    specially designed for compositional data.
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.
Interfacing to Nocedal et al. L-BFGS-B.3.0 
        (See <http://users.iems.northwestern.edu/~nocedal/lbfgsb.html>)
        limited memory BFGS minimizer with bounds on parameters. 
        This is a fork of 'lbfgsb3'. 
	This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same 
	function types and optimization as the optim() function (see writing 'R' extensions
        and source for details).  This package also adds more stopping criteria as well 
        as allowing the adjustment of more tolerances.
Fit the log binomial regression model (LBM) by Exact method. Limited parameter space of 
    LBM causes trouble to find admissible estimates and fail to converge when MLE is close to or on 
    the boundary of space. Exact method utilizes the property of boundary vectors to re-parametrize 
    the model without losing any information, and fits the model on the standard fitting algorithm 
    with no convergence issues.
Implements a logistic box-cox model. This model is fully described in Xing, L. et al. (2021) <doi:10.1002/cjs.11587>.
The Length-Biased Power Garima distribution for computes the probability density,
    the cumulative density distribution and the quantile function of the distribution, 
    and generates sample values with random variables based on Kittipong and Sirinapa(2021)<DOI: 10.14456/sjst-psu.2021.89>.
Maximum likelihood estimation of log-binomial regression with special functionality when the MLE is on the boundary of the parameter space.
Simulate expected equilibrium length composition, yield-per-recruit, and
    the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fit the LBSPR
    model to length data to estimate  selectivity, relative apical fishing mortality, and
    the spawning potential ratio for data-limited fisheries.
    See Hordyk et al (2016) <doi:10.1139/cjfas-2015-0422> for more information about the
    LBSPR assessment method.
Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.
Latent Class Analysis of
        phenotypic measurements in pedigrees and model selection
        based on one of two methods: likelihood-based cross-validation
        and Bayesian Information Criterion. Computation of individual
        and triplet child-parents weights in a pedigree is performed using an
        upward-downward algorithm. The model takes into account the familial
        dependence defined by the pedigree structure by considering
        that a class of a child depends on his parents classes via
        triplet-transition probabilities of the classes. The package
        handles the case where measurements are available on all
        subjects and the case where measurements are available only on
        symptomatic (i.e. affected) subjects. Distributions for
        discrete (or ordinal) and continuous data are currently
        implemented. The package can deal with missing data.
Provides Shiny widgets and theme that support a 'Library Computer Access/Retrieval System' (LCARS) aesthetic for Shiny apps. 
    The package also includes functions for adding a minimal LCARS theme to static 'ggplot2' graphs. 
    More details about LCARS can be found at <https://en.wikipedia.org/wiki/LCARS>.
Variable selection for latent class analysis for model-based clustering of multivariate categorical data. The package implements a general framework for selecting the subset of variables with relevant clustering information and discard those that are redundant and/or not informative. The variable selection method is based on the approach of Fop et al. (2017) <doi:10.1214/17-AOAS1061> and Dean and Raftery (2010) <doi:10.1007/s10463-009-0258-9>. Different algorithms are available to perform the selection: stepwise, swap-stepwise and evolutionary stochastic search. Concomitant covariates used to predict the class membership probabilities can also be included in the latent class analysis model. The selection procedure can be run in parallel on multiple cores machines.
Estimates the longitudinal concordance correlation to access the longitudinal agreement profile. The estimation approach implemented is variance components approach based on polynomial mixed effects regression model, as proposed by Oliveira, Hinde and Zocchi (2018) <doi:10.1007/s13253-018-0321-1>.  In addition, non-parametric confidence intervals were implemented using percentile method or normal-approximation based on Fisher Z-transformation.
Estimation of latent class models with individual covariates for capture-recapture data. See Forcina and Bartolucci (2021)<arxiv:2106.03811>.
Providing a method for Local Discrimination via Latent Class Models. The approach is described in <https://www.r-project.org/conferences/useR-2009/abstracts/pdf/Bucker.pdf>.
Baseline correction, normalization and linear combination fitting (LCF) 
    of X-ray absorption near edge structure (XANES) spectra.
    The package includes data loading of .xmu files exported from 'ATHENA' (Ravel and Newville, 2005) <doi:10.1107/S0909049505012719>. 
    Loaded spectra can be background corrected and all standards can be fitted at once.
    Two linear combination fitting functions can be used:
    (1) fit_athena(): Simply fitting combinations of standards as in ATHENA, 
    (2) fit_float(): Fitting all standards with changing baseline correction and edge-step normalization parameters.
This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model.
A joint latent class model where a hierarchical structure exists, with an interaction between female and male partners of a couple. A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. <doi:10.1111/biom.12972>.
Bayesian population size estimation using non parametric latent-class models.
Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).
The goal of 'LCMSQA' is to make it easy to check the quality of
             liquid chromatograph/mass spectrometry (LC/MS) experiments using a
             'shiny' application. This package provides interactive data
             visualizations for quality control (QC) samples, including total
             ion current chromatogram (TIC), base peak chromatogram (BPC), mass
             spectrum, extracted ion chromatogram (XIC), and feature detection
             results from internal standards or known metabolites.
Provides functions to upload vectorial data and derive landscape
    connectivity metrics in habitat or matrix systems. Additionally, includes an 
    approach to assess individual patch contribution to the overall landscape 
    connectivity, enabling the prioritization of habitat patches. The computation
    of landscape connectivity and patch importance are very useful in Landscape 
    Ecology research. The metrics available are: number of components, number of 
    links, size of the largest component, mean size of components, class coincidence
    probability, landscape coincidence probability, characteristic path length, 
    expected cluster size, area-weighted flux and integral index of connectivity.
    Pascual-Hortal, L., and Saura, S. (2006) <doi:10.1007/s10980-006-0013-z>
    Urban, D., and Keitt, T. (2001) <doi:10.2307/2679983>
    Laita, A., Kotiaho, J., Monkkonen, M. (2011) <doi:10.1007/s10980-011-9620-4>.
Collections of functions allowing random number generations and
    estimation of 'Liouville' copulas, as described in Belzile and Neslehova (2017) <doi:10.1016/j.jmva.2017.05.008>.
An implementation of the Log Cumulative Probability Model (LCPM) 
  and Proportional Probability Model (PPM) for which the Maximum Likelihood Estimates are determined using constrained optimization. 
  This implementation accounts for the implicit constraints on the parameter space. Other 
  features such as standard errors, z tests and p-values use standard methods adapted from the results based on constrained optimization.
For fitting Bayesian joint latent class and regression models using
    Gibbs sampling. See the documentation for the model.
    The technical details of the model implemented here are described in Elliott,
    Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham,
    Belinda L., "Methods to account for uncertainty in latent class assignments when
    using latent classes as predictors in regression models, with application to
    acculturation strategy measures" (2020) In press at Epidemiology
    <doi:10.1097/EDE.0000000000001139>.
Helper functions to implement univariate and bivariate latent change score models in R using the 'lavaan' package.
  For details about Latent Change Score Modeling (LCSM) see McArdle (2009) <doi:10.1146/annurev.psych.60.110707.163612> and Grimm, An, McArdle, Zonderman and Resnick (2012) <doi:10.1080/10705511.2012.659627>.
  The package automatically generates 'lavaan' syntax for different model specifications and varying timepoints.
  The 'lavaan' syntax generated by this package can be returned and further specifications can be added manually.
  Longitudinal plots as well as simplified path diagrams can be created to visualise data and model specifications.
  Estimated model parameters and fit statistics can be extracted as data frames.
  Data for different univariate and bivariate LCSM can be simulated by specifying estimates for model parameters to explore their effects.
  This package combines the strengths of other R packages like 'lavaan', 'broom', and 'semPlot' by generating 'lavaan' syntax that helps these packages work together.
Provides researchers and educators with easy-to-learn user friendly tools for calculating 
    key spatial statistics and to apply simple as well as advanced methods of spatial analysis in real data. 
    These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients, 
    Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation 
    (Global and Local Moran's I), several Geographically Weighted Regression techniques and other 
    Spatial Analysis tools (other geographically weighted statistics). This package also contains functions for 
    measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.
Analysis of stock data ups and downs trend, the stock technical analysis indicators function have trend line, reversal pattern and market trend.
Implements latent Dirichlet allocation (LDA)
	     and related models.  This includes (but is not limited
	     to) sLDA, corrLDA, and the mixed-membership stochastic
	     blockmodel.  Inference for all of these models is
	     implemented via a fast collapsed Gibbs sampler written
	     in C.  Utility functions for reading/writing data
	     typically used in topic models, as well as tools for
	     examining posterior distributions are also included.
Computation of linkage disequilibrium of ancestry (LDA) and linkage disequilibrium of ancestry score (LDAS). LDA calculates the pairwise linkage disequilibrium of ancestry between single nucleotide polymorphisms (SNPs). LDAS calculates the LDA score of SNPs. The methods are described in Barrie W, Yang Y, Attfield K E, et al (2022) <doi:10.1101/2022.09.23.509097>.
Contains the development of a tool that provides a web-based graphical
             user interface (GUI) to perform Biplots representations from a scraping of
             news from digital newspapers under the Bayesian approach of Latent Dirichlet 
             Assignment (LDA) and machine learning algorithms. Contains LDA methods 
             described by Blei , David M., Andrew Y. Ng and Michael I. Jordan (2003) 
             <https://jmlr.org/papers/volume3/blei03a/blei03a.pdf>,
             and Biplot methods described by Gabriel K.R(1971) <doi:10.1093/biomet/58.3.453>
             and Galindo-Villardon P(1986) <https://diarium.usal.es/pgalindo/files/2012/07/Questiio.pdf>.
Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search.
Determine a Prototype from a number of runs of Latent Dirichlet Allocation (LDA) measuring its similarities with S-CLOP: A procedure to select the LDA run with highest mean pairwise similarity, which is measured by S-CLOP (Similarity of multiple sets by Clustering with Local Pruning), to all other runs. LDA runs are specified by its assignments leading to estimators for distribution parameters. Repeated runs lead to different results, which we encounter by choosing the most representative LDA run as prototype.
Contains the development of a tool that provides a
    web-based graphical user interface (GUI) to perform a review of the
    scientific literature under the Bayesian approach of Latent Dirichlet
    Allocation (LDA)and machine learning algorithms. The application
    methodology is framed by the well known procedures in topic modelling
    on how to clean and process data. Contains methods described by 
    Blei, David M., Andrew Y. Ng, and Michael I. Jordan (2003) 
    <https://jmlr.org/papers/volume3/blei03a/blei03a.pdf> 
    Allocation"; Thomas L. Griffiths and Mark Steyvers (2004) 
    <doi:10.1073/pnas.0307752101> ; Xiong Hui, et al (2019) 
    <doi:10.1016/j.cie.2019.06.010>.
A classification tree method that uses LDA (Linear
    Discriminant Analysis) for variable selection, split determination,
    and model fitting in terminal nodes.  It automatically handles missing
    values and offers visualization tools.
Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial
    time series methods in a two-stage analysis to quantify dynamics in
	high-dimensional temporal data. LDA decomposes multivariate data into 
    lower-dimension latent groupings, whose relative proportions are modeled
    using generalized Bayesian time series models that include abrupt 
    changepoints and smooth dynamics. The methods are described in Blei
    et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp
   (2004) <doi:10.1093/pan/mph023>, Venables and Ripley 
   (2002, ISBN-13:978-0387954578), and Christensen et al. 
   (2018) <doi:10.1002/ecy.2373>.
For this first version only metrics to estimate the best fitting
    number of topics are implemented.
Tools to create an interactive web-based visualization of a
    topic model that has been fit to a corpus of text data using
    Latent Dirichlet Allocation (LDA). Given the estimated parameters of
    the topic model, it computes various summary statistics as input to
    an interactive visualization built with D3.js that is accessed via
    a browser. The goal is to help users interpret the topics in their
    LDA topic model.
Flexible procedures to compute local density-based outlier scores for ranking outliers.
  Both exact and approximate nearest neighbor search can be implemented, while also accommodating
  multiple neighborhood sizes and four different local density-based methods. It allows for
  referencing a random subsample of the input data or a user specified reference data set
  to compute outlier scores against, so both unsupervised and semi-supervised outlier
  detection can be implemented.
Computations related to group sequential boundaries.
    Includes calculation of bounds using the Lan-DeMets
    alpha spending function approach. Based on FORTRAN
        program ld98 implemented by Reboussin, et al. (2000) <doi:10.1016/s0197-2456(00)00057-x>.
Four measures of linkage disequilibrium are provided: the usual r^2
        measure, the r^2_S measure (r^2 corrected by the structure
        sample), the r^2_V (r^2 corrected by the relatedness of
        genotyped individuals), the r^2_VS measure (r^2 corrected by
        both the relatedness of genotyped individuals and the structure
        of the sample).
Implementation of the drift-diffusion mixed model for category learning as described in Paulon et al. (2021) <doi:10.1080/01621459.2020.1801448>.
Hidden Markov Model (HMM) based on symmetric lambda distribution
    framework is implemented for the study of return time-series in the financial
    market. Major features in the S&P500 index, such as regime identification,
    volatility clustering, and anti-correlation between return and volatility,
    can be extracted from HMM cleanly. Univariate symmetric lambda distribution
    is essentially a location-scale family of exponential power distribution.
    Such distribution is suitable for describing highly leptokurtic time series
    obtained from the financial market. It provides a theoretically solid foundation
    to explore such data where the normal distribution is not adequate. The HMM
    implementation follows closely the book: "Hidden Markov Models for Time Series",
    by Zucchini, MacDonald, Langrock (2016).
A wide variety of ways to calculate (through equations) or predict (using 9 Machine learning methods as well as a stack algorithm combination of them all) the Low Density Lipoprotein values of patients based on the values of three other metrics, namely Total Cholesterol , Triglycerides and High Density Lipoprotein. It can also calculate the variance of LDL and the Atherogenic Index of Plasma (AIP) using error propagation and bootstrapping.
Provides access to the 'LDlink' API (<https://ldlink.nih.gov/?tab=apiaccess>)
    using the R console.  This programmatic access facilitates researchers who are 
    interested in performing batch queries in 1000 Genomes Project (2015) <doi:10.1038/nature15393> 
    data using 'LDlink'. 'LDlink' is an interactive and powerful suite of web-based tools for querying 
    germline variants in human population groups of interest. For more details, please see 
    Machiela et al. (2015) <doi:10.1093/bioinformatics/btv402>.
A single analysis path that includes distance-based ordination, global tests of any effect of the microbiome, and tests of the effects of individual taxa with false-discovery-rate (FDR) control. It accommodates both continuous and discrete covariates as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based p-values that can control for sample correlations. It can be applied to transformed data, and an omnibus test can combine results from analyses conducted on different transformation scales. It can also be used for testing presence-absence associations based on infinite number of rarefaction replicates, testing mediation effects of the microbiome, analyzing censored time-to-event outcomes, and for compositional analysis by fitting linear models to centered-log-ratio taxa count data.
This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs.
Linear dimension reduction subspaces can be uniquely defined using orthogonal projection matrices. This package provides tools to compute distances between such subspaces and to compute the average subspace. For details see Liski, E.Nordhausen K., Oja H., Ruiz-Gazen A. (2016) Combining Linear Dimension Reduction Subspaces <doi:10.1007/978-81-322-3643-6_7>.
Estimate haplotypic or composite pairwise linkage disequilibrium
    (LD) in polyploids, using either genotypes or genotype likelihoods. 
    Support is provided to estimate the popular measures of LD: the LD 
    coefficient D, the standardized LD coefficient D', and the Pearson 
    correlation coefficient r. All estimates are returned with corresponding 
    standard errors. These estimates and standard errors can then be used
    for shrinkage estimation. The main functions are ldfast(), ldest(), mldest(),
    sldest(), plot.lddf(), format_lddf(), and ldshrink(). Details of the methods
    are available in Gerard (2021a) <doi:10.1111/1755-0998.13349>
    and Gerard (2021b) <doi:10.1038/s41437-021-00462-5>.
Streamflow (and climate) reconstruction using Linear Dynamical Systems. 
    The advantage of this method is the additional state trajectory which can reveal more information 
    about the catchment or climate system. For details of the method please refer to Nguyen and Galelli 
    (2018) <doi:10.1002/2017WR022114>.
Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) <doi:10.1007/b97636>, among others). 
             'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented.
             Evaluations are both in-sample and out-of-sample. 
             It is designed to be efficient in terms of CPU usage and memory consumption.
The algorithm Leabra (local error driven and associative
    biologically realistic algorithm) allows for the construction of artificial
    neural networks that are biologically realistic and balance supervised and
    unsupervised learning within a single framework. This package is based on
    the 'MATLAB' version by Sergio Verduzco-Flores, which in turn was based on
    the description of the algorithm by Randall O'Reilly (1996)
    <ftp://grey.colorado.edu/pub/oreilly/thesis/oreilly_thesis.all.pdf>. For
    more general (not 'R' specific) information on the algorithm Leabra see
    <https://grey.colorado.edu/emergent/index.php/Leabra>.
The leader clustering algorithm provides
 a means for clustering a set of data points. Unlike many other clustering
 algorithms it does not require the user to specify the number of clusters,
 but instead requires the approximate radius of a cluster as its primary
 tuning parameter. The package provides a fast implementation of this
 algorithm in n-dimensions using Lp-distances (with special cases for p=1,2,
 and infinity) as well as for spatial data using the Haversine
 formula, which takes latitude/longitude pairs as inputs and clusters
 based on great circle distances.
An interface for the image processing program 'ImageJ', which
    allows a rapid digital image analysis for particle sizes. This package includes
    function to write an 'ImageJ' macro which is optimized for a leaf area analysis by
    default.
Provides drill down functionality for 'leaflet' choropleths in 'shiny' apps.
Provides extensions for packages 'leaflet' & 'mapdeck', 
    many of which are used by package 'mapview'. 
    Focus is on functionality readily available in 
    Geographic Information Systems such as 'Quantum GIS'. Includes functions
    to display coordinates of mouse pointer position, query image values via 
    mouse pointer and zoom-to-layer buttons. Additionally, provides a feature 
    type agnostic function to add points, lines, polygons to a map.
Provides bindings to the 'Leaflet.glify' JavaScript library which extends the 'leaflet' JavaScript library to render large data in the browser using 'WebGl'.
Provides extensions to the 'leaflet' package to 
    customize legends with images, text styling, orientation, sizing,
    and symbology and functions to create symbols to plot on maps.
Create and customize interactive maps using the 'Leaflet'
    JavaScript library and the 'htmlwidgets' package. These maps can be
    used directly from the R console, from 'RStudio', in Shiny
    applications and R Markdown documents.
An add-on package to the 'leaflet' package, which provides bindings for 'ESRI' services. This package allows a user to add 'ESRI' provided services such as 'MapService', 'ImageMapService', 'TiledMapService' etc. to a 'leaflet' map.
The 'leaflet' JavaScript library provides many plugins some of which
    are available in the core 'leaflet' package, but there are many more. It is not
    possible to support them all in the core 'leaflet' package. This package serves
    as an add-on to the 'leaflet' package by providing extra functionality via 'leaflet'
    plugins.
Several 'leaflet' plugins are integrated, which are available as extension to the 'leaflet' package.
Add and modify small charts on an interactive map created with 
    package 'leaflet'. These charts can be used to represent at same time multiple 
    variables on a single map.
Contains third-party map tile provider information from
    'Leaflet.js', <https://github.com/leaflet-extras/leaflet-providers>,
    to be used with the 'leaflet' R package. Additionally,
    'leaflet.providers' enables users to retrieve up-to-date provider
    information between package updates.
An R gallery for China and other geojson choropleth map in leaflet. Contains the geojson data for provinces, cities in China.
A collection of tools for interactive manipulation of (spatial) data 
    layers on leaflet web maps. Tools include editing of existing layers, creation 
    of new layers through drawing of shapes (points, lines, polygons), deletion 
    of shapes as well as cutting holes into existing shapes. Provides control over 
    options to e.g. prevent self-intersection of polygons and lines or to enable/disable 
    snapping to align shapes.
Creates 'HTML' strings to embed tables, images or graphs in pop-ups
  of interactive maps created with packages like 'leaflet' or 'mapview'. Handles
  local images located on the file system or via remote URL. Handles graphs created 
  with 'lattice' or 'ggplot2' as well as interactive plots created with 'htmlwidgets'.
A set of functions for analyzing the structure 
  of forests based on the leaf area density (LAD) and leaf area index (LAI) measures 
  calculated from Airborne Laser Scanning (ALS), i.e., scanning lidar (Light Detection 
  and Ranging) data. The methodology is discussed and described in 
  Almeida et al. (2019) <doi:10.3390/rs11010092> and 
  Stark et al. (2012) <doi:10.1111/j.1461-0248.2012.01864.x>.
Implementation of trigonometric functions to calculate the exposure of flat, tilted surfaces, such as leaves and slopes, to direct solar radiation. It implements the equations in A.G. Escribano-Rocafort, A. Ventre-Lespiaucq, C. Granado-Yela, et al. (2014) <doi:10.1111/2041-210X.12141> in a few user-friendly R functions. All functions handle data obtained with 'Ahmes' 1.0 for Android, as well as more traditional data sources (compass, protractor, inclinometer). The main function (star()) calculates the potential exposure of flat, tilted surfaces to direct solar radiation (silhouette to area ratio, STAR). It is equivalent to the ratio of the leaf projected area to total leaf area, but instead of using area data it uses spatial position angles, such as pitch, roll and course, and information on the geographical coordinates, hour, and date. The package includes additional functions to recalculate STAR with custom settings of location and time, to calculate the tilt angle of a surface, and the minimum angle between two non-orthogonal planes.
Create small multiples of several leaflet web maps with (optional) 
    synchronised panning and zooming control. When syncing is enabled all maps 
    respond to mouse actions on one map. This allows side-by-side comparisons
    of different attributes of the same geometries. Syncing can be adjusted
    so that any combination of maps can be synchronised.
Use the 'leaflet-timeline' plugin with a leaflet widget to add an
    interactive slider with play, pause, and step buttons to explore temporal
    geographic spatial data changes.
Provides access to the 'Leanpub' API
    <https://leanpub.com/help/api> for gathering information about 
    publications and submissions to the 'Leanpub' platform.
Implements the method described in "Network-based analysis of omics data: The LEAN method" [Gwinner Boulday (2016) <DOI:10.1093/bioinformatics/btw676>]
 Given a protein interaction network and a list of p-values describing a measure of interest (as e.g. differential gene expression) this method
 computes an enrichment p-value for the protein neighborhood of each gene and compares it to a background distribution of randomly drawn p-values.
 The resulting scores are corrected for multiple testing and significant hits are returned in tabular format.
These functions take a gene expression value matrix, a
        primary covariate vector, an additional known covariates
        matrix.  A two stage analysis is applied to counter the effects
        of latent variables on the rankings of hypotheses.  The
        estimation and adjustment of latent effects are proposed by
        Sun, Zhang and Owen (2011).  "leapp" is developed in the
        context of microarray experiments, but may be used as a general
        tool for high throughput data sets where dependence may be
        involved.
Regression subset selection, including exhaustive search.
Provides methods for estimating borders of uniform distribution on
  the interval (one-dimensional) and on the elliptical domain (two-dimensional)
  under measurement errors. For one-dimensional case, it also estimates the
  length of underlying uniform domain and tests the hypothesized length against
  two-sided or one-sided alternatives. For two-dimensional case, it estimates
  the area of underlying uniform domain. It works with numerical inputs as well
  as with pictures in JPG format.
A collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
Classical hierarchical clustering algorithms, agglomerative and divisive clustering. Algorithms are implemented as a theoretical way, step by step.
  It includes some detailed functions that explain each step. Every function allows options to get different results using different techniques. 
  The package explains non expert users how hierarchical clustering algorithms work.
Contains some functions to learn and teach basic plane Geometry at undergraduate level with the aim of being helpful to young students with little programming skills.
Crabs in the English channel, deer skulls, English
    monarchs, half-caste Manga characters, Jamaican cities,
    Shakespeare's The Tempest, drugged up cyclists and sexually
    transmitted diseases.
Aids in learning statistical functions incorporating the result of calculus done with each function and how they are obtained, that is, which equation and variables are used. Also for all these equations and their related variables detailed explanations and interactive exercises are also included. All these characteristics allow to the package user to improve the learning of statistics basics by means of their use.
Provides tools to teach students elemental statistics. The main topics covered are descriptive statistics, probability models (discrete and continuous variables) and statistical inference (confidence intervals and hypothesis tests). One of the main advantages of this package is that allows the user to read quite a variety of types of data files with one unique command. Moreover it includes shortcuts to simple but up-to-now not in R descriptive features such a complete frequency table or an histogram with the optimal number of intervals. Related to model distributions (both discrete and continuous), the package allows the student to easy plot the mass/density function, distribution function and quantile function just detailing as input arguments the known population parameters. The inference related tools are basically confidence interval and hypothesis testing. Having defined independent commands for these two tools makes it easier for the student to understand what the software is performing, and it also helps the student to have a better knowledge on which specific tool they need to use in each situation. Moreover, the hypothesis testing commands provide not only the numeric result on the screen but also a very intuitive graph (which includes the statistic distribution, the observed value of the statistic, the rejection area and the p-value) that is very useful for the student to visualise the process. The regression section includes up to now, a simple linear model, with one single command the student can obtain the numeric summary as well as the corresponding diagram with the adjusted regression model and a legend with basic information (formula of the adjusted model and R-squared).
The Programme for International Student Assessment (PISA) is a global study conducted by the Organization for Economic Cooperation and Development (OECD) in member and non-member countries to assess educational systems by assessing 15-year-old school students academic performance in mathematics, science, and reading. This datasets contains information on their scores and other socioeconomic characteristics, information about their school and its infrastructure, as well as the countries that are taking part in the program.
Implementations of several basic neural network concepts in R, as based on posts on \url{http://qua.st/}.
Principal component analysis (PCA) is one of the most widely used data analysis techniques.  This package provides a series of vignettes explaining PCA starting from basic concepts. The primary purpose is to serve as a self-study resource for anyone wishing to understand PCA better. A few convenience functions are provided as well.
Conducts various numerical analyses and simulations in population genetics and evolutionary theory, primarily for the purpose of teaching (and learning about) key concepts in population & quantitative genetics, and evolutionary theory.
Create interactive tutorials using R Markdown. Use a
    combination of narrative, figures, videos, exercises, and quizzes to
    create self-paced tutorials for learning about R and R packages.
Data, scripts and code from chunks used as examples in the book 
   "Learn R: As a Language" by Pedro J. Aphalo.
   ISBN 9780367182533 (pbk); ISBN 9780367182557 (hbk); ISBN 9780429060342 (ebk).
Calculates cost surfaces based on slope to be used when modelling pathways and movement potential within a landscape (Lewis, 2021) <doi:10.1007/s10816-021-09522-w>.
Utilities for querying plain text accounting files from 'Ledger', 'HLedger', and 'Beancount'.
Educational tool for teaching of statistics 
    and mathematics in primary and higher education. The objective is 
    to assist in teaching/learning for both student study planning 
    and teacher teaching strategies. The 'leem' package will try to bring, 
    in a simple and at the same time in-depth, 
    knowledge of statistics and mathematics to everyone 
    who wants to study these areas of knowledge. The main function of the 
    package is 'leem()' function.
Functions to download data from the SIE, which is the Statistical Information System (Sistema de Información Estadística) in the Statistical Portal of the Government of Castilla y León (Spain) <https://estadistica.jcyl.es>.
Complete analytical environment for the construction and analysis
             of matrix population models and integral projection models.
             Includes the ability to construct historical matrices, which are
             2d matrices comprising 3 consecutive times of demographic
             information. Estimates both raw and function-based forms of
             historical and standard ahistorical matrices. It also estimates
             function-based age-by-stage matrices and raw and function-based
             Leslie matrices.
Functions implementing multivariate state space models for purposes of time series analysis and forecasting.
             The focus of the package is on multivariate models, such as Vector Exponential Smoothing,
             Vector ETS (Error-Trend-Seasonal model) etc. It currently includes Vector Exponential
             Smoothing (VES, de Silva et al., 2010, <doi:10.1177/1471082X0901000401>), Vector ETS and
             simulation function for VES.
Facilitates access to the Comparative Legislators Database (CLD). The CLD includes political, sociodemographic, career, online presence, public attention, and visual information for over 67,000 contemporary and historical politicians from 16 countries.
Constructs genotype x environment interaction (GxE) models where
    G is a weighted sum of genetic variants (genetic score) and E is a weighted
    sum of environments (environmental score) using the alternating optimization algorithm 
    by Jolicoeur-Martineau et al. (2017) <arXiv:1703.08111>. This approach has greatly 
    enhanced predictive power over traditional GxE models which include only a single 
    genetic variant and a single environmental exposure. Although this approach was 
    originally made for GxE modelling, it is flexible and does not require the use of 
    genetic and environmental variables. It can also handle more than 2 latent variables 
    (rather than just G and E) and 3-way interactions or more. The LEGIT model produces 
    highly interpretable results and is very parameter-efficient thus it can even be 
    used with small sample sizes (n < 250). Tools to determine the type of interaction
    (vantage sensitivity, diathesis-stress or differential susceptibility), with any 
    number of genetic variants or environments, are available <arXiv:1712.04058>. The 
    software can now produce mixed-effects LEGIT models through the lme4 package.
Provides a dataset containing several color naming conventions established by multiple sources, along with associated color metadata.
    The package also provides related helper functions for mapping among the different Lego color naming conventions and between Lego colors, hex colors, and 'R' color names, 
    making it easy to convert any color palette to one based on existing Lego colors while keeping as close to the original color palette as possible.
    The functions use nearest color matching based on Euclidean distance in RGB space. 
    Naming conventions for color mapping include those from 'BrickLink' (<https://www.bricklink.com>), 'The Lego Group' (<https://www.lego.com>), 'LDraw' (<https://www.ldraw.org/>), and 'Peeron' (<http://www.peeron.com/>).
Implements the 'Python leidenalg' module to be called in R.
    Enables clustering using the leiden algorithm for partition a graph into communities.
    See the 'Python' repository for more details: <https://github.com/vtraag/leidenalg>
    Traag et al (2018) From Louvain to Leiden: guaranteeing well-connected communities. <arXiv:1810.08473>.
An R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The original implementation was constructed as a python interface "leidenalg" found here: <https://github.com/vtraag/leidenalg>. The algorithm was originally described in Traag, V.A., Waltman, L. & van Eck, N.J. "From Louvain to Leiden: guaranteeing well-connected communities". Sci Rep 9, 5233 (2019) <doi:10.1038/s41598-019-41695-z>.
An R to C/C++ interface that runs the Leiden community
    detection algorithm to find a basic partition (). It runs the
    equivalent of the 'leidenalg' find_partition() function, which is
    given in the 'leidenalg' distribution file
    'leiden/src/functions.py'. This package includes the
    required source code files from the official 'leidenalg'
    distribution and functions from the R 'igraph'
    package.  The 'leidenalg' distribution is available from
    <https://github.com/vtraag/leidenalg/>
    and the R 'igraph' package is available from
    <https://igraph.org/r/>.
    The Leiden algorithm is described in the article by
    Traag et al. (2019) <doi:10.1038/s41598-019-41695-z>.
    Leidenbase includes code from the packages:
       igraph version 0.9.8 with license GPL (>= 2),
       leidenalg version 0.8.10 with license GPL 3.
Estimate the slope and intercept of a bivariate
	linear relationship by calculating a posterior density
	that is invariant to interchange and scaling of the
	coordinates.
Set up, run and explore the outputs of the Length-based Multi-species model (LeMans; Hall et al. 2006 <doi:10.1139/f06-039>), focused on the marine environment.
The reference implementation of model equations and default
    parameters for the toxicokinetic-toxicodynamic (TKTD) model of the Lemna
    (duckweed) aquatic plant. Lemna is a standard test macrophyte used in ecotox
    effect studies. The model was described and published by the SETAC Europe
    Interest Group Effect Modeling. It is a refined description of the Lemna
    TKTD model published by Schmitt et al. (2013)
    <doi:10.1016/j.ecolmodel.2013.01.017>.
Functions for working with legends and axis lines of 'ggplot2',
    facets that repeat axis lines on all panels, and some 'knitr' extensions.
Convenient aliases for common ways of misspelling the base R function length(). These include every permutation of the final three letters.
Provides tools for creating and using lenses to simplify data manipulation. Lenses are composable getter/setter pairs for working with data in a purely functional way. Inspired by the 'Haskell' library 'lens' (Kmett, 2012) <https://hackage.haskell.org/package/lens>. For a fairly comprehensive (and highly technical) history of lenses please see the 'lens' wiki <https://github.com/ekmett/lens/wiki/History-of-Lenses>.
An implementation of the Input-Output model developed by
    Wassily Leontief that represents the interdependencies between different 
    sectors of a national economy or different regional economies.
Code generator for robust dependency-free 'Shiny' 
    applications in the form of packages. It includes numerous
    convenience functions to create modules, include utility 
    functions to create common 'Bootstrap' elements, setup a
    project from the ground-up, and much more.
A way to fit Parsimonious Finite Mixtures of Multivariate Elliptical Leptokurtic-Normals (MLN) based on the component covariance matrix eigen-decomposition. Two methods of estimation are implemented. The MLN is useful for fitting to data with excess kurtosis. Based on work in Bagnato, L., Punzo, A. and Zoia, M.G. (2017) <doi:10.1002/cjs.11308> and Browne (2023) <doi:10.1016/j.spl.2022.109640>.
Finds and downloads Landscape Evaporative Response
    Index (LERI) data, then reads the data into 'R' using the 'raster'
    package. The LERI product measures anomalies in actual
    evapotranspiration, to support drought monitoring and early warning
    systems. More info on LERI is available at
    <https://www.esrl.noaa.gov/psd/leri/>.
Generates quotes from 'Lero Lero', a database for meaningless sentences filled with corporate buzzwords, intended to be used as corporate lorem ipsum (see <http://www.lerolero.com/> for more information). Unfortunately, quotes are currently portuguese-only.
"Learning with Subset Stacking" is a supervised learning algorithm that is based on training many local estimators on subsets of a given dataset, and then passing their predictions to a global estimator. You can find the details about LESS in our manuscript at <arXiv:2112.06251>.
Each function accomplishes the work of multiple standard R functions. For example, two function calls, Read() and CountAll(), read the data and generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate. Other functions provide for comprehensive summary statistics via pivot tables, a comprehensive regression analysis, ANOVA and t-test, visualizations including the Violin/Box/Scatter plot for a numerical variable, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function call, variable labels, color themes, and Trellis graphics. Also includes a confirmatory factor analysis of multiple indicator measurement models, pedagogical routines for data simulation such as for the Central Limit Theorem, generation and rendering o regression instructions for interpretative output, and interactive visualizations.
Provides regularized structural equation modeling 
  (regularized SEM) with non-smooth penalty functions (e.g., lasso) building 
  on 'lavaan'. The package is heavily inspired by the 
  ['regsem'](<https://github.com/Rjacobucci/regsem>) and 
  ['lslx'](<https://github.com/psyphh/lslx>) packages.
Functions for vectorised conditional recoding of
    variables. case_when() enables you to vectorise multiple if and else
    statements (like 'CASE WHEN' in 'SQL'). if_else() is a stricter and
    more predictable version of ifelse() in 'base' that preserves
    attributes. These functions are forked from 'dplyr' with all package
    dependencies removed and behave identically to the originals.
Some simple objects and functions to do 
             statistics using linear models and a Bayesian framework.
Letter Values for the course Exploratory Data Analysis at Federal University of Bahia (Brazil). The approach implemented in the package is presented in the textbook of Tukey  (1977) <ISBN: 978-0201076165>.
Provides string similarity calculations inspired by the
    Python 'fuzzywuzzy' package. Compare strings by edit distance,
    similarity ratio, best matching substring, ordered token matching and
    set-based token matching. A range of edit distance measures are
    available thanks to the 'stringdist' package.
Implements code to identify lexical competitors in a given list
  of words. We include many of the standard competitor types used in spoken word
  recognition research, such as functions to find cohorts, neighbors, and
  rhymes, amongst many others. The package includes documentation for using a
  variety of lexicon files, including those with form codes made up of multiple
  letters (i.e., phoneme codes) and also basic orthographies. Importantly, the
  code makes use of multiple CPU cores and vectorization when possible, making
  it extremely fast and able to handle large lexicons. Additionally, the package
  contains documentation for users to easily write new functions, allowing
  researchers to examine other relationships within a lexicon. 
  Preprint: <https://psyarxiv.com/8dyru/>. Open access: <https://link.springer.com/epdf/10.3758/s13428-021-01667-6?sharing_token=9WlO9soCc9y0uSuwWSUYfJAH0g46feNdnc402WrhzyrdKcK8uzZx_hDEtgbYzn3gvxdG5Cuj0j0cC4lVMFBqYCGTQmE2blN2Gwo74LJ8ro1pEOAYDRFy6Lhf1nc719vD-zU7GDvKOQxDAwPbrisvPBeXSIu0NkqXF7Jx3IuUwIs%3D>. 
  Citation: Li, Z., Crinnion, A.M. & Magnuson, J.S. (2021). 
  <doi:10.3758/s13428-021-01667-6>.
A collection of lexical hash tables, dictionaries, and word lists.
Provides easy access for sentiment lexicons for those who want to do text analysis in Portuguese texts.
    As of now, two Portuguese lexicons are available: 'SentiLex-PT02' and 'OpLexicon' (v2.1 and v3.0).
My PhD supervisor once told me that everyone doing newspaper
    analysis starts by writing code to read in files from the 'LexisNexis' newspaper
    archive (retrieved e.g., from <https://www.lexisnexis.com/> or any of the partner
    sites). However, while this is a nice exercise I do recommend, not everyone has
    the time. This package takes files downloaded from the newspaper archive of
    'LexisNexis', reads them into R and offers functions for further processing.
Plots empty Lexis grids, adds lifelines and highlights certain areas of the grid, like cohorts and age groups.
An R implementation of the LexRank algorithm described by G. Erkan and D. R. Radev (2004) <DOI:10.1613/jair.1523>.
Provides an extension to factors called 'lfactor' that are similar
    to factors but allows users to refer to 'lfactor' levels by either the level or
    the label.
Shiny apps for the quantitative analysis of images from lateral flow assays (LFAs). The images are segmented and background corrected and color intensities are extracted. The apps can be used to import and export intensity data and to calibrate LFAs by means of linear, loess, or gam models. The calibration models can further be saved and applied to intensity data from new images for determining concentrations.
Ratios of count data such as obtained from RNA-seq are modelled
    using Bayesian statistics to derive posteriors for effects sizes. This
    approach is described in Erhard & Zimmer (2015) <doi:10.1093/nar/gkv696> 
    and Erhard (2018) <doi:10.1093/bioinformatics/bty471>.
Functions for performing and visualizing Local Fisher Discriminant
    Analysis(LFDA), Kernel Fisher Discriminant Analysis(KLFDA), and Semi-supervised
    Local Fisher Discriminant Analysis(SELF).
Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).
Estimation of the local false discovery rate using the method of moments.
New empirical Bayes methods aiming at analyzing the association of single nucleotide polymorphisms (SNPs) to some particular disease are implemented in this package. The package uses local false discovery rate (LFDR) estimates of SNPs within a sample population defined as a  "reference class" and discovers if SNPs are associated with the corresponding disease. Although SNPs are used throughout this document, other biological data such as protein data and other gene data can be used. Karimnezhad, Ali and Bickel, D. R. (2016) <http://hdl.handle.net/10393/34889>.
Transforms away factors with many levels prior to doing an OLS.
  Useful for estimating linear models with multiple group fixed effects, and for
  estimating linear models which uses factors with many levels as pure control variables. See Gaure (2013) <doi:10.1016/j.csda.2013.03.024>
  Includes support for instrumental variables, conditional F statistics for weak instruments,
  robust and multi-way clustered standard errors, as well as limited mobility bias correction (Gaure 2014 <doi:10.1002/sta4.68>). 
  WARNING: This package is NOT under active development anymore, no further improvements are to be expected, and the package is at risk of being removed from CRAN.
Various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association
    rules, composition of fuzzy relations, performing perception-based logical deduction (PbLD), 
    and forecasting time-series using fuzzy rule-based ensemble (FRBE). The package also contains basic
    fuzzy-related algebraic functions capable of handling missing values in different styles (Bochvar,
    Sobocinski, Kleene etc.), computation of Sugeno integrals and fuzzy transform.
Fast and accurate inference of 
             gene-environment associations (GEA) in genome-wide studies 
             (Caye et al., 2019, <doi:10.1093/molbev/msz008>). 
             We developed a least-squares estimation approach for confounder and effect sizes 
             estimation that provides a unique framework for several categories of genomic data, 
             not restricted to genotypes. 
             The speed of the new algorithm is several times faster than the existing GEA approaches, 
             then our previous version of the 'LFMM' program present in the 'LEA' package 
             (Frichot and Francois, 2015, <doi:10.1111/2041-210X.12382>).
The "Manual on Low-flow Estimation and Prediction" (Gustard & Demuth (2009, ISBN:978-92-63-11029-9)), published by the World Meteorological Organisation, gives a comprehensive summary on how to analyse stream flow data focusing on low-flows. This packages provides functions to compute the described statistics and produces plots similar to the ones in the manual.
An implementation of locally Gaussian distributions. It provides methods for
    implementing locally Gaussian multivariate density estimation, conditional density 
    estimation, various independence tests for iid and time series data, a test for conditional 
    independence and a test for financial contagion.
Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.
Spatial and spatio-temporal modelling of point patterns using the
    log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal,
    multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.
The goal of this package is to cover the most common steps in Loss Given Default (LGD) rating model development.
             The main procedures available are those that refer to bivariate and multivariate analysis. In particular two statistical methods for 
             multivariate analysis are currently implemented – OLS regression and fractional logistic regression.
             Both methods are also available within different blockwise model designs and both have customized stepwise algorithms. 
             Descriptions of these customized designs are available in Siddiqi (2016) <doi:10.1002/9781119282396.ch10> and 
             Anderson, R.A. (2021) <doi:10.1093/oso/9780192844194.001.0001>. 
             Although they are explained for PD model, the same designs are applicable for LGD model with different underlying regression methods 
             (OLS and fractional logistic regression). To cover other important steps for LGD model development, it is recommended to use 
             'LGDtoolkit' package along with 'PDtoolkit', and 'monobin' (or 'monobinShiny') packages.
             Additionally, 'LGDtoolkit' provides set of procedures handy for initial and periodical model validation.
Functions for genome-wide association studies (GWAS)/gene-environment-wide interaction studies (GEWIS) with longitudinal outcomes and exposures. He et al. (2017) "Set-Based Tests for Gene-Environment Interaction in Longitudinal Studies" and He et al. (2017) "Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA)".
For high-dimensional correlated observations, this package carries out the L_1 penalized maximum likelihood 
            estimation of the precision matrix (network) and the correlation parameters. The correlated data can be 
            longitudinal data (may be irregularly spaced) with dampening correlation or clustered data with uniform correlation.  
            For the details of the algorithms, please see the paper Jie Zhou et al. Identifying Microbial Interaction Networks Based on Irregularly Spaced 
            Longitudinal 16S rRNA sequence data <doi:10.1101/2021.11.26.470159>.
Interpretable nonparametric modeling of longitudinal data
    using additive Gaussian process regression. Contains functionality
    for inferring covariate effects and assessing covariate relevances.
    Models are specified using a convenient formula syntax, and can include
    shared, group-specific, non-stationary, heterogeneous and temporally
    uncertain effects. Bayesian inference for model parameters is performed
    using 'Stan'. The modeling approach and methods are described in detail in
    Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.
A flexible, feature-rich yet light-weight logging
    framework based on 'R6' classes. It supports hierarchical loggers,
    custom log levels, arbitrary data fields in log events, logging to
    plaintext, 'JSON', (rotating) files, memory buffers. For extra
    appenders that support logging to databases, email and push
    notifications see the the package lgr.app.
A largish collection of example datasets, including several classics. Many of
    these datasets are well suited for regression, classification, and visualization.
Additional appenders for the logging package 'lgr' that
    support logging to databases, email and push notifications.
Functions for the longitudinal genetic random field method (He et al., 2015, <doi:10.1111/biom.12310>) to test the association between a longitudinally measured quantitative outcome and a set of genetic variants in a gene/region.
A very simple implementation of a class for 
	     longitudinal data.
Contains different algorithms and construction methods for optimal Latin hypercube designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating maximin distance LHDs, maximum projection LHDs, and orthogonal and nearly orthogonal LHDs. Detailed comparisons and summary of all the algorithms and construction methods in this package can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <arXiv:2010.09154>. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments.
Fits sex-specific life-history models for fish and other taxa where some of the individuals have unknown sex.
Provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples.
Given an RNA-seq derived cell-by-gene matrix and an DNA-seq derived copy number segmentation, LIAYSON predicts the number of clones present in a tumor, their size, the copy number profile of each clone and the clone membership of each single cell (Andor, N. & Lau, B., et al. (2018) <doi:10.1101/445932>).
Provides functions for validating and normalizing bibliographic
   codes such as ISBN, ISSN, and LCCN. Also includes functions to communicate
   with the WorldCat API, translate Call numbers (Library of Congress and
   Dewey Decimal) to their subject classifications or subclassifications,
   and provides various loadable data files such call number / subject
   crosswalks and code tables.
Basic infrastructure for linear test statistics and permutation
  inference in the framework of Strasser and Weber (1999) <https://epub.wu.ac.at/102/>.
  This package must not be used by end-users. CRAN package 'coin' implements all
  user interfaces and is ready to be used by anyone.
Provides the Open Source Geometry Engine ('GEOS') as a
  C API that can be used to write high-performance C and C++
  geometry operations using R as an interface. Headers are provided
  to make linking to and using these functions from C++ code as
  easy and as safe as possible. This package contains an internal
  copy of the 'GEOS' library to guarantee the best possible
  consistency on multiple platforms.
A wrapper around the LIBLINEAR C/C++ library for machine
        learning (available at
        <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is
        a simple library for solving large-scale regularized linear
        classification and regression. It currently supports
        L2-regularized classification (such as logistic regression,
        L2-loss linear SVM and L1-loss linear SVM) as well as
        L1-regularized classification (such as L2-loss linear SVM and
        logistic regression) and L2-regularized support vector
        regression (with L1- or L2-loss). The main features of
        LiblineaR include multi-class classification (one-vs-the rest,
        and Crammer & Singer method), cross validation for model
        selection, probability estimates (logistic regression only) or
        weights for unbalanced data. The estimation of the models is
        particularly fast as compared to other libraries.
The 'LibOPF' is a framework to develop pattern recognition techniques based on optimum-path forests (OPF), João P. Papa and Alexandre X. Falcão (2008) <doi:10.1007/978-3-540-89639-5_89>, with methods for supervised learning and data clustering.
Contains a set of functions to create data libraries,
    generate data dictionaries, and simulate a data step.
    The libname() function will load a directory of data into 
    a library in one line of code.  The dictionary() function
    will generate data dictionaries for individual
    data frames or an entire library.  And the datestep() function
    will perform row-by-row data processing.
Efficient procedures for fitting the regularization path
    for linear, binomial, multinomial, Ising and Potts models with lasso,
    group lasso or column lasso(only for multinomial) penalty.
    The package uses Linearized Bregman Algorithm to solve the
    regularization path through iterations. Bregman Inverse Scale Space Differential
    Inclusion solver is also provided for linear model with lasso penalty.
Automatically install, update, and load 'CRAN', 'GitHub', and 'Bioconductor' 
    packages in a single function call. By accepting bare unquoted names for packages, 
    it's easy to add or remove packages from the list.
Generate a local library copy with relevant packages. 
    All packages currently found within the search path - except base packages - 
    will be copied to the directory provided and can be used later on with the 
    .libPaths() function.
Tools for fast and accurate evaluation of skew stable distributions 
  (CDF, PDF and quantile functions), random number generation, and parameter 
  estimation.  This is 'libstableR' as per Royuela del Val, Simmross-Wattenberg, 
  and Alberola López (2017) <doi:10.18637/jss.v078.i01> under a new maintainer.
The LIC criterion is to determine the most informative subsets so that the subset can retain most of the information contained in the complete data. The philosophy of the package is described in Guo G. (2022) <doi:10.1080/02664763.2022.2053949>.
Provides functions for forest analysis using airborne laser scanning (LiDAR remote sensing) data: 
    tree detection (method 1 in Eysn et al. (2015) <doi:10.3390/f6051721>) and segmentation; 
    forest parameters estimation and mapping with the area-based approach. It includes complementary steps for forest mapping: 
    co-registration of field plots with LiDAR data (Monnet and Mermin (2014) <doi:10.3390/f5092307>); 
    extraction of both physical (gaps, edges, trees) and statistical features from LiDAR data useful 
    for e.g. habitat suitability modeling (Glad et al. (2020) <doi:10.1002/rse2.117>) and forest maturity mapping (Fuhr et al. (2022) <doi:10.1002/rse2.274>); 
    model calibration with ground reference, and maps export.
Airborne LiDAR (Light Detection and Ranging) interface for data
    manipulation and visualization. Read/write 'las' and 'laz' files, computation
    of metrics in area based approach, point filtering, artificial point reduction,
    classification from geographic data, normalization, individual tree segmentation
    and other manipulations.
Classes and methods that allow the user to manage life table,
    actuarial tables (also multiple decrements tables). Moreover, functions to easily
    perform demographic, financial and actuarial mathematics on life contingencies
    insurances calculations are contained therein. See Spedicato (2013)	<doi:10.18637/jss.v055.i10>.
Provides in built datasets and three functions.
  These functions are mobility_index, nonStanTest and linkedLives.  The mobility_index
  function facilitates the calculation of lifecourse fluidity, whilst the nonStanTest and the 
  linkedLives functions allow the user to determine the probability that the observed sequence data 
  was due to chance.  The linkedLives function acknowledges the fact that some individuals may
  have identical sequences.
  The datasets available provide sequence data on marital status(maritalData) 
  and mobility (mydata) for a selected group of individuals from the British Household Panel Study
  (BHPS).  In addition, personal and house ID's for 100 individuals are provided in a 
  third dataset (myHouseID) from the BHPS.
Manage the life cycle of your exported functions with shared
    conventions, documentation badges, and user-friendly deprecation
    warnings.
Likelihood-based estimation of individual growth and sexual maturity models for organisms, usually fish and invertebrates. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions.
R6 classes to model traditional life insurance
    contracts like annuities, whole life insurances or endowments. Such life
    insurance contracts provide a guaranteed interest and are not directly linked
    to the performance of a particular investment vehicle. However, they typically
    provide (discretionary) profit participation. This package provides a framework
    to model such contracts in a very generic (cash-flow-based) way and includes
    modelling profit participation schemes, dynamic increases or more general
    contract layers, as well as contract changes (like sum increases or premium
    waivers). All relevant quantities like premium decomposition, reserves and 
    benefits over the whole contract period are calculated and potentially 
    exported to excel. Mortalities are given using the 'MortalityTables' package.
Provides a framework for combining self-data (exercise, sleep, etc.) from multiple sources (fitbit, Apple Health), creating visualizations, and experimenting on onself.
Allow to visualise data on the NCBI phylogenetic tree as presented in Lifemap '<http://lifemap.univ-lyon1.fr/>'. It takes as input a dataframe with at least a "taxid" column containing NCBI format TaxIds and allows to draw multiple layers with different visualisation tools.
A suite of tools to use the 'eBird' database 
    (<https://ebird.org/home/>) and APIs to compare users' species lists to 
    recent observations and create a report of the top sites to visit to see 
    new species.
Functions supplied in this package will implement
        discriminant analysis to select an appropriate life table
        family, select an appropriate alpha level based on a desired
        life expectancy at birth, produce a model mortality pattern
        based on family and level as well as plot the results.
Implementations of Hurst exponent estimators based on the relationship between wavelet lifting scales and wavelet energy of Knight et al (2017) <doi:10.1007/s11222-016-9698-2>.
Persistent reproducible reporting by containerization of R Markdown documents.
Allows you to read and change the state of 'LIFX' smart light bulbs via the 'LIFX' developer api <https://api.developer.lifx.com/>.
    Covers most 'LIFX' api endpoints, including changing light color and brightness, selecting lights by id, group or location as well as activating effects.
Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. The original algorithm is detailed in Subramanian et al. with 'Java' implementations available through the Broad Institute (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>). The 'liger' package provides a lightweight R implementation of this enrichment test on a list of values (Fan et al., 2017 <doi:10.5281/zenodo.887386>). Given a list of values, such as p-values or log-fold changes derived from differential expression analysis or other analyses comparing biological states, this package enables you to test a priori defined set of genes for enrichment to enable interpretability of highly significant or high fold-change genes.
Tree based algorithms can be improved by introducing boosting frameworks. 
    'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>.
    This package offers an R interface to work with it.
    It is designed to be distributed and efficient with the following advantages:
        1. Faster training speed and higher efficiency.
        2. Lower memory usage.
        3. Better accuracy.
        4. Parallel learning supported.
        5. Capable of handling large-scale data.
    In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions.
    Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.
The purpose of this package is to enable usage of lightningviz server to be accessible from R. The
   server itself can be found at http://lightning-viz.org/ and is required to work with this package. Package
   by itself cannot and will not create any visualizations.
Parse various reflectance/transmittance/absorbance spectra file
    formats to extract spectral data and metadata, as described in Gruson, White
    & Maia (2019) <doi:10.21105/joss.01857>. Among other formats, it can import
    files from 'Avantes' <https://www.avantes.com/>, 'CRAIC' 
    <https://www.microspectra.com/>, and 'OceanInsight' (formerly 'OceanOptics') 
    <https://www.oceaninsight.com/> brands.
Lights Out is a puzzle game consisting of a grid of lights
    that are either on or off. Pressing any light will toggle it and its
    adjacent lights. The goal of the game is to switch all the lights off. This
    package provides an interface to play the game on different board sizes,
    both through the command line or with a visual application. Puzzles can
    also be solved using the automatic solver included. View a demo
    online at <https://daattali.com/shiny/lightsout/>.
Tools for maximum likelihood estimation of parameters 
  of scientific models. Based on Goffe et al (1994) <doi:10.1016/0304-4076(94)90038-8>.
Functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.
Provides likelihood functions as defined by Fisher (1922)
    <doi:10.1098/rsta.1922.0009> and a function that creates likelihood 
    functions from density functions. The functions are meant to aid in 
    education of likelihood based methods.
A collection of functions that calculate the log likelihood 
    (support) for a range of statistical tests. Where possible the likelihood 
    function and likelihood interval for the observed data are displayed. The 
    evidential approach used here is based on the book "Likelihood" by A.W.F. 
    Edwards (1992, ISBN-13 : 978-0801844430), "Statistical Evidence" by R. 
    Royall (1997, ISBN-13 : 978-0412044113), S.N. Goodman & R. Royall 
    (2011) <doi:10.2105/AJPH.78.12.1568>, "Understanding 
    Psychology as a Science" by Z. Dienes (2008, ISBN-13 : 978-0230542310), 
    S. Glover & P. Dixon <https://link.springer.com/article/10.3758/BF03196706> 
    and others. This package accompanies "Evidence-Based Statistics" by 
    P. Cahusac (2020, ISBN-13 : 978-1119549802).
An approach to analyzing Likert response items, with an emphasis on visualizations. 
    The stacked bar plot is the preferred method for presenting Likert results. Tabular results
    are also implemented along with density plots to assist researchers in determining whether 
    Likert responses can be used quantitatively instead of qualitatively. See the likert(), 
    summary.likert(), and plot.likert() functions to get started.
Synthesise and correlate rating-scale data with 
  predefined first & second moments and, optionally, predefined 
  correlation matrix. 
  The function, `lexact()`, uses the 'DEoptim' 
  <https://CRAN.R-project.org/package=DEoptim> package, described in 
  Mullen, Ardia, Gil, Windover, & Cline (2011) <doi:10.18637/jss.v040.i06>,
  to synthesise a vector of discrete values with predefined mean and 
  standard deviation exact to two decimal places, if feasible. 
  The function, `lfast()`, draws a random sample from a _Beta_ distribution 
  which is rescaled to give a vector with approximate first and second moments. 
  It is much faster than `lexact()` but not as precise. 
  The function, `lcor()`, systematically swaps values within each column of a 
  data-frame so that they are correlated to fit a predefined correlation matrix.
A comprehensive analysis tool for metabolomics data.  It consists a variety of functional modules, including several new modules: a pre-processing module for normalization and imputation, an exploratory data analysis module for dimension reduction and source of variation analysis, a classification module with the new deep-learning method and other machine-learning methods, a prognosis module with cox-PH and neural-network based Cox-nnet methods, and pathway analysis module to visualize the pathway and interpret metabolite-pathway relationships. References: H. Paul Benton <http://www.metabolomics-forum.com/index.php?topic=281.0> Jeff Xia <https://github.com/cangfengzhe/Metabo/blob/master/MetaboAnalyst/website/name_match.R> Travers Ching, Xun Zhu, Lana X. Garmire (2018) <doi:10.1371/journal.pcbi.1006076>.
Estimation of life expectancy and
    Life Years Lost (LYL, or lillies for short) for a given population, for 
    example those with a given disease or condition. In addition, the package 
    can be used to compare estimates from different populations, or to estimate 
    confidence intervals. Technical details of the method are available in
    Plana-Ripoll et al. (2020) <doi:10.1371/journal.pone.0228073> and Andersen
    (2017) <doi:10.1002/sim.7357>.
This is for code management functions, NLP tools, a Monty Hall simulator, and for implementing my own variable reduction technique called Feed Reduction. The Feed Reduction technique is not yet published, but is merely a tool for implementing a series of binary neural networks meant for reducing data into N dimensions, where N is the number of possible values of the response variable.
Functions that read and solve linear inverse problems (food web problems, linear programming problems).
  These problems find solutions to linear or quadratic functions:
  min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(ai*xi)
  subject to equality constraints Ex=f and inequality constraints Gx>=h.
When building complex models, it is often difficult to explain why
    the model should be trusted. While global measures such as accuracy are
    useful, they cannot be used for explaining why a model made a specific
    prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for
    explaining the outcome of black box models by fitting a local model around
    the point in question an perturbations of this point. The approach is
    described in more detail in the article by Ribeiro et al. (2016) 
    <arXiv:1602.04938>.
Compose interactive visualisations designed for exploratory 
        high-dimensional data analysis. With 'liminal' you can create linked
        interactive graphics to diagnose the quality of a dimension reduction
        technique and explore the global structure of a dataset with a tour. A
        complete description of the method is discussed in 
        ['Lee' & 'Laa' & 'Cook' (2020) <arXiv:2012.06077>].
Palettes generated from limnology based field and laboratory photos. Palettes can be used to generate color values to be used in any functions that calls for a color (i.e. ggplot(), plot(), flextable(), etc.).
'LimeSurvey' is Free/Libre Open Source Software for
  the development and administrations of online studies, using
  sophisticated tailoring capabilities to support multiple study
  designs (see <https://www.limesurvey.org>). This package supports
  programmatic creation of surveys that can then be imported into
  'LimeSurvey', as well as user friendly import of responses from
  'LimeSurvey' studies.
A flexible approach, inspired by cosinor regression, for
  differential analysis of rhythmic transcriptome data. See Singer and Hughey
  (2018) <doi:10.1177/0748730418813785>.
Fit linear models based on periodic splines, moderate model
  coefficients using multivariate adaptive shrinkage, then compute properties of
  the moderated curves.
Functions that (1) find the minimum/maximum of a linear or quadratic function:
  min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(a_i*x_i)
  subject to equality constraints Ex=f and/or inequality constraints Gx>=h,
  (2) sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b,      
  (3) solve a linear system Ax=B for the unknown x. It includes banded and tridiagonal linear systems.
Evaluates whether the relationship between two vectors is linear or nonlinear. Performs a test to determine how well a linear model fits the data compared to higher order polynomial models. Jhang et al. (2004) <doi:10.1043/1543-2165(2004)128%3C44:EOLITC%3E2.0.CO;2>.
Short for 'linear binning', the linbin package provides functions
    for manipulating, binning, and plotting linearly referenced data. Although
    developed for data collected on river networks, it can be used with any interval
    or point data referenced to a 1-dimensional coordinate system. Flexible bin
    generation and batch processing makes it easy to compute and visualize variables
    at multiple scales, useful for identifying patterns within and between variables
    and investigating the influence of scale of observation on data interpretation.
Estimate and confidence/credible intervals for an unknown
    regressor x0 given an observed y0.
Perform two linear combination methods for biomarkers: (1) Empirical performance optimization for specificity (or sensitivity) at a controlled sensitivity (or specificity) level of Huang and Sanda (2022) <doi:10.1214/22-aos2210>, and (2) weighted maximum score estimator with empirical minimization of averaged false positive rate and false negative rate. Both adopt the algorithms of Huang and Sanda (2022) <doi:10.1214/22-aos2210>. 'MOSEK' solver is used and needs to be installed; an academic license for 'MOSEK' is free.
Sample truncated multivariate Normal distribution following Gessner, A., Kanjilal, O., & Hennig, P. (2019). Integrals over Gaussians under Linear Domain Constraints. 108. <arxiv:1910.09328>.
L-systems or Lindenmayer systems are parallel rewriting systems which can
    be used to simulate biological forms and certain kinds of fractals.
    Briefly, in an L-system a series of symbols in a string are replaced
    iteratively according to rules to give a more complex string. Eventually,
    the symbols are translated into turtle graphics for plotting. Wikipedia has
    a very good introduction: en.wikipedia.org/wiki/L-system This package
    provides basic functions for exploring L-systems.
Provides a set of streamlined functions that allow
    easy generation of linear regression diagnostic plots necessarily 
    for checking linear model assumptions.
    This package is meant for easy scheming of linear 
    regression diagnostics, while preserving merits of 
    "The Grammar of Graphics" as implemented in 'ggplot2'.
    See the 'ggplot2' website for more information regarding the
    specific capability of graphics.
Computes the probability density function, the cumulative distribution function, the hazard rate function, the quantile function and random generation for Lindley Power Series distributions, see Nadarajah and Si (2018) <doi:10.1007/s13171-018-0150-x>.
An interface to accelerate linear regression (Ordinary least squares) modelling, which
  allows users to build models quickly, while automatically generating interactive visualizations of the results.
  Non-linear models specification (e.g. y = b1*x1 + b2*log(x2)) can be easily constructed using user-defined transformations.
  Functions for testing wide ranges of model specifications (e.g. y = b*log(x,10) , y = b*log(x,20) , ...), all at once, are also available.
  Finally, models can be imported and exported as Excel files where all the information necessary for re-running the models is stored in separate sheets.
A unified framework for simultaneous structural break detection and parameter estimation in high-dimensional linear models. The proposed method can handle a wide range of models, including change-in-mean model, multiple linear regression model, Vector auto-regressive model and Gaussian graphical model.
Functions to access and test results from a linear model.
Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.
Tests whether the linear hypothesis of a model
    is correct specified using Dominguez-Lobato test. Also Ramsey's RESET (Regression Equation 
    Specification Error Test) test is implemented and Wald tests can be carried out. 
    Although RESET test is widely used to 
    test the linear hypothesis of a model, Dominguez and Lobato (2019) proposed a novel 
    approach that generalizes well known specification tests such as Ramsey's. This test 
    relies on wild-bootstrap; this package implements this approach to be 
    usable with any function that fits linear models and is compatible with 
    the update() function such as 'stats'::lm(), 'lfe'::felm() and 'forecast'::Arima(), 
    for ARMA (autoregressive–moving-average) models. 
    Also the package can handle custom statistics such as Cramer von Mises and Kolmogorov 
    Smirnov, described by the authors, and custom distributions such as Mammen (discrete 
    and continuous) and Rademacher.
    Manuel A. Dominguez & Ignacio N. Lobato (2019) <doi:10.1080/07474938.2019.1687116>.
Provides tools to help storing and handling case line list data. The 'linelist' class adds a tagging system to classical 'data.frame' objects to identify key epidemiological data such as dates of symptom onset, epidemiological case definition, age, gender or disease outcome. Once tagged, these variables can be seamlessly used in downstream analyses, making data pipelines more robust and reliable.
Create maps made of lines. The package contains two functions:
    linemap() and getgrid(). linemap() displays a map made of lines using a
    data frame of gridded data. getgrid() transforms a set of polygons
    (sf objects) into a suitable data frame for linemap().
Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures.
Tools for detecting and correcting sample mix-ups between two sets
    of measurements, such as between gene expression data on two tissues.
    Broman et al. (2015) <doi:10.1534/g3.115.019778>.
Tools for detecting and correcting sample mix-ups between two sets
    of measurements, such as between gene expression data on two
    tissues. This is a revised version of the 'lineup' package, to be
    more general and not tied to the 'qtl' package.
'LineUp' is an interactive technique designed to create, visualize and explore rankings of items based on a set of heterogeneous attributes.
  This is a 'htmlwidget' wrapper around the JavaScript library 'LineUp.js'.
  It is designed to be used in 'R Shiny' apps and 'R Markddown' files.
  Due to an outdated 'webkit' version of 'RStudio' it won't work in the integrated viewer.
Helps to render interlinear glossed linguistic examples in html 
    'rmarkdown' documents and then semi-automatically compiles the list of
    glosses at the end of the document. It also provides a database of linguistic
    glosses.
Measure similarity between texts. Offers a variety of processing
  tools and similarity metrics to facilitate flexible representation of texts and matching.
  Implements forms of Language Style Matching (Ireland & Pennebaker, 2010) <doi:10.1037/a0020386>
  and Latent Semantic Analysis (Landauer & Dumais, 1997) <doi:10.1037/0033-295X.104.2.211>.
Provides R with the Glottolog database <https://glottolog.org/> and some more abilities for purposes of linguistic mapping. The Glottolog database contains the catalogue of languages of the world. This package helps researchers to make a linguistic maps, using philosophy of the Cross-Linguistic Linked Data project <https://clld.org/>, which allows for while at the same time facilitating uniform access to the data across publications. A tutorial for this package is available on GitHub pages <https://docs.ropensci.org/lingtypology/> and package vignette. Maps created by this package can be used both for the investigation and linguistic teaching. In addition, package provides an ability to download data from typological databases such as WALS, AUTOTYP and some others and to create your own database website.
Provides 'Shiny gadgets' to search, type, and insert IPA symbols
    into documents or scripts, requiring only knowledge about phonetics or 
    'X-SAMPA'. Also provides functions to facilitate the rendering of IPA
    symbols in 'LaTeX' and PDF format, making IPA symbols properly rendered
    in all output formats. A minimal R Markdown template for authoring 
    Linguistics related documents is also bundled with the package. Some
    helper functions to facilitate authoring with R Markdown is also provided.
Functions to simplify the linking of open source GIS and remote sensing related command line interfaces.
It allows to cluster communication networks using the Stochastic
  Topic Block Model <doi:10.1007/s11222-016-9713-7> by posting jobs through 
  the API of the linkage.fr server, which implements the clustering method.
  The package also allows to visualize the clustering results returned by the server.
Produces high resolution, publication ready linkage maps
    and quantitative trait loci maps. Input can be output from 'R/qtl',
    simple text or comma delimited files. Output is currently
    a portable document file.
Link communities reveal the nested and overlapping
    structure in networks, and uncover the key nodes that form connections
    to multiple communities. linkcomm provides a set of tools for
    generating, visualizing, and analysing link communities in networks of
    arbitrary size and type. The linkcomm package also includes tools for
    generating, visualizing, and analysing Overlapping Cluster Generator
    (OCG) communities. Kalinka and Tomancak (2011) <doi:10.1093/bioinformatics/btr311>.
Prototypes for construction of a Gaussian Stochastic Process emulator (GASP) of a computer model. This is done within the objective Bayesian implementation of the GASP. The package allows for construction of a linked GASP of the composite computer model. Computational implementation follows the mathematical exposition given in publication: Ksenia N. Kyzyurova, James O. Berger, Robert L. Wolpert. Coupling computer models through linking their statistical emulators. SIAM/ASA Journal on Uncertainty Quantification, 6(3): 1151-1171, (2018).<DOI:10.1137/17M1157702>.
Collect  marketing data from 'LinkedIn' Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
A class that links matrix-like objects (nodes) by rows or by
    columns while behaving similarly to a base R matrix. Very large matrices
    are supported if the nodes are file-backed matrices.
Implementations of most of the existing proximity-based methods of 
  link prediction in graphs. Among the 20 implemented methods are e.g.:
  Adamic L. and Adar E. (2003) <doi:10.1016/S0378-8733(03)00009-1>,
  Leicht E., Holme P., Newman M. (2006) <doi:10.1103/PhysRevE.73.026120>,
  Zhou T. and Zhang Y (2009) <doi:10.1140/epjb/e2009-00335-8>, and
  Fouss F., Pirotte A., Renders J., and Saerens M. (2007) <doi:10.1109/TKDE.2007.46>.
Compute and visualize using the 'visNetwork' package all the bivariate correlations of a dataframe.
  Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau,
  distance correlation, maximal information coefficient and 
  equal-freq discretization-based maximal normalized mutual information) are used according to 
  the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).
A 'LaTeX' Letter class for 'rmarkdown', using the
 'pandoc-letter' template adapted for use with 'markdown'.
Conveniently generate 'CSS' using R code.
Generate concentration-time profiles from linear pharmacokinetic
  (PK) systems, possibly with first-order absorption or zero-order infusion,
  possibly with one or more peripheral compartments, and possibly under
  steady-state conditions. Single or multiple doses may be specified. Secondary
  (derived) PK parameters (e.g. Cmax, Ctrough, AUC, Tmax, half-life, etc.) are
  computed.
Can be used to solve Linear Programming / Linear
   Optimization problems by using the simplex algorithm.
Interactive visualization of effects, response functions 
    and marginal effects for different kinds of regression models. In this version 
    linear regression models, generalized linear models, generalized additive
    models and linear mixed-effects models are supported.  
    Major features are the interactive approach and the handling of the effects of categorical covariates: 
    if two or more factors are used as covariates every combination of the levels of each 
    factor is treated separately. The automatic calculation of 
    marginal effects and a number of possibilities to customize the graphical output 
    are useful features as well.
Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators,
  following the method developed by Y. Baraud, C. Giraud and S. Huet (2014) <doi:10.1214/13-AIHP539>.
  In particular it solves the problem of variable selection by choosing the best predictor among predictors emanating from different methods as lasso,
  elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.