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);
    the link is provided in the URL field below.
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.
Fitting the regularization path of the L1-regularized multi-state models since they can exploit sparsity structure of input. Different tuning regularization parameter methods are provided. The cumulative hazard rate estimation and the transition probability predictions can be made from the fitted models.
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.
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.
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.
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.
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 2021, as recorded in the 2022
    version of the database. Documentation examples show how many baseball
    questions can be investigated.
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.
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.
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.
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.
Provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes.
Measurements of leaf area are important in the studies of plant biological characteristics. High-throughput plant phenotyping using image analysis is the key area in the domain of plant phenotyping. For determining the leaf area, the RGB image is converted into the grayscale image by simply averaging the Red(R), Green (G) and Blue (B) pixel values. Grayscale image is then converted into a binary image using Otsu’s thresholding method Otsu, N. (1979) <doi:10.1109/TSMC.1979.4310076> to separate plant area from the background (image segmentation). The segmentation process was accomplished by selecting the pixels with values over the threshold value belonging to the plant region and other pixels to the background region. The resulting binary image consists of white and black pixels representing the plant and background regions, respectively. Finally, the number of pixels inside the plant region was counted and converted to square centimetres (cm2) using the reference object (any object whose actual area is known previously) to get the projected leaf area.
Functions to write or append a R list to a file, as well as read, remove, modify elements from it without restoring the whole list.
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.
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'.
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.
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.
Fit structural equation models (SEM) including factor analysis,
    multivariate regression models with latent variables and many other latent
    variable models while correcting estimates, standard errors, and
    chi-square-derived fit measures for a complex sampling design. 
    Incorporate clustering, stratification, sampling weights, and 
    finite population corrections into a SEM analysis.
    Wrapper around packages lavaan and survey.
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.
Simplifies the whole process of creating stacked tilted maps, that are often used in scientific publications to show different environmental layers for a geographical region. Tilting maps and layering them allows to easily draw visual correlations between these environmental layers.
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.
Provides an R wrapper for an efficient C++ implementation of the Lazy-Greedy spanning algorithm of Bouts, ten Brink, and Buchin (2014) <DOI:10.1145/2582112.2582154>.
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>.
The core of this 'Rcpp' based package is several functions to estimate the baseline hazard, frailty variance, and fixed effect parameter for a discrete-time shared frailty model with random effects. The functions are designed to analyze grouped time-to-event data accounting for family structure of related individuals (i.e., trios). The core functions include two processes: (1) evaluate the multivariable integration to compute the exact proportional hazards model based likelihood and (2) estimate the desired parameters using maximum likelihood estimation. The integration is evaluated by the 'Cuhre' algorithm from the 'Cuba' library (Hahn, T., Cuba-a library for multidimensional numerical integration, Comput. Phys. Commun. 168, 2005, 78-95 <doi:10.1016/j.cpc.2005.01.010>), and the source files of the 'Cuhre' function 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, R.,Algorithms for Minimization without Derivatives, Dover, 2002, ISBN 0-486-41998-3).
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>).
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>.
Tools for working with vectors and data sets that are too large
    to keep in memory. Extends the basic functionality provided in the 'lvec'
    package. Provides basis statistical functionality of 'lvec' objects, such
    as arithmetic operations and calculating means and sums. Also implements
    'data.frame'-like objects storing its data in 'lvec' objects.
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).
Produces a graphical display, as a heat map, of measures
        of pairwise linkage disequilibria between single nucleotide polymorphisms (SNPs). Users may
        optionally include the physical locations or genetic map
        distances of each SNP on the plot. The methods are described in 
        Shin et al. (2006) <doi:10.18637/jss.v016.c03>. Users should note that the
        imported package 'snpStats' and the suggested packages 
        'rtracklayer', 'GenomicRanges', 'GenomInfoDb' and 'IRanges' are all
        BioConductor packages <https://bioconductor.org>.
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.nci.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>.
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>.
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.
Extends Leaflet for R by adding widget to control opacity of multiple layers.
Extends the 'leaflet' R package with the 'Leaflet.OpacityControls' JavaScript 
    plugin. Adds controls to the leaflet map for adjusting the 
    opacity of a layer.
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.
Advances in sequencing technology now allow researchers to capture the expression profiles of individual cells. Several algorithms have been developed to attempt to account for these effects by determining a cell's so-called `pseudotime', or relative biological state of transition. By applying these algorithms to single-cell sequencing data, we can sort cells into their pseudotemporal ordering based on gene expression. LEAP (Lag-based Expression Association for Pseudotime-series) then applies a time-series inspired lag-based correlation analysis to reveal linearly dependent genetic associations.
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 45,000 contemporary and historical politicians from ten 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/>).
Designed to query Longitudinal Employer-Household Dynamics (LEHD) 
    workplace/residential association and origin-destination flat files and 
    optionally aggregate Census block-level data to block group, tract, county, 
    or state. Data comes from the LODES FTP server <https://lehd.ces.census.gov/data/lodes/LODES7/>.
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>.
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.
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.
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 several or more 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, analysis of variance 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 of R markdown instructions for interpretative output, and interactive visualizations.
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.
Handling, processing, and analyzing geographic
    data on species' distributions and environmental variables. 
    Read Vilela & Villalobos (2015) <doi: 10.1111/2041-210X.12401> for details.
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>.
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.
Solving the linear SVM problem with coordinate descent
    is very efficient and is implemented in one of the most often used packages,
    'LIBLINEAR' (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
    It has been shown that the uniform selection of coordinates can be
    accelerated by using an online adaptation of coordinate frequencies (ACF).
    This package implements ACF and is based on 'LIBLINEAR' as well as
    the 'LiblineaR' package (<https://cran.r-project.org/package=LiblineaR>).
    It currently supports L2-regularized L1-loss as well as L2-loss linear SVM.
    Similar to 'LIBLINEAR' multi-class classification (one-vs-the rest, and
    Crammer & Singer method) and cross validation for model selection is
    supported. The training of the models based on ACF is much faster than
    standard 'LIBLINEAR' on many problems.
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.
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.
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.
Compute the top decile lift and plot the lift curve. Cumulative lift curves are also supported.
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 http://daattali.com/shiny/lightsout/.
Performs locally induced approximate GP regression for large computer experiments and spatial datasets following Cole D.A., Christianson, R., Gramacy, R.B. (2021) Statistics and Computing, 31(3), 1-21, <arXiv:2008.12857>. The approximation is based on small local designs combined with a set of inducing points (latent design points) for predictions at particular inputs. Parallelization is supported for generating predictions over an immense out-of-sample testing set. Local optimization of the inducing points design is provided based on variance-based criteria. Inducing point template schemes, including scaling of space-filling designs, are also provided.
Tools for maximum likelihood estimation of parameters 
  of scientific models.
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.
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.
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.
It is a linear algorithm to simulate quantiles of multiscale statistics 
            under hull hypothesis for multiscale change-point segmentation. 
            The reference is in preparation.
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.
Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), 
    Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution,  
    projecting a vector on the convex polytope described by a system of (in)equations, 
    simplify systems by removing spurious columns and rows and collapse implied equalities, 
    test if a matrix is totally unimodular, compute variable ranges implied by linear
    (in)equalities.
Checks adherence to a given style, syntax errors and possible
    semantic issues.  Supports on the fly checking of R code edited with
    'RStudio IDE', 'Emacs', 'Vim', 'Sublime Text', 'Atom' and 'Visual
    Studio Code'.
Lipid Maps Rest service. Researchers can access the Lipid Maps Rest
  service programmatically and conveniently integrate it into the current workflow
  or packages.
Lipid annotation in untargeted LC-MS lipidomics based on fragmentation rules. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) <doi:10.1021/acs.analchem.8b03409>.
Create lipidome-wide heatmaps of statistics with the 'lipidomeR'.
    The 'lipidomeR' provides a streamlined pipeline for the systematic 
    interpretation of the lipidome through publication-ready visualizations 
    of regression models fitted on lipidomics data. 
    With 'lipidomeR', associations between covariates and the lipidome 
    can be interpreted systematically and intuitively through heatmaps, where 
    lipids are categorized by the lipid class and are presented on 
    two-dimensional maps organized by the lipid size and level of saturation. 
    This way, the 'lipidomeR' helps you gain an immediate understanding of 
    the multivariate patterns in the lipidome already at first glance.
    You can create lipidome-wide heatmaps of statistical associations, changes, 
    differences, variation, or other lipid-specific values.  
    The heatmaps are provided with publication-ready quality and the results 
    behind the visualizations are based on rigorous statistical models.
Provides three classes: Queue, PriorityQueue and Stack. Queue is just a
    "plain vanilla" FIFO queue; PriorityQueue orders items according to priority. Stack implements LIFO.
Performs Bayesian linear regression and forecasting in astronomy. The method accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables) and scatter correlation, time evolution of slopes, normalization, scatters, Malmquist and Eddington bias, upper limits and break of linearity. The posterior distribution of the regression parameters is sampled with a Gibbs method exploiting the JAGS library.
Contains 128 palettes from Color Lisa. All palettes are based on 
    masterpieces from the worlds greatest artists. For more information, see 
    <http://colorlisa.com/>.
This is an unofficial package aimed at automating the import of 
            'LISREL' <https://ssicentral.com/index.php/products/lisrel/> output 
            in R. This package or its maintainer is not in any way affiliated with 
            the creators of 'LISREL' and 'SSI, Inc'.
Allows researchers to conduct multivariate
    statistical analyses of survey data with list experiments. This
    survey methodology is also known as the item count technique or
    the unmatched count technique and is an alternative to the commonly
    used randomized response method. The package implements the methods
    developed by Imai (2011) <doi:10.1198/jasa.2011.ap10415>, 
    Blair and Imai (2012) <doi:10.1093/pan/mpr048>, 
    Blair, Imai, and Lyall (2013) <doi:10.1111/ajps.12086>, 
    Imai, Park, and Greene (2014) <doi:10.1093/pan/mpu017>,
    Aronow, Coppock, Crawford, and Green (2015) <doi:10.1093/jssam/smu023>, 
    Chou, Imai, and Rosenfeld (2017) <doi:10.1177/0049124117729711>, and 
    Blair, Chou, and Imai (2018) <https://imai.fas.harvard.edu/research/files/listerror.pdf>. 
    This includes a Bayesian MCMC implementation of regression for the 
    standard and multiple sensitive item list experiment designs and a 
    random effects setup, a Bayesian MCMC hierarchical regression model 
    with up to three hierarchical groups, the combined list experiment 
    and endorsement experiment regression model, a joint model of the 
    list experiment that enables the analysis of the list experiment as 
    a predictor in outcome regression models, a method for combining 
    list experiments with direct questions, and methods for diagnosing and
    adjusting for response error. In addition, the package implements the 
    statistical test that is designed to detect certain failures of list 
    experiments, and a placebo test for the list experiment using data from 
    direct questions.
The aim of the package is to create data objects which allow for accesses like x["test"] and x["test","test"].
A toolbox for R arrays. Flexibly split, bind, reshape, modify, 
    subset and name arrays.
An implementation of list comprehensions as purely syntactic
  sugar with a minor runtime overhead. It constructs nested for-loops and
  executes the byte-compiled loops to collect the results.
Syntactic shortcuts for creating synthetic lists, vectors, 
    data frames, and matrices using list comprehension.
Programmatically create R Markdown documents from lists.
List environments are environments that have list-like properties.  For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a = 1, b = 2); for (i in seq_along(x)) x[[i]] <- x[[i]] ^ 2; y <- as.list(x)'.
Tests for independence between X and Y computed from a paired sample (x1,y1),...(xn,yn) of (X,Y), using one of the following statistics (a) the Longest Increasing Subsequence (Ln), (b) JLn, a Jackknife version of Ln or (c) JLMn, a Jackknife version of the longest monotonic subsequence. This family of tests can be applied under the assumption of continuity of X and Y.
Tools for common operations on lists. Provided are short-cuts to
    operations like selecting and merging data stored in lists. The functions in
    this package are designed to be used with pipes.
R lists, especially nested lists, can be very difficult to
    visualize or represent. Sometimes 'str()' is not enough, so this suite of
    htmlwidgets is designed to help see, understand, and maybe even modify your R
    lists.  The function 'reactjson()' requires a package
    'reactR' that can be installed from CRAN or <https://github.com/timelyportfolio/reactR>.
Provides a function that, as an alternative to base::list, allows
    default values to be inherited from another list.
Performs likelihood-based inference for stationary time series 
    extremes.  The general approach follows Fawcett and Walshaw (2012)
    <doi:10.1002/env.2133>.  Marginal extreme value inferences are adjusted for 
    cluster dependence in the data using the methodology in Chandler and Bate 
    (2007) <doi:10.1093/biomet/asm015>, producing an adjusted log-likelihood 
    for the model parameters.  A log-likelihood for the extremal index is 
    produced using the K-gaps model of Suveges and Davison (2010) 
    <doi:10.1214/09-AOAS292>. These log-likelihoods are combined to make 
    inferences about return levels.
Temporary and permanent message queues for R. Built on top of
    'SQLite' databases. 'SQLite' provides locking, and makes it possible
    to detect crashed consumers. Crashed jobs can be automatically marked
    as "failed", or put in the queue again, potentially a limited number of times.
Dataset and functions to explore quality of literary novels. The package is a part of the Riddle of Literary Quality project, and it contains the data of a reader survey about fiction in Dutch, a description of the novels the readers rated, and the results of stylistic measurements of the novels. The package also contains functions to combine, analyze, and visualize these data.
Data sets on various litter types like beach litter, riverain
    litter, floating litter, and seafloor litter are rapidly growing. This 
    package offers a simple user interface to analyse these litter data in
    a consistent and reproducible way. It also provides functions to 
    facilitate several kinds of litter analysis, e.g., trend analysis, 
    power analysis, and baseline analysis. Under the hood, these functions 
    are also used by the user interface. See Schulz et al. (2019)
    <doi:10.1016/j.envpol.2019.02.030> for details. MS-Windows users are
    advised to run 'litteR' in 'RStudio'. See our vignette: Installation manual 
    for 'RStudio' and 'litteR'.
There is a long tradition of studying the flux of carbon from the biosphere to the atmosphere by following a particular cohort of litter (wood, leaves, roots, or other organic material) through time.  The resulting data are mass remaining and time. A variety of functional forms may be used to fit the resulting data. Some work better empirically. Some are better connected to a process-based understanding.  Some have a small number of free parameters; others have more. This package matches decomposition data to a family of these curves using likelihood--based fitting. This package is based on published research by Cornwell & Weedon (2013) <doi:10.1111/2041-210X.12138>.
A scripting and command-line front-end
 is provided by 'r' (aka 'littler') as a lightweight binary wrapper around
 the GNU R language and environment for statistical computing and graphics.
 While R can be used in batch mode, the r binary adds full support for
 both 'shebang'-style scripting (i.e. using a  hash-mark-exclamation-path
 expression as the first line in scripts) as well as command-line use in
 standard Unix pipelines. In other words, r provides the R language without
 the environment.
Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Akdeniz and Kaciranlar (1995) <doi:10.1080/03610929508831585>
  ii.  Druilhet and Mom (2008) <doi:10.1016/j.jmva.2006.06.011>
  iii. Imdadullah, Aslam, and Saima (2017)
  iv.  Liu (1993) <doi:10.1080/03610929308831027>
  v.   Liu (2001) <doi:10.1016/j.jspi.2010.05.030>.
Interpretability of complex machine learning models is a growing concern.
    This package helps to understand key factors that drive the 
    decision made by complicated predictive model (so called black box model). 
    This is achieved through local approximations that are either based on 
    additive regression like model or CART like model that allows for 
    higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>.
    More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.
Provides a wrapper around LiveChat's API. The R functions allow for
    one to extract chat sessions, raw text of chats between agents and customers and
    events.
Provides a collection of helper functions that make various techniques from data science more user-friendly for non-experts. In this way, our aim is to allow non-experts to become familiar with the techniques with only a minimal level of coding knowledge. Indeed, following an ancient Persian idiom, we refer to this as "eating the liver of data science" which could be interpreted as "getting intimately close with data science". Examples of procedures we include are: data partitioning for out-of-sample testing, computing Mean Squared Error (MSE) for quantifying prediction accuracy, and data transformation (z-score and min-max). Besides such helper functions, the package also includes several interesting datasets that are useful for multivariate analysis.
Fits and tests logistic joinpoint models.
Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) <https://ieeexplore.ieee.org/document/9616435>. 'LKT' is a method to compute features of student data that are used as predictors of subsequent performance. 'LKT' allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of 'LiblineaR', which enables extremely fast solutions compared to base glm() in R.
Provides functionality to train and evaluate algorithm selection models for portfolios.
R functions and data sets for the work Paz, R.F., Balakrishnan, N and Bazán, J.L. (2018). L-logistic regression models: Prior sensitivity analysis, robustness to outliers and applications. Brazilian Journal of Probability and Statistics, <https://www.imstat.org/wp-content/uploads/2018/05/BJPS397.pdf>.
Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) <DOI:10.1016/j.ejor.2018.02.009>).
Density, distribution function, quantile function and random generation for the L-Logistic distribution with parameters m and phi. The parameter m is the median of the distribution.
Originally design to characterise Aqueous Two Phase Systems, LLSR provide a simple way to analyse experimental data and obtain phase diagram parameters, among other properties, systematically. The package will include (every other update) new functions in order to comprise useful tools in liquid-liquid extraction research.
Adds standardized regression coefficients to objects created by lm. Also extends the S3 methods print, summary and coef with additional boolean argument standardized.
Exact significance tests for a changepoint in linear or multiple linear regression.  
  Confidence regions with exact coverage probabilities for the changepoint.  Based on 
  Knowles, Siegmund and Zhang (1991) <doi:10.1093/biomet/78.1.15>.
Set of tools for mapping of categorical response variables based on principal component analysis (pca) and multidimensional unfolding (mdu).
Various efficient and robust bootstrap methods are implemented for 
    linear models with least squares estimation.  Functions within this package 
    allow users to create bootstrap sampling distributions for model parameters, 
    test hypotheses about parameters, and visualize the bootstrap sampling or null 
    distributions.  Methods implemented for linear models include the wild bootstrap by 
    Wu (1986) <doi:10.1214/aos/1176350142>, the residual and paired bootstraps by
    Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by 
    Quenouille (1956) <doi:10.2307/2332914>, and the Bayesian bootstrap by 
    Rubin (1981) <doi:10.1214/aos/1176345338>.
Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) <doi:10.1098/rsif.2005.0058> and the 'Python' library 'PyLMD'.
Several service functions to be used to analyse datasets obtained from diallel experiments within the frame of linear models in R, as described in  Onofri et al (2020) <DOI:10.1007/s00122-020-03716-8>.
A fast dimensionality reduction method scaleable to large numbers of samples.
  Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS,
  but rather than calculating a complete distance matrix between all pairs of samples,
  only the distances between a set of landmarks and the samples are calculated.
Fit linear and generalized linear mixed-effects models.
    The models and their components are represented using S4 classes and
    methods.  The core computational algorithms are implemented using the
    'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
Provides analytic derivatives and information matrices for
    fitted linear mixed effects (lme) models and generalized least squares (gls) models
    estimated using lme() (from package 'nlme') and gls() (from package 'nlme'), respectively.
    The package includes functions for estimating the sampling variance-covariance of variance
    component parameters using the inverse Fisher information. The variance components include
    the parameters of the random effects structure (for lme models), the variance structure,
    and the correlation structure. The expected and average forms of the Fisher information matrix
    are used in the calculations, and models estimated by full maximum likelihood or
    restricted maximum likelihood are supported. The package also includes a function for estimating
    standardized mean difference effect sizes (Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>)
    based on fitted lme or gls models.
The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.
Bootstrap routines for nested linear mixed effects models fit using
    either 'lme4' or 'nlme'. The provided 'bootstrap()' function implements the
    parametric, residual, cases, random effect block (REB), and wild bootstrap 
    procedures. An overview of these procedures can be found 
    in Van der Leeden et al. (2008) <doi: 10.1007/978-0-387-73186-5_11>, 
    Carpenter, Goldstein & Rasbash (2003) <doi: 10.1111/1467-9876.00415>,
    and Chambers & Chandra (2013) <doi: 10.1080/10618600.2012.681216>.
Provides p-values in type I, II or III anova and summary tables
    for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A
    Kenward-Roger method is also available via the pbkrtest package. Model selection
    methods include step, drop1 and anova-like tables for random effects (ranova).
    Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed
    effects are also available.
Adds smoothing spline modelling capability to nlme. Fits
        smoothing spline terms in Gaussian linear and nonlinear
        mixed-effects models.
Latent Markov models for longitudinal continuous and categorical data. See Bartolucci, Pandolfi, Pennoni (2017)<doi:10.18637/jss.v081.i04>.
Provides methods for estimation and statistical
               inference on directional and fluctuating selection in age-structured
               populations.
We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.
Functions for different purposes related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, modelling of diameter distributions and estimation off stand density using ITD. Several empirical datasets are also included.
Introduces in-sample, out-of-sample, pseudo out-of-sample, and
    benchmark linear model forecast tests and a new class for working with 
    forecast data: Forecast.
The Gaussian location-scale regression model is a multi-predictor
    model with explanatory variables for the mean (= location) and the standard
    deviation (= scale) of a response variable. This package implements maximum
    likelihood and Markov chain Monte Carlo (MCMC) inference (using algorithms
    from Girolami and Calderhead (2011) <doi:10.1111/j.1467-9868.2010.00765.x>
    and Nesterov (2009) <doi:10.1007/s10107-007-0149-x>), a parametric
    bootstrap algorithm, and diagnostic plots for the model class.
It implements Expectation/Conditional Maximization Either (ECME)
             and rapidly converging algorithms as well as
             Bayesian inference for linear mixed models, 
             which is described in Schafer, J.L. (1998)
             "Some improved procedures for linear mixed models".
             Dept. of Statistics, The Pennsylvania State University.
Fit right censored Multiple Ordinal Tobit (MOT) model.
Embarrassingly Parallel Linear Mixed Model calculations spread across local cores which repeat until convergence.
An efficient and flexible system to solve sparse mixed model
    equations, for models that are often used in statistical genetics. 
    Important applications are the use of splines to model spatial or temporal
    trends. Another application area is mixed model QTL analysis for 
    multiparental populations, allowing for heterogeneous residual variance and
    random design matrices with Identity-By-Descent (IBD) probabilities.
Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers"
	     taught by the section of Biostatistics of the University of Copenhagen.
	     It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, unstructured, ...).
	     Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite degrees of freedom.
	     Normalized residuals are provided to assess model misspecification.
	     Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients.
	     Predictions can be computed conditional to covariates only or also to outcome values.
Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model.  Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.
Computes model II simple linear regression using ordinary
 least squares (OLS), major axis (MA), standard major axis (SMA), and
 ranged major axis (RMA).
A complete framework for frequency analysis is provided by 'LMoFit'. It has functions related to the determination of sample L-moments as in Hosking, J.R.M. (1990) <doi:10.1111/j.2517-6161.1990.tb01775.x>, the fitting of various distributions as in Zaghloul et al. (2020) <doi:10.1016/j.advwatres.2020.103720> and Hosking, J.R.M. (2019) <https://CRAN.R-project.org/package=lmom>, besides plotting and manipulating L-space diagrams as in Papalexiou, S.M. & Koutsoyiannis, D. (2016) <doi:10.1016/j.advwatres.2016.05.005> for two-shape parametric distributions on the L-moment ratio diagram. Additionally, the quantile, probability density, and cumulative probability functions of various distributions are provided in a user-friendly manner.
Functions related to L-moments: computation of L-moments
  and trimmed L-moments of distributions and data samples; parameter
  estimation; L-moment ratio diagram; plot vs. quantiles of an
  extreme-value distribution.
Extensive functions for L-moments (LMs) and probability-weighted moments
  (PWMs), parameter estimation for distributions, LM computation for distributions, and
  L-moment ratio diagrams. Maximum likelihood and maximum product of spacings estimation
  are also available. LMs for right-tail and left-tail censoring by known or unknown threshold
  and by indicator variable are available. Asymmetric (asy) trimmed LMs (TL-moments, TLMs) are
  supported. LMs of residual (resid) and reversed (rev) residual life are implemented along
  with 13 quantile function operators for reliability and survival analyses. Exact analytical
  bootstrap estimates of order statistics, LMs, and variances-covariances of LMs are provided.
  The Harri-Coble Tau34-squared Normality Test is available. Distribution support with
  "L" (LMs), "TL" (TLMs) and added (+) support for right-tail censoring (RC) encompasses:
  Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L],
  Gamma [L], Generalized (Gen) Exp Poisson [L], Gen Extreme Value [L], Gen Lambda [L,TL],
  Gen Logistic [L), Gen Normal [L], Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L],
  Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile
  Function [L], Normal [L], 3-p log-Normal [L], Pearson Type III [L], Polynomial Density-
  Quantile 3 and 4[L], Rayleigh [L], Rev-Gumbel [L+RC], Rice/Rician [L], Slash [TL],
  3-p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L]. Multivariate
  sample L-comoments (LCMs) are implemented to measure asymmetric associations.
Contains functions to estimate
        L-moments and trimmed L-moments from the data. Also
        contains functions to estimate the parameters of the normal
        polynomial quantile mixture and the Cauchy polynomial quantile
        mixture from L-moments and trimmed L-moments.
Functions for regional frequency analysis using the methods
  of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis:
  an approach based on L-moments".
Linear model functions using permutation tests.
Implementation based on Zhang, Jie & Huang, Kun (2014) <doi:10.4137/CIN.S14021> Normalized ImQCM: An Algorithm for Detecting Weak Quasi-Cliques in Weighted Graph with Applications in Gene Co-Expression Module Discovery in Cancers. Cancer informatics, 13, CIN-S14021.
Data files and a few functions used in the book 
             'Linear Models and Regression with R: An Integrated Approach' 
             by Debasis Sengupta and Sreenivas Rao Jammalamadaka (2019).
Linear ridge regression coefficient's estimation and testing with different ridge related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Hoerl and Kennard (1970) <doi:10.1080/00401706.1970.10488634>,
  ii.  Halawa and El-Bassiouni (2000) <doi:10.1080/00949650008812006>,
  iii. Imdadullah, Aslam, and Saima (2017),
  iv.  Marquardt (1970) <doi:10.2307/1267205>.
Exact and approximation algorithms for variable-subset
  selection in ordinary linear regression models.  Either compute all
  submodels with the lowest residual sum of squares, or determine the
  single-best submodel according to a pre-determined statistical
  criterion.  Hofmann et al. (2020) <doi:10.18637/jss.v093.i03>.
A collection of tests, data sets, and examples
 for diagnostic checking in linear regression models. Furthermore,
 some generic tools for inference in parametric models are provided.
Non-parametric estimators for casual effects based on longitudinal modified treatment 
  policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, 
  and traditional longitudinal effects. Continuous, binary, and categorical treatments are allowed as well are 
  censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure 
  irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects 
  can be calculated and relative risks and odds ratios may be calculated for binary outcomes.
Runs a linear-like regression with in which both the expected value and the variance can vary per observation. The expected values mu follows the standard linear model mu = X_mu * beta_mu. The standard deviation sigma follows the model log(sigma) = X_sigma * beta_sigma. The package comes with two vignettes: 'Intro' gives an introduction, 'Math' gives mathematical details.
Contains a suite of shiny applications
    meant to explore linear model inference feature through simulation
    and games.
We developed an approach to detect differential expression features in long non-coding RNA low counts, using generalized linear model with zero-inflated exponential quasi likelihood ratio test. Methods implemented in this package are described in Li (2019) <doi:10.1186/s12864-019-5926-4>.
Long non-coding RNAs identification and analysis. Default models are trained with human, mouse and wheat datasets by employing SVM. Features are based on intrinsic composition of sequence, EIIP value (electron-ion interaction pseudopotential), and secondary structure. This package can also extract other classic features and build new classifiers. Reference: Han SY., Liang YC., Li Y., et al. (2018) <doi:10.1093/bib/bby065>.
Identifies pathways synergisticly regulated by the interested lncRNA(long non-coding RNA) sets based on a lncRNA-mRNA(messenger RNA) interaction network. 1) The lncRNA-mRNA interaction network was built from the protein-protein interactions and the lncRNA-mRNA co-expression relationships in 28 RNA-Seq data sets. 2) The interested lncRNAs can be mapped into networks as seed nodes and a random walk strategy will be performed to evaluate the rate of each coding genes influenced by the seed lncRNAs. 3) Pathways regulated by the lncRNA set will be evaluated by a weighted Kolmogorov-Smirnov statistic as an ES Score. 4) The p value and false discovery rate value will also be calculated through a permutation analysis. 5) The running score of each pathway can be plotted and the heat map of each pathway can also be plotted if an expression profile is provided. 6) The rank and scores of the gene list of each pathway can be printed.
Allows the simultaneous analysis of responses and response times in an Item Response Theory (IRT) modelling framework. Supports variable person speed functions (intercept, trend, quadratic), and covariates for item and person (random) parameters. Data missing-by-design can be specified. Parameter estimation is done with a MCMC algorithm. LNIRT replaces the package CIRT, which was written by Rinke Klein Entink. For reference, see the paper by Fox, Klein Entink and Van der Linden (2007), "Modeling of Responses and Response Times with the Package cirt", Journal of Statistical Software, <doi:10.18637/jss.v020.i07>.
An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) <arXiv:2011.06682>, and is designed for clustering compositional data. The package includes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM Bicluster Mixture Model (LNM-BMM) and Penalized LNM Factor Analyzer (LNM-FA). There are several advantages of LNM models: 1. LNM provides more flexible covariance structure; 2. Factor analyzer can reduce the number of parameters to estimate; 3. Bicluster can simultaneously cluster subjects and taxa, and provides significant biological insights; 4. Penalty term allows sparse estimation in the covariance matrix. Details for model assumptions and interpretation can be found in papers: Tu and Subedi (2021) <arXiv:2101.01871> and Tu and Subedi (2022) <doi:10.1002/sam.11555>.
Various plots and functions that make use of the lattice/trellis plotting framework. 
   The plots, which include loaPlot(), RgoogleMapsPlot() and trianglePlot(), use panelPal(), a function that 
   extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel 
   and panel-to-key synchronization/management.
A collection of functions created to study water discharge
    (Q) and suspended sediment concentration (SSC) relationship.
Computing principal component loading (Yamamoto, H. et al. (2014) <doi:10.1186/1471-2105-15-51> and Yamamoto, H. et al. (2021) <doi:10.3390/metabo11030149>), partial least squares loading (Yamamoto, H. (2017) <doi:10.1002/cem.2883> and Yamamoto, H. (2022) <doi:10.1101/2022.08.30.505949>) and their statistical hypothesis testing.
Provides intuitive functions for loading objects into
    environments, encouraging less cluttered workspaces and sharing variables
    with large or reusable data across users and sessions. The user
    provides named variables which are loaded into the variable environment for
    later retrieval.
Modifying a load shape to match specific peak and 
  load factor is a fundamental component for various power system 
  planning and operation studies. This package is an efficient tool 
  to modify a reference load shape while matching the desired peak
  and load factor. The package offers both linear and non-linear method,
  described in <https://rpubs.com/riazakhan94/load_shape_match_peak_energy>. 
  The user can control the shape of the final load shape by regulating 
  certain parameters. The package provides validation metrics for 
  assessing the derived load shape in terms of preserving time series 
  properties. It also offers powerful graphics, that allows the user to
  visually assess the derived load shape.
Automated analysis and modeling of longitudinal 'omics' data (e.g. breath 'metabolomics') using generalized spline mixed effect models. Including automated filtering of noise parameters and determination of breakpoints.
A set of tools for inspecting and understanding R data
    structures inspired by str(). Includes ast() for visualizing abstract
    syntax trees, ref() for showing shared references, cst() for showing
    call stack trees, and obj_size() for computing object sizes.
Implements novel nonparametric approaches to address
    biases and confounding when comparing treatments or exposures in
    observational studies of outcomes. While designed and appropriate for use
    in studies involving medicine and the life sciences, the package can be
    used in other situations involving outcomes with multiple confounders.
    The package implements a family of methods for non-parametric bias correction
    when comparing treatments in observational studies, including survival
    analysis settings, where competing risks and/or censoring may be present.
    The approach extends to bias-corrected personalized predictions of treatment
    outcome differences, and analysis of heterogeneity of treatment effect-sizes
    across patient subgroups. For further details, please see: 
    Lauve NR, Nelson SJ, Young SS, Obenchain RL, Lambert CG. LocalControl:
    An R Package for Comparative Safety and Effectiveness Research.
    Journal of Statistical Software. 2020. p. 1–32. Available from <doi:10.18637/jss.v096.i04>.
Especially when cross-sectional data are observational, effects of treatment
  selection bias and confounding are revealed by using the Nonparametric and Unsupervised 
  "preprocessing" methods central to Local Control (LC) Strategy. The LC objective is
  to estimate the "effect-size distribution" that best quantifies a potentially causal
  relationship between a numeric y-Outcome variable and a t-Treatment or e-Exposure
  variable. Treatment variables are binary {either 1 = "new" or 0 = "control"}, while
  Exposure variables vary continuously over a finite range. LC Strategy starts by
  CLUSTERING experimental units (individual patients, US Counties, etc.) on their
  X-confounder characteristics. Clusters represent exclusive and exhaustive BLOCKS of
  relatively well-matched units. The implicit statistical model for LC is thus simple
  one-way ANOVA. Within-Block measures of effect-size are Local Rank Correlations (LRCs)
  when Exposure is numeric with (many) more than two levels. Otherwise, Treatment choice
  is Nested within BLOCKS, and effect-sizes are LOCAL Treatment Differences (LTDs) between
  Within-Cluster y-Outcome Means ["new" minus "control"]. An Instrumental Variable (IV)
  method is also provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also
  contribute information for effect-size inferences ...assuming that X-Covariates influence
  only Treatment choice or Exposure level and otherwise have no direct effects on y-Outcome.
  Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to
  aid Doctor-Patient or Researcher-Society communications about Heterogeneous Outcomes.
Implementation of a theoretically supported alternative to k-nearest neighbors for functional data 
  to solve problems of estimating unobserved segments of a partially observed functional data sample, 
  functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. 
  Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. 
  The package allows the implementation of the methodology and the replication of the results in Elías, A., Jiménez, R. and Yukich, J. (2020) <arXiv:2007.16059>.
Computational routines for estimating local Gaussian parameters. Local Gaussian parameters are useful for characterizing and testing for non-linear dependence within bivariate data. See e.g. Tjostheim and Hufthammer, Local Gaussian correlation: A new measure of dependence, Journal of Econometrics, 2013, Volume 172 (1), pages 33-48 <DOI:10.1016/j.jeconom.2012.08.001>.
Local Individual Conditional Expectation ('localICE') is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI). localICE is a model-agnostic XAI approach which provides three-dimensional local explanations for particular data instances. The approach is proposed in the master thesis of Martin Walter as an extension to ICE (see Reference). The three dimensions are the two features at the horizontal and vertical axes as well as the target represented by different colors. The approach is applicable for classification and regression problems to explain interactions of two features towards the target. For classification models, the number of classes can be more than two and each class is added as a different color to the plot. The given instance is added to the plot as two dotted lines according to the feature values. The localICE-package can explain features of type factor and numeric of any machine learning model. Automatically supported machine learning packages are 'mlr', 'randomForest', 'caret' or all other with an S3 predict function. For further model types from other libraries, a predict function has to be provided as an argument in order to get access to the model. Reference to the ICE approach: Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin (2013) <arXiv:1309.6392>.
In the generalized Roy model, the marginal treatment effect (MTE) can be used as
  a building block for constructing conventional causal parameters such as the average treatment
  effect (ATE) and the average treatment effect on the treated (ATT). Given a treatment selection
  equation and an outcome equation, the function mte() estimates the MTE via the semiparametric
  local instrumental variables method or the normal selection model. The function mte_at() evaluates
  MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at()
  evaluates MTE projected onto the estimated propensity score. The function ace() estimates
  population-level average causal effects such as ATE, ATT, or the marginal policy relevant
  treatment effect.
Local explanations of machine learning models describe, how features contributed to a single prediction. 
    This package implements an explanation method based on LIME 
    (Local Interpretable Model-agnostic Explanations, 
    see Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>) in which interpretable
    inputs are created based on local rather than global behaviour of each original feature.
Functionalities for calculating the local score and calculating statistical relevance (p-value) to find a local Score in a sequence of given distribution (S. Mercier and J.-J. Daudin (2001) <https://hal.archives-ouvertes.fr/hal-00714174>) ; S. Karlin and S. Altschul (1990) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC53667/>  ; S. Mercier, D. Cellier and F. Charlot (2003) <https://hal.archives-ouvertes.fr/hal-00937529v1> ; A. Lagnoux, S. Mercier and P. Valois (2017) <doi:10.1093/bioinformatics/btw699> ).
The package converts R data onto input and data for LocalSolver,
    executes optimization and exposes optimization results as R data.
    LocalSolver (http://www.localsolver.com/) is an optimization engine
    developed by Innovation24 (http://www.innovation24.fr/). It is designed to
    solve large-scale mixed-variable non-convex optimization problems.  The
    localsolver package is developed and maintained by WLOG Solutions
    (http://www.wlogsolutions.com/en/) in collaboration with Decision Support
    and Analysis Division at Warsaw School of Economics
    (http://www.sgh.waw.pl/en/).
A set of functions to locate some programs 
 available on the user machine. The package provides functions to locate 
 'Node.js', 'npm', 'LibreOffice', 'Microsoft Word', 'Microsoft PowerPoint', 
 'Microsoft Excel', 'Python', 'pip', 'Mozilla Firefox' and 'Google Chrome'.
 User can test the availability of a program with eventually a version 
 and call it with function system2() or system(). This allows the use of 
 a single function to retrieve the path to a program regardless of the 
 operating system and its configuration.
Identification of equilibrium locations in location games (Hotelling (1929) 
    <doi:10.2307/2224214>). In these games, two competing actors place
    customer-serving units in two locations simultaneously. Customers make the 
    decision to visit the location that is closest to them. The functions in 
    this package include Prim algorithm (Prim (1957) 
    <doi:10.1002/j.1538-7305.1957.tb01515.x>) to find the minimum spanning tree 
    connecting all network vertices, an implementation of Dijkstra algorithm 
    (Dijkstra (1959) <doi:10.1007/BF01386390>) to find the shortest distance and 
    path between any two vertices, a self-developed algorithm using elimination 
    of purely dominated strategies to find the equilibrium, and several plotting 
    functions.
Computation of local false discovery rates.
Local regression, likelihood and density estimation methods as described in the 1999 book by Loader.
Provides test of second-order stationarity for time
	series (for dyadic and arbitrary-n length data). Provides
	localized autocovariance, with confidence intervals,
	for locally stationary (nonstationary) time series.
	See Nason, G P (2013) "A test for second-order stationarity and
	approximate confidence intervals for localized autocovariance
	for locally stationary time series." Journal of the Royal Statistical
	Society, Series B, 75, 879-904.  <doi:10.1111/rssb.12015>.
Datasets for the third edition of "Statistics: Unlocking the Power of Data" by Lock^5
    Includes version of datasets from earlier editions.
Data sets and other utilities for 
    'Statistics: Unlocking the Power of Data'
    by Lock, Lock, Lock, Lock and Lock 
    (ISBN : 978-0-470-60187-7, http://lock5stat.com/).
Locally sparse estimator of generalized varying coefficient model for asynchronous longitudinal data by kernel-weighted estimating equation.
Computes local polynomial estimators for 
  the regression and also density. It comprises several 
  different utilities to handle kernel estimators.
Provides the local polynomial expectile regression method and different bandwidth selection procedures. The codes include local polynomial univariate expectile regression with several data-driven methods for bandwidth selection; local linear bivariate and trivariate expectile regression; and partially linear expectile regression, allowing for different errors structures (homoscedastic error and various heteroscedastic error structures). For more details, see Adam and Gijbels (2021a) <doi:10.1007/s10463-021-00799-y> and Adam and Gijbels (2021b) <doi:10.1007/978-3-030-73249-3_8>.
Fast implementations to compute the genetic covariance matrix, the Jaccard similarity matrix, the s-matrix (the weighted Jaccard similarity matrix), and the (classic or robust) genomic relationship matrix of a (dense or sparse) input matrix (see Hahn, Lutz, Hecker, Prokopenko, Cho, Silverman, Weiss, and Lange (2020) <doi:10.1002/gepi.22356>). Full support for sparse matrices from the R-package 'Matrix'. Additionally, an implementation of the power method (von Mises iteration) to compute the largest eigenvector of a matrix is included, a function to perform an automated full run of global and local correlations in population stratification data, a function to compute sliding windows, and a function to invert minor alleles and to select those variants/loci exceeding a minimal cutoff value. New functionality in locStra allows one to extract the k leading eigenvectors of the genetic covariance matrix, Jaccard similarity matrix, s-matrix, and genomic relationship matrix via fast PCA without actually computing the similarity matrices. The fast PCA to compute the k leading eigenvectors can now also be run directly from 'bed'+'bim'+'fam' files.
To decompose symmetric matrices such as brain connectivity matrices so that one can extract sparse latent component matrices and also estimate mixing coefficients, a blind source separation (BSS) method named LOCUS was proposed in Wang and Guo (2023) <arXiv:2008.08915>. For brain connectivity matrices, the outputs correspond to sparse latent connectivity traits and individual-level trait loadings.
Read and write access to PNG image files using the LodePNG
    library. The package has no external dependencies.
Genome-wide association (GWAS) analyses
  of a biomarker that account for the limit of detection.
Impute observed values below the limit of detection (LOD) via
    censored likelihood multiple imputation (CLMI) in single-pollutant
    models, developed by Boss et al (2019) <doi:10.1097/EDE.0000000000001052>.
    CLMI handles exposure detection limits that may change throughout the course
    of exposure assessment. 'lodi' provides functions for imputing and
    pooling for this method.
Tools to fit linear regression model to data while taking into account covariates with lower limit of detection (LOD).
Local Ordinal embedding (LOE) is one of graph embedding methods for unweighted graphs.
Estimate model parameters to determine whether two compounds have synergy, antagonism, or Loewe's Additivity.
Logger to keep track of informational events and errors useful for debugging.
The log4r package is meant to provide a fast, lightweight,
  object-oriented approach to logging in R based on the widely-emulated
  'log4j' system and etymology.
Enables users to handle the dataset cleaning for conducting
    specific analyses with the log files from two international educational
    assessments: the Programme for International Student Assessment (PISA,
    <https://www.oecd.org/pisa/>) and the Programme for the International
    Assessment of Adult Competencies (PIAAC,
    <https://www.oecd.org/skills/piaac/>). An illustration of the analyses can be
    found on the LOGAN Shiny app (<https://loganpackage.shinyapps.io/shiny/>) on
    your browser.
Enables researchers to model log-file data from computer-based assessments using machine-learning techniques. It allows researchers to generate new knowledge by comparing the performance of three tree-based classification models (i.e., decision trees, random forest, and gradient boosting) to predict student's outcome. It also contains a set of handful functions for the analysis of the features' influence on the modeling. Data from the Climate control item from the 2012 Programme for International Student Assessment (PISA, <https://www.oecd.org/pisa/>) is available for an illustration of the package's capability. He, Q., & von Davier, M. (2015) <doi:10.1007/978-3-319-19977-1_13> Boehmke, B., & Greenwell, B. M. (2019) <doi:10.1201/9780367816377> .
Methods for fitting log-link GLMs and GAMs to binomial data,
    including EM-type algorithms with more stable convergence properties than standard methods.
Software for computing a log-concave (maximum likelihood) estimator for i.i.d. data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, <doi:10.1111/j.1467-9868.2010.00753.x>).
Based on right or interval censored data, compute the maximum likelihood estimator of a (sub)probability density under the assumption that it is log-concave. For further information see Duembgen, Rufibach and Schuhmacher (2014) <doi:10.1214/14-EJS930>.
Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.
Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k.
Inspired by the the 'futile.logger' R package and 'logging' Python module, this utility provides a flexible and extensible way of formatting and delivering log messages with low overhead.
Pure R implementation of the ubiquitous log4j package. It offers hierarchic 
  loggers, multiple handlers per logger, level based filtering, space handling in messages 
  and custom formatting.
An effortless 'ndjson' (newline-delimited 'JSON') logger, with two primary
    log-writing interfaces. It provides a set of wrappings for base R's
    message(), warning(), and stop() functions that maintain identical
    functionality, but also log the handler message to an 'ndjson' log file.
    'loggit' also exports its internal 'loggit()' function for powerful and
    configurable custom logging. No change in existing code is necessary to use
    this package, and should only require additions to fully leverage the power
    of the logging system. 'loggit' also provides a log reader for reading an
    'ndjson' log file into a data frame, log rotation, and live echo of the
    'ndjson' log messages to terminal 'stdout' for log capture by external
    systems (like containers). 'loggit' is ideal for Shiny apps, data pipelines,
    modeling work flows, and more. Please see the vignettes for detailed example
    use cases.
Implementation of the Swiss Confederation's standard analysis model for salary analyses <https://www.ebg.admin.ch/dam/ebg/en/dokumente/lohngleichheit/infos-zu-analysen/standard-analysemodellzurueberpruefungderlohngleichheitzwischenf.pdf.download.pdf/methodological_approachformonitoringcompliancewithwageequalitybe.pdf> in R. The analysis is run at company-level and the model is intended for companies with 50 or more employees (apprentices, trainees/interns and expats are not included in the analysis). Employees with at least 100 employees are required by the Gender Equality Act to conduct an equal pay analysis.
    This package allows users to run the equal salary analysis in R, providing additional transparency with respect to the methodology and simple automation possibilities.
Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.
A global statistical learning method
  which tries to find the best set of predictors and interactions
  between predictors for modeling binary or quantitative response data.
  Several search algorithms and ensembling techniques are implemented
  allowing for finetuning the method to the specific problem.
  Interactions with single quantitative covariables can be properly
  taken into account by also splitting after those or by fitting
  local four parameter logistic models.
Routines for fitting Logic Regression models. Logic Regression is described
	in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte
        Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005)
        <DOI:10.1002/gepi.20042>.
Provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions.
Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys 
	prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal
	solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary
	maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available
	in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.
Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.
A system for fitting Logistic Curve by Rhodes Method. Method for fitting logistic curve by Rhodes Method is described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).
Dimensionality reduction techniques for binary data including
    logistic PCA.
Here we provide an implementation of the linear and logistic regression-based Reliable Change Index (RCI), to be used with lm and binomial glm model objects, respectively, following Moral et al. <https://psyarxiv.com/gq7az/>. The RCI function returns a score assumed to be approximately normally distributed, which is helpful to detect patients that may present cognitive decline.
Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders.
Functions for fitting a functional principal components logit regression model
	in four different situations: ordinary and filtered functional principal components
	of functional predictors, included in the model according to their variability
	explanation power, and according to their prediction ability by stepwise methods. The
	proposed methods were developed in Escabias et al (2004) 
	<doi:10.1080/10485250310001624738> and Escabias et al (2005)
	<doi:10.1016/j.csda.2005.03.011>.
Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.
Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) "Discrete Choice Methods with Simulation, 2nd Edition" <doi:10.1017/CBO9780511805271>.
Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018) <doi:10.21105/joss.00870>.
Functions to sample from the double log normal distribution and calculate the density, distribution and quantile functions.
Functions to fit log-multiplicative models using 'gnm', with
  support for convenient printing, plots, and jackknife/bootstrap
  standard errors. For complex survey data, models can be fitted from
  design objects from the 'survey' package. Currently supported models
  include UNIDIFF (Erikson & Goldthorpe, 1992),
  a.k.a. log-multiplicative layer effect model (Xie, 1992)
  <doi:10.2307/2096242>, and several association models:
  Goodman (1979) <doi:10.2307/2286971>
  row-column association models of the RC(M) and RC(M)-L families
  with one or several dimensions; two skew-symmetric association
  models proposed by Yamaguchi (1990) <doi:10.2307/271086>
  and by van der Heijden & Mooijaart (1995) <doi:10.1177/0049124195024001002>
  Functions allow computing the intrinsic association coefficient
  (see Bouchet-Valat (2022) <doi:10.1177/0049124119852389>)
  and the Altham (1970) index <doi:10.1111/j.2517-6161.1970.tb00816.x>,
  including via the Bayes shrinkage estimator proposed
  by Zhou (2015) <doi:10.1177/0081175015570097>;
  and the RAS/IPF/Deming-Stephan algorithm.
The lognormal distribution  
  (Limpert et al. (2001) <doi:10.1641/0006-3568(2001)051%5B0341:lndats%5D2.0.co;2>)
  can characterize uncertainty that is bounded by zero.
  This package provides estimation of distribution parameters, computation of
  moments and other basic statistics, and an approximation of the distribution
  of the sum of several correlated lognormally distributed variables 
  (Lo 2013 <doi:10.12988/ams.2013.39511>) and the approximation of the 
  difference of two correlated lognormally distributed variables
  (Lo 2012 <doi:10.1155/2012/838397>).
Functions to fits simple linear regression models with log normal errors 
		and identity link, i.e. taking the responses on the original scale. See
		Muggeo (2018) <doi:10.13140/RG.2.2.18118.16965>.
Uses approximations to compute the natural logarithm of the Gamma
    function for large values.
Contains functions to help create log files.  The 
    package aims to overcome the difficulty of the base R sink() command.  The
    log_print() function will print to both the console and the file log, 
    without interfering in other write operations.
Tools for assessing equivalence of similar Logistic Regression models.
A utility to facilitate the logging and review of R programs in clinical trial programming workflows.
Contains routines for logspline density estimation.
	The function oldlogspline() uses the same algorithm as the logspline package
	version 1.0.x; i.e. the Kooperberg and Stone (1992) 
	algorithm (with an improved interface).  The recommended routine logspline()
	uses an algorithm from Stone et al (1997)  <DOI:10.1214/aos/1031594728>.
Kernel regression smoothing with adaptive local or global plug-in
	     bandwidth selection.
Estimation of Latent Order Logistic (LOLOG) Models for Networks.
    LOLOGs are a flexible and fully general class of statistical graph models. 
    This package provides functions for performing MOM, GMM and variational 
    inference. Visual diagnostics and goodness of fit metrics are provided. 
    See Fellows (2018) <arXiv:1804.04583> for a detailed description of the methods.
Supervised learning techniques designed for the situation when the dimensionality exceeds the sample size have a tendency to overfit as the dimensionality of the data increases. To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsupervised case, yet in the supervised HDLSS regime, few works have attempted to devise dimensionality reduction techniques that leverage the labels associated with the data. In this package and the associated manuscript Vogelstein et al. (2017) <arXiv:1709.01233>, we provide several methods for feature extraction, some utilizing labels and some not, along with easily extensible utilities to simplify cross-validative efforts to identify the best feature extraction method. Additionally, we include a series of adaptable benchmark simulations to serve as a standard for future investigative efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of the included algorithms across a range of benchmark simulations and real data applications.
Read, register and compare point sets from single molecule localization microscopy.
Computes the Lomb-Scargle Periodogram for unevenly sampled time series. Includes a randomization procedure to obtain exact p-values.
An easy tool to transform 2D longitudinal data into 3D arrays suitable for 
  Long short-term memory neural networks training. The array output can be
  used by the 'keras' package. Long short-term memory neural networks are described
  in: Hochreiter, S., & Schmidhuber, J. (1997) <doi:10.1162/neco.1997.9.8.1735>.
Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the 'LongCART' and 'SurvCART' algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.
Methods for plotting categorical longitudinal and time-series data by mapping individuals to the vertical space (each horizontal line represents a participant), time (or repeated measures) to the horizontal space, categorical (or discrete) states as facets using color or shade, and events to points using plotting characters. Sorting individuals in the vertical space and (or) stratifying them by groups can reveal patterns in the changes over time.
This tool takes longitudinal dataset as input and analyzes if there is significant 
             change of the features over time (a proxy for treatments), while detects and controls 
             for 'covariates' simultaneously. 'LongDat' is able to take in several data types as input, 
             including count, proportion, binary, ordinal and continuous data. The output table contains 
              p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy.
High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). 
             Currently support mixed effect regression with or without missing observations by considering 
             covariance structures. It provides estimates by missing at random and missing not at random assumptions.
             In this R package, we present Bayesian approaches that statisticians and clinical 
             researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by 
             Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.
Contains general data structures and
  functions for longitudinal data with multiple variables, 
  repeated measurements, and irregularly spaced time points.
  Also implements a shrinkage estimator of dynamical correlation
  and dynamical covariance.
Tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d).
Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data.  In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) <doi:10.1177/0962280220946080> Random forests for high-dimensional longitudinal data.
Datasets and Functionality from
  'Jan Beran' (1994). Statistics for Long-Memory Processes; Chapman & Hall.
  Estimation of Hurst (and more) parameters for fractional Gaussian noise,
  'fARIMA' and 'FEXP' models.
Long Memory Time Series is a collection of functions for estimation, simulation and testing of long memory processes, spurious long memory processes and fractionally cointegrated systems.
An adaption of the consensus clustering approach from
    'ConsensusClusterPlus' for longitudinal data. The longitudinal data is
    clustered with flexible mixture models from 'flexmix', while the consensus
    matrices are hierarchically clustered as in 'ConsensusClusterPlus'. By using
    the flexibility from 'flexmix' and 'FactoMineR', one can use mixed data
    types for the clustering.
Compute power and sample size for linear models of longitudinal
    data. Supported models include mixed-effects models and models fit by
    generalized least squares and generalized estimating equations. Relevant
    formulas are derived by Liu and Liang (1997) <DOI:10.2307/2533554>, 
    Diggle et al (2002) <ISBN:9780199676750>, and Lu, Luo, and Chen (2008)
    <DOI:10.2202/1557-4679.1098>.
Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.
Performs recursive partitioning of linear and nonlinear mixed effects models,
    specifically for longitudinal data. The package is an
    extension of the original 'longRPart' package by 
    Stewart and Abdolell (2013) <https://cran.r-project.org/package=longRPart>.
Assess the proportion of treatment effect explained by a longitudinal surrogate marker as described in Agniel D and Parast L (2021) <doi:10.1111/biom.13310>.
Tools are provided to expand vectors of short URLs into long 'URLs'. 
    No 'API' services are used, which may mean that this operates more slowly than 
    'API' services do (since they usually cache results of expansions that every 
    user of the service requests). You can setup your own caching layer with the 
    'memoise' package if you wish to have a speedup during single sessions or add 
    larger dependencies, such as 'Redis', to gain a longer-term performance boost 
    at the expense of added complexity.
Efficient approximate leave-one-out cross-validation (LOO)
    for Bayesian models fit using Markov chain Monte Carlo, as 
    described in Vehtari, Gelman, and Gabry (2017) 
    <doi:10.1007/s11222-016-9696-4>. 
    The approximation uses Pareto smoothed importance sampling (PSIS), 
    a new procedure for regularizing importance weights. 
    As a byproduct of the calculations, we also obtain approximate 
    standard errors for estimated predictive errors and for the comparison 
    of predictive errors between models. The package also provides methods 
    for using stacking and other model weighting techniques to average 
    Bayesian predictive distributions.
Outlier detection using leave-one-out kernel density estimates and
    extreme value theory. The bandwidth for kernel density estimates is computed 
    using persistent homology, a technique in topological data analysis. Using 
    peak-over-threshold method, a generalized Pareto distribution is fitted to
    the log of leave-one-out kde values to identify outliers.
Simple functions to lookup items in key-value pairs. See  Mehta (2021) <doi:10.1007/978-1-4842-6613-7_6>.
Fits look-up tables by filling entries with the mean or median values of observations
            fall in partitions of the feature space. Partitions can be determined by user of the
            package using input argument feature.boundaries, and dimensions of the feature space
            can be any combination of continuous and categorical features provided by the data set.
            A Predict function directly fetches corresponding entry value, and a default value is
            defined as the mean or median of all available observations.
            The table and other components are represented using the S4 class lookupTable.
An extendable toolkit for interactive data visualization and exploration.
Data used as examples in the 'loon' package.
Provides a bridge between the 'loon' and  'ggplot2' packages.  Extends the grammar of ggplot to add clauses to create interactive 'loon' plots. Existing ggplot(s) can be turned into interactive 'loon' plots and 'loon' plots into static ggplot(s); the function 'loon.ggplot()' is the bridge from one plot structure to the other.
Package 'shiny' provides interactive web applications in R. Package 'loon' is an interactive toolkit engaged in open-ended, creative and unscripted data exploration. The 'loon.shiny' package can take 'loon' widgets and display a selfsame 'shiny' app.
Implement tour algorithms in interactive graphical system 'loon'.
Loop analysis makes qualitative predictions of variable change in a system of causally interdependent variables, where "qualitative" means sign only (i.e. increases, decreases, non change, and ambiguous). This implementation includes output support for graphs in .dot file format for use with visualization software such as 'graphviz' (<http://graphviz.org>). 'LoopAnalyst' provides tools for the construction and output of community matrices, computation and output of community effect matrices, tables of correlations, adjoint, absolute feedback, weighted feedback and weighted prediction matrices, change in life expectancy matrices, and feedback, path and loop enumeration tools.
Detect feedback loops (cycles, circuits) between species (nodes) in ordinary differential equation (ODE) models. Feedback loops are paths from a node to itself without visiting any other node twice, and they have important regulatory functions. Loops are reported with their order of participating nodes and their length, and whether the loop is a positive or a negative feedback loop. An upper limit of the number of feedback loops limits runtime (which scales with feedback loop count). Model parametrizations and values of the modelled variables are accounted for. Computation uses the characteristics of the Jacobian matrix as described e.g. in Thomas and Kaufman (2002) <doi:10.1016/s1631-0691(02)01452-x>. Input can be the Jacobian matrix of the ODE model or the ODE function definition; in the latter case, the Jacobian matrix is determined using 'numDeriv'. Graph-based algorithms from 'igraph' are employed for path detection.
Common coordinate-based workflows involving processed chromatin loop and genomic element data are considered and packaged into appropriate customizable functions. Includes methods for linking element sets via chromatin loops and creating consensus loop datasets.
Change-point detection algorithm with
 label constraints and a penalty for each change outside of labels.
 Read TD Hocking, A Srivastava (2020) <arXiv:2006.13967> for details.
Analysis of Differential Item Functioning (DIF) for
        dichotomous and polytomous items using an iterative hybrid of
        ordinal logistic regression and item response theory (IRT).
Estimate covariance matrices that contain low rank and sparse components
The Lorentz transform in special relativity; also the gyrogroup structure of three-velocities.  Includes active and passive transforms and the ability to use units in which the speed of light is not one.  For general relativity, see the
  'schwarzschild' package.
Provides two methods of estimating income inequality statistics from binned income data, such as the income data provided in the Census.
  These methods use different interpolation techniques to infer the distribution of incomes within income bins.  One method is an implementation of 
  Jargowsky and Wheeler's mean-constrained integration over brackets (MCIB).  The other method is based on a new technique, Lorenz interpolation, 
  which estimates income inequality by constructing an interpolated Lorenz curve based on the binned income data.  These methods can be used to 
  estimate three income inequality measures: the Gini (the default measure returned), the Theil, and the Atkinson's index.  
  Jargowsky and Wheeler (2018) <doi:10.1177/0081175018782579>.
Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear Poisson model where main row and column effects, as well as effects of known covariates and interaction terms can be fitted. The estimation procedure is based on the convex optimization of the Poisson loss penalized by a Lasso type penalty and a nuclear norm. LORI returns estimates of main effects, covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure. The methods are described in Robin, Josse, Moulines and Sardy (2019) <arXiv:1703.02296v4>.
Functions for simulating missing morphometric
	data randomly, with taxonomic bias and with anatomical bias. LOST also 
	includes functions for estimating linear and geometric morphometric data.
Running Lotka's Law following Pao (1985)(DOI: 10.1016/0306-4573(85)90055-X). The Law is based around the proof that the number of authors making n contributions is about 1/n^{a} of those making one contribution.
Provides a simple mechanism to specify a symmetric block
    diagonal matrices (often used for covariance matrices).  This is based
    on the domain specific language implemented in 'nlmixr2' but expanded
    to create matrices in R generally instead of specifying parts of
    matrices to estimate.
Performs the trimmed k-means clustering algorithm with lower memory use. It also provides a number of utility functions such as BIC calculations.
Creates lowpass filters which are commonly used in ion channel recordings. It supports generation of random numbers that are filtered, i.e. follow a model for ion channel recordings, see <doi:10.1109/TNB.2018.2845126>. Furthermore, time continuous convolutions of piecewise constant signals with the kernel of lowpass filters can be computed.
Solves quadratic programming problems where the Hessian is represented as the product of two matrices.
Implementation of Low Walsh Figure of Merit (WAFOM) sequence
        based on Niederreiter-Xing sequence <DOI:10.1007/978-3-642-56046-0_30>.
Implementation of Low Walsh Figure of Merit (WAFOM) sequence
        based on Sobol sequence.
Given a postulated model and a set of data, the comparison density is estimated and the deviance test is implemented in order to assess if the data distribution deviates significantly from the postulated model. Finally, the results are summarized in a CD-plot as described in Algeri S. (2019) <arXiv:1906.06615>.
Implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.
Tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2022).
Fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with some additional functions for mean shift clustering.
Without imposing stringent distributional assumptions or shape restrictions, nonparametric estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density (and derivatives) estimator based on local polynomial regressions, documented in Cattaneo, Jansson and Ma (2022) <doi:10.18637/jss.v101.i02>: lpdensity() to construct local polynomial based density (and derivatives) estimator, and lpbwdensity() to perform data-driven bandwidth selection.
It uses 'phenological' and productivity-related variables derived from time series of vegetation 
    indexes, such as the Normalized Difference Vegetation Index, to assess ecosystem dynamics and change, which 
    eventually might drive to land degradation. The final result of the Land Productivity Dynamics indicator 
    is a categorical map with 5 classes of land productivity dynamics, ranging from declining to increasing 
    productivity. See <https://eartharxiv.org/repository/view/2294/> for a description of the methods used in 
    the package to calculate the indicator.
A nonparametric method to approximate Laplacian graph spectra of a network with 
    ordered vertices. This provides a computationally efficient algorithm for obtaining an 
	accurate and smooth estimate of the graph Laplacian basis. The approximation results can 
	then be used for tasks like change point detection, k-sample testing, and so on. The 
	primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).
Provides a bunch of algorithms based on linear programming for estimating, 
    under the homogeneity hypothesis, RxC ecological contingency tables 
    (or vote transition matrices) using exclusively aggregate
    data (from voting units). References: 
    Romero, Pavía, Martín and Romero (2020) <doi:10.1080/02664763.2020.1804842>.
    Pavía and Romero (2021a) <doi:10.31124/advance.14716638.v1>.
    Pavía and Romero (2021b) Symmetry estimating R×C vote transfer matrices from aggregate data.
Functions to estimate the intensity function and its derivative of a given order of a multiplicative counting process using the local polynomial method.
Provides functions to estimate and visualize linear as well as nonlinear impulse 
             responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>.
             The methods and the package are explained in detail in Adämmer (2019) <doi:10.32614/RJ-2019-052>.
LP nonparametric high-dimensional K-sample comparison method that includes 
    (i) confirmatory test, (ii) exploratory analysis, and (iii) options to output a 
	data-driven LP-transformed matrix for classification. The primary reference is 
	Mukhopadhyay, S. and Wang, K. (2020, Biometrika); <arXiv:1810.01724>.
Local partial likelihood estimation by Fan, Lin and Zhou(2006)<doi:10.1214/009053605000000796> and simultaneous confidence band is a set of tools to test the covariates-biomarker interaction for survival data. Test for the covariates-biomarker interaction using the bootstrap method and the asymptotic method with simultaneous confidence band (Liu, Jiang and Chen (2015)<doi:10.1002/sim.6563>).
Apply Univariate Long Memory Models,
    Apply Multivariate Short Memory Models To Hydrological Dataset,
    Estimate Intensity Duration Frequency curve to rainfall series.
Provide nonparametric methods for mean regression model, modal regression and conditional density estimation in the presence/absence of measurement error. Bandwidth selection is also provided for each method. See Huang and Zhou (2017) <doi:10.1080/10485252.2017.1303060>, Zhou and Huang (2016) <doi:10.1214/16-EJS1210>, Huang and Zhou (2020) <doi:10.1214/20-EJS1688>, and Zhou and Huang (2019) <doi:10.1080/03610918.2017.1402044>.
Creates a consensus genetic map by merging linkage maps from different populations.  The software uses linear programming (LP) to efficiently minimize the mean absolute error between the consensus map and the linkage maps.  This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved.  When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts.
Computes power, or sample size or the detectable difference for a repeated measures model with attrition. It requires the variance covariance matrix of the observations but can compute this matrix for several common random effects models. See Diggle, P, Liang, KY and Zeger, SL (1994, ISBN:9780198522843).
Provide methods to perform customized inference at individual level by taking 
	contextual covariates into account. Three main functions are provided 
	in this package: (i) LASER(): it generates specially-designed artificial relevant 
	samples for a given case; (ii) g2l.proc(): computes customized fdr(z|x); and (iii) 
	rEB.proc(): performs empirical Bayes inference based on LASERs. The details can be 
	found in Mukhopadhyay, S., and Wang, K (2021, <arXiv:2004.09588>).
Local Polynomial Regression with Ridging.
An implementation of the Linear Predictor Score approach, as initiated by Radmacher et al. (J Comput Biol 2001) and enhanced by Wright et al. (PNAS 2003) for gene expression signatures. Several tools for unsupervised clustering of gene expression data are also provided.
Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. To overcome these shortcomings, we establish a comprehensive framework for goodness-of-fit which naturally integrates modeling, estimation, inference and graphics. In this package, the deviance tests and comparison density plots are performed to conduct the LP smoothed inference, where the letter L denotes nonparametric methods based on quantiles and P stands for polynomials. Simulations methods are used to perform variance estimation, inference and post-selection adjustments. Algeri S. and Zhang X. (2020) <arXiv:2005.13011>.
Lp_solve is freely available (under LGPL 2) software for
        solving linear, integer and mixed integer programs. In this
        implementation we supply a "wrapper" function in C and some R
        functions that solve general linear/integer problems,
        assignment problems, and transportation problems. This version
        calls lp_solve version 5.5.
The lpSolveAPI package provides an R interface to 'lp_solve',
    a Mixed Integer Linear Programming (MILP) solver with support for pure
    linear, (mixed) integer/binary, semi-continuous and special ordered sets
    (SOS) models.
Computes a time series distance measure for clustering based on weighted correlation and introduction of lags. The lags capture delayed responses in a time series dataset. The timepoints must be specified. T. Chandereng, A. Gitter (2020) <doi:10.1186/s12859-019-3324-1>.
Produces a group screening procedure that is based on maximum Lq-likelihood estimation, to simultaneously account for the group structure and data contamination in variable screening. The methods are described in Li, Y., Li, R., Qin, Y., Lin, C., & Yang, Y. (2021) Robust Group Variable Screening Based on Maximum Lq-likelihood Estimation. Statistics in Medicine, 40:6818-6834.<doi:10.1002/sim.9212>.
Functions to fit quantile regression models for hierarchical
    data (2-level nested designs) as described in Geraci and
    Bottai (2014, Statistics and Computing) <doi:10.1007/s11222-013-9381-9>.
    A vignette is given in Geraci (2014, Journal of Statistical Software)
    <doi:10.18637/jss.v057.i13> and included in the package documents.
    The packages also provides functions to fit quantile models for
    independent	data and for count responses.
It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. Missing values and censored observations can be handled as well. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Galarza et.al.(2020) <doi:10.1007/s13571-020-00231-0>. It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.
Provides functions for calculating test statistics, simulating quantiles 
	and simulating p-values of likelihood ratio contrast tests in regression models 
	with a lack of identifiability.
Lexical response data is a package that 
    can be used for processing cued-recall, free-recall, 
    and sentence responses from memory experiments.
The programs were developed for estimation of parameters and testing exponential versus Pareto distribution during our work on hydrologic extremes. See Kozubowski, T.J., A.K. Panorska, F. Qeadan, and A. Gershunov (2007) <doi:10.1080/03610910802439121>, and Panorska, A.K., A. Gershunov, and T.J. Kozubowski (2007) <doi:10.1007/978-0-387-34918-3_26>.
In the fashion of 'node.js' <https://nodejs.org/>, requires a file,
  sourcing into the current environment only the variables explicitly specified
  in the module.exports or exports list variable. If the file was already sourced,
  the result of the earlier sourcing is returned to the caller.
Implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution. Described further in Mantz (2016) <DOI:10.1093/mnras/stv3008>.
When multicollinearity exists among predictor variables of the linear model, least square estimators does not provide a better solution for estimating parameters. To deal with multicollinearity several estimators are proposed in the literature. Some of these estimators are Ordinary Least Square Estimator (OLSE), Ordinary Generalized Ordinary Least Square Estimator (OGOLSE), Ordinary Ridge Regression Estimator (ORRE), Ordinary Generalized Ridge Regression Estimator (OGRRE), Restricted Least Square Estimator (RLSE), Ordinary Generalized Restricted Least Square Estimator (OGRLSE), Ordinary Mixed Regression Estimator (OMRE), Ordinary Generalized Mixed Regression Estimator (OGMRE), Liu Estimator (LE), Ordinary Generalized Liu Estimator (OGLE), Restricted Liu Estimator (RLE), Ordinary Generalized Restricted Liu Estimator (OGRLE), Stochastic Restricted Liu Estimator (SRLE), Ordinary Generalized Stochastic Restricted Liu Estimator (OGSRLE), Type (1),(2),(3) Liu Estimator (Type-1,2,3 LTE), Ordinary Generalized Type (1),(2),(3) Liu Estimator (Type-1,2,3 OGLTE), Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 ALTE), Ordinary Generalized Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 OGALTE), Almost Unbiased Ridge Estimator (AURE), Ordinary Generalized Almost Unbiased Ridge Estimator (OGAURE), Almost Unbiased Liu Estimator (AULE), Ordinary Generalized Almost Unbiased Liu Estimator (OGAULE), Stochastic Restricted Ridge Estimator (SRRE), Ordinary Generalized Stochastic Restricted Ridge Estimator (OGSRRE), Restricted Ridge Regression Estimator (RRRE) and Ordinary Generalized Restricted Ridge Regression Estimator (OGRRRE). To select the best estimator in a practical situation the Mean Square Error (MSE) is used. Using this package scalar MSE value of all the above estimators and Prediction Sum of Square (PRESS) values of some of the estimators can be obtained, and the variation of the MSE and PRESS values for the relevant estimators can be shown graphically.
Provides S3 classes to represent low rank matrix
    decompositions.
Fit a quantile regression mixed model involved Relationship Matrix using a sparse implementation of the
  Frisch-Newton interior-point algorithm as described in 
  Portnoy and Koenker (1977, Statistical Science)  <https://www.jstor.org/stable/2246216>.
Performs power and sample size calculation for non-proportional 
  hazards model using the Fleming-Harrington family of weighted log-rank 
  tests. The sequentially calculated log-rank test score statistics are 
  assumed to have independent increments as characterized in 
  Anastasios A. Tsiatis (1982) <doi:10.1080/01621459.1982.10477898>. The mean 
  and variance of log-rank test score statistics are calculated based on 
  Kaifeng Lu (2021) <doi:10.1002/pst.2069>. The boundary crossing probabilities 
  are calculated using the recursive integration algorithm described in 
  Christopher Jennison and Bruce W. Turnbull (2000, ISBN:0849303168).
A collection of hypothesis tests and confidence intervals based on the likelihood ratio
    <https://en.wikipedia.org/wiki/Likelihood-ratio_test>.
R code of a likelihood ratio test for genome-wide association under genetic
    heterogeneity.
Estimates two-dimensional local wavelet spectra.
Wavelet-based methods for testing stationarity and quadtree segmenting of images, see Taylor et al (2014) <doi:10.1080/00401706.2013.823890>.
The basic idea of latent semantic analysis (LSA) is, 
  that text do have a higher order (=latent semantic) structure which, 
  however, is obscured by word usage (e.g. through the use of synonyms 
  or polysemy). By using conceptual indices that are derived statistically 
  via a truncated singular value decomposition (a two-mode factor analysis) 
  over a given document-term matrix, this variability problem can be overcome.
Provides functions that allow for convenient working with vector space models of semantics/distributional semantic models/word embeddings. 
	Originally built for LSA models (hence the name), but can be used for all such vector-based models. 
	For actually building a vector semantic space, use the package 'lsa' or other specialized software. 
	Downloadable semantic spaces can be found at <https://sites.google.com/site/fritzgntr/software-resources>.
Dieses R-Paket stellt Zusatzmaterial in Form von Daten, Funktionen
             und R-Hilfe-Seiten für den Herausgeberband Breit, S. und Schreiner, 
             C. (Hrsg.). (2016). "Large-Scale Assessment mit R: Methodische Grundlagen 
             der österreichischen Bildungsstandardüberprüfung." Wien: facultas. 
             (ISBN: 978-3-7089-1343-8, <https://www.iqs.gv.at/themen/bildungsforschung/publikationen/veroeffentlichte-publikationen>) zur 
             Verfügung.
Provides functions to simulate data from large-scale educational
  assessments, including background questionnaire data and cognitive item
  responses that adhere to a multiple-matrix sampled design. The theoretical
  foundation can be found on
  Matta, T.H., Rutkowski, L., Rutkowski, D. et al. (2018)
  <doi:10.1186/s40536-018-0068-8>.
Functions for performing least-squares bilinear clustering of
    three-way data. The method uses the bilinear decomposition (or bi-additive
    model) to model two-way matrix slices while clustering over the third way.
    Up to four different types of clusters are included, one for each term of the
    bilinear decomposition. In this way, matrices are clustered simultaneously on
    (a subset of) their overall means, row margins, column margins and row-column
    interactions. The orthogonality of the bilinear model results in separability of
    the joint clustering problem into four separate ones. Three of these sub-problems
    are specific k-means problems, while a special algorithm is implemented for the
    interactions. Plotting methods are provided, including biplots for the low-rank
    approximations of the interactions.
Bandwidth selection for kernel density estimators of 2-d
    level sets and highest density regions. It applies a plug-in
    strategy to estimate the asymptotic risk function and minimize to
    get the optimal bandwidth matrix. See Doss and Weng (2018)
    <arXiv:1806.00731> for more detail.
Create lots of colorful plots in a plethora of variations. Try the LSD demotour().
Implementation of Locally Scaled Density Based Clustering (LSDBC) algorithm proposed by Bicici and Yuret (2007) <doi:10.1007/978-3-540-71618-1_82>. This package also contains some supporting functions such as betaCV() function and get_spectral() function.
Interfaces R with LSD simulation models. Reads object-oriented data in results files (.res[.gz]) produced by LSD and creates appropriate multi-dimensional arrays in R. Supports multiple core parallel threads of multi-file data reading for increased performance. Also provides functions to extract basic information and statistics from data files. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).
Tools for sensitivity analysis of LSD simulation models. Reads object-oriented data produced by LSD simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) <doi:10.1016/j.ejor.2007.10.013>) is estimated using the simulation data to provide the data required by the Sobol decomposition. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).
The solution of equality constrained least squares problem (LSE) is
    given through four analytics methods (Generalized QR Factorization, Lagrange 
    Multipliers, Direct Elimination and Null Space method). We expose the 
    orthogonal decomposition called Generalized QR Factorization (GQR) and also RQ 
    factorization. Finally some codes for the solution of LSE applied in 
    quaternions.
It contains functions that solve least squares linear
	     regression problems under linear equality/inequality
	     constraints. Functions for solving quadratic programming
	     problems are also available, which transform such problems
	     into least squares ones first. It is developed based on the
	     'Fortran' program of Lawson and Hanson (1974, 1995), which is
	     public domain and available at
	     <http://www.netlib.org/lawson-hanson/>.
Analysis of dichotomous and continuous response data using latent factor by both 1PL LSIRM and 2PL LSIRM as described in Jeon et al. (2021) <doi:10.1007/s11336-021-09762-5>. It includes original 1PL LSIRM and 2PL LSIRM provided for binary response data and its extension for continuous response data. Bayesian model selection with spike-and-slab prior and method for dealing data with missing value under missing at random, missing completely at random are also supported. Various diagnostic plots are available to inspect the latent space and summary of estimated parameters.
Fits structural equation modeling via penalized likelihood.
Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) or penalized least squares (PLS). For details, please see Huang (2020) <doi:10.18637/jss.v093.i07>.
When the values of the outcome variable Y are either 0 or 1,
      the function lsm() calculates the estimation of the log likelihood 
      in the saturated model. 
      This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 
      through the assumptions 1 and 2. 
      The function LogLik() works (almost perfectly) when the number of independent
      variables K is high, but for small K it calculates wrong values in some cases.
      For this reason, when Y is dichotomous and the data are grouped in J populations,
      it is recommended to use the function lsm() because it works very well for all K.
Obtain least-squares means for linear, generalized linear, 
    and mixed models. Compute contrasts or linear functions of 
    least-squares means, and comparisons of slopes. 
    Plots and compact letter displays. Least-squares means were proposed in
    Harvey, W (1960) "Least-squares analysis of data with unequal subclass numbers",
    Tech Report ARS-20-8, USDA National Agricultural Library, and discussed
    further in Searle, Speed, and Milliken (1980) "Population marginal means 
    in the linear model: An alternative to least squares means", 
    The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
    NOTE: lsmeans now relies primarily on code in the 'emmeans' package.
    'lsmeans' will be archived in the near future.
The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.
The least-squares Monte Carlo (LSM) simulation method is a popular method for the approximation of the value of early and multiple exercise options. 'LSMRealOptions' provides implementations of the LSM simulation method to value American option products and capital investment projects through real options analysis. 'LSMRealOptions' values capital investment projects with cash flows dependent upon underlying state variables that are stochastically evolving, providing analysis into the timing and critical values at which investment is optimal. 'LSMRealOptions' provides flexibility in the stochastic processes followed by underlying assets, the number of state variables, basis functions and underlying asset characteristics to allow a broad range of assets to be valued through the LSM simulation method. Real options projects are further able to be valued whilst considering construction periods, time-varying initial capital expenditures and path-dependent operational flexibility including the ability to temporarily shutdown or permanently abandon projects after initial investment has occurred. The LSM simulation method was first presented in the prolific work of Longstaff and Schwartz (2001) <doi:10.1093/rfs/14.1.113>.
Tools for statistical analysis using partitioning-based least squares regression as described in Cattaneo, Farrell and Feng (2019a, <arXiv:1804.04916>) and Cattaneo, Farrell and Feng (2019b, <arXiv:1906.00202>): lsprobust() for nonparametric point estimation of regression functions and their derivatives and for robust bias-corrected (pointwise and uniform) inference; lspkselect() for data-driven selection of the IMSE-optimal number of knots; lsprobust.plot() for regression plots with robust confidence intervals and confidence bands; lsplincom() for estimation and inference for linear combinations of regression functions from different groups.
Creates plots of peptides from shotgun proteomics analysis of secretome and lysate samples. These plots contain associated protein features and scores for potential secretion and truncation.
Linear splines with convenient parametrisations such that 
  (1) coefficients are slopes of consecutive segments or (2) coefficients are 
  slope changes at consecutive knots. Knots can be set manually or at break points
  of equal-frequency or equal-width intervals covering the range of 'x'.
  The implementation follows Greene (2003), chapter 7.2.5.
Implements the LS-PLS (least squares - partial least squares)
  method described in for instance Jørgensen, K., Segtnan, V. H., Thyholt, K.,
  Næs, T. (2004)  "A Comparison of Methods for Analysing Regression Models with
  Both Spectral and Designed Variables"
  Journal of Chemometrics, 18(10), 451--464, <doi:10.1002/cem.890>.
Fit logistic regression models using LS-PLS approaches 
	to analyse both clinical and genomic data. (C. Bazzoli and 
	S. Lambert-Lacroix. (2017) Classification using LS-PLS with 
	logistic regression based on both clinical and gene expression
	variables <https://hal.archives-ouvertes.fr/hal-01405101>).
A collection of tools intended to make introductory 
    statistics easier to teach, including wrappers for common 
    hypothesis tests and basic data manipulation. It accompanies 
    Navarro, D. J. (2015). Learning Statistics with R: A Tutorial 
    for Psychology Students and Other Beginners, Version 0.6.
Rapid satellite data streams in operational applications
    have clear benefits for monitoring land cover, 
    especially when information can be delivered as fast as changing
    surface conditions. Over the past decade,
    remote sensing has become a key tool for monitoring and predicting
    environmental variables by using satellite data. 
    This package presents the main applications in remote sensing for 
    land surface monitoring and land cover mapping (soil, vegetation, water...).
     Tomlinson, C.J., Chapman, L., Thornes, E., Baker, C (2011) <doi:10.1002/met.287>.
Due to lack of proper inference procedure and software,
        the ordinary linear regression model is seldom used in practice
        for the analysis of right censored data. This paper presents an
        S-Plus/R program that implements a recently developed inference
        procedure (Jin, Lin and Ying, 2006) <doi:10.1093/biomet/93.1.147> 
        for the accelerated failure time model based on the least-squares
        principle.
Calculates Land Surface Temperature from Landsat band 10 and 11.
  Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. Jimenez-Munoz JC, Cristobal J, Sobrino JA, et al (2009). <doi: 10.1109/TGRS.2008.2007125>.
  Land surface temperature retrieval from LANDSAT TM 5. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004). <doi:10.1016/j.rse.2004.02.003>.
  Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Srivastava PK, Majumdar TJ, Bhattacharya AK (2009). <doi: 10.1016/j.asr.2009.01.023>.
  Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Valor E (1996). <doi:10.1016/0034-4257(96)00039-9>.
  On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Van de Griend AA, Owe M (1993). <doi:10.1080/01431169308904400>.
  Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Yu X, Guo X, Wu Z (2014). <doi:10.3390/rs6109829>.
  Calibration and Validation of land surface temperature for Landsat8-TIRS sensor. Land product validation and evolution. Skoković D, Sobrino JA, Jimenez-Munoz JC, Soria G, Julien Y, Mattar C, Cristóbal J. (2014).
A set of functions that allow stationary analysis and locally stationary time series analysis.
Implementations of estimation algorithm of low rank plus sparse structured VAR model by using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). It relates to the algorithm in Sumanta, Li, and Michailidis (2019) <doi:10.1109/TSP.2018.2887401>.
Library of functions for the statistical analysis and simulation of Locally Stationary Wavelet Packet (LSWP) processes.  The methods implemented by this library are described in Cardinali and Nason (2017) <doi:10.1111/jtsa.12230>.
A word embeddings-based semisupervised model for document scaling Watanabe (2020) <doi:10.1080/19312458.2020.1832976>.
    LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove).
    It can generate word vectors on a users-provided corpus or incorporate a pre-trained word vectors.
Constructs tables of counts and proportions out of data sets with possibility to insert tables to Excel, Word, HTML, and PDF documents. Transforms tables to data suitable for modelling. Features Gibbs sampling based log-linear (NB2) and power analyses (original by Oleksandr Ocheredko <doi:10.35566/isdsa2019c5>) for tabulated data.
A suite of functions for reading in a rate file in XML format, 
    stratify a cohort, and calculate 'SMRs' from the stratified cohort and rate file.
Analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.
Mixture modelling of one-dimensional data using combinations of left-truncated Gamma, Weibull, and Lognormal Distributions. Blostein, Martin & Miljkovic, Tatjana. (2019) <doi:10.1016/j.insmatheco.2018.12.001>.
Targeted Maximum Likelihood Estimation (TMLE) of
    treatment/censoring specific mean outcome or marginal structural model for
    point-treatment and longitudinal data.
Calculation of rectifying LTPD and AOQL plans for sampling inspection by variables which minimize mean inspection cost per lot of process average quality.
Implements the conditional inference forest and relative risk forest 
             algorithm to modeling left-truncated right-censored data with time-invariant        
             covariates, and (left-truncated) right-censored survival data with time-varying 
             covariates. It also provides functions to tune the parameters and evaluate the 
             model fit. See Yao et al. (2020) <arXiv:2006.00567>.
Recursive partition algorithms designed for fitting survival tree with left-truncated and right censored (LTRC) data, as well as interval-censored data.
    The LTRC trees can also be used to fit survival tree with time-varying covariates.
Methods of developing linear time series modelling.
 Methods are given for loglikelihood computation, forecasting
  and simulation.
Implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively. The method is documented in Kumar et al (2013) <https://www.nature.com/articles/jes201352)>.
Implementation of robust and sparse PCA algorithm of Wang and Van Aelst (2019) <DOI:10.1080/00401706.2019.1671234>.
Sparklines are small plots (about one line of text high),
  made popular by Edward Tufte.  This package is the interface from R
  to the LaTeX package sparklines by Andreas Loeffer and Dan Luecking
  (<http://www.ctan.org/pkg/sparklines>).  It can work with Sweave or
  knitr or other engines that produce TeX.  The package can be used to
  plot vectors, matrices, data frames, time series (in ts or zoo format).
Functions to work with date-times and time-spans: fast and
    user friendly parsing of date-time data, extraction and updating of
    components of a date-time (years, months, days, hours, minutes, and
    seconds), algebraic manipulation on date-time and time-span objects.
    The 'lubridate' package has a consistent and memorable syntax that
    makes working with dates easy and fun.
Likelihood inference under covariate assumptions (LUCA) in case-control studies of a rare disease assuming independence or simple dependence of genetic and
        non-genetic covariates.
Reproduces the harmonized DB of the ESTAT survey of the same name. The survey data is served as separate spreadsheets with noticeable differences in the collected attributes. The tool here presented carries out a series of instructions that harmonize the attributes in terms of name, meaning, and occurrence, while also introducing a series of new variables, instrumental to adding value to the product. Outputs include one harmonized table with all the years, and three separate geometries, corresponding to the theoretical point, the gps location where the measurement was made and the 250m east-facing transect.
Print vectors (and data frames) of floating point numbers
    using a non-scientific format optimized for human readers.  Vectors
    of numbers are rounded using significant digits, aligned at the
    decimal point, and all zeros trailing the decimal point are dropped.
    See: Wright (2016). Lucid: An R Package for Pretty-Printing Floating Point
    Numbers. In JSM Proceedings, Statistical Computing Section. Alexandria,
    VA: American Statistical Association. 2270-2279.
An implementation of the LUCID model (Peng (2019) <doi:10.1093/bioinformatics/btz667>).
  LUCID conducts integrated clustering using exposures, omics data (and outcome 
  as an option). An EM algorithm is implemented to estimate MLE of the LUCID model.
  LUCIDus features integrated variable selection, incorporation of missing omics
  data, bootstrap inference, prediction and visualization of the model.
Probabilistic record linkage without direct identifiers using only 
    diagnosis codes. Method is detailed in: Hejblum, Weber, Liao, Palmer, 
    Churchill, Szolovits, Murphy, Kohane & Cai (2019) <doi: 10.1038/sdata.2018.298> ;
    Zhang, Hejblum, Weber, Palmer, Churchill, Szolovits, Murphy, Liao, Kohane 
    & Cai (2021) <doi: 10.1101/2021.05.02.21256490>.
Contains LUE_BIOMASS(),LUE_BIOMASS_VPD(), LUE_YIELD() and LUE_YIELD_VPD() to estimate aboveground biomass and crop yield firstly by calculating the Absorbed Photosynthetically Active Radiation (APAR) and secondly the actual values of light use efficiency with and without vapour presure deficit Shi et al.(2007) <doi:10.2134/agronj2006.0260>.
Classes and methods for spatially explicit land use change
    modelling in R.
A framework that allows for easy logging of changes in data.
    Main features: start tracking changes by adding a single line of code to 
    an existing script. Track changes in multiple datasets, using multiple 
    loggers. Add custom-built loggers or use loggers offered by other 
    packages. <doi:10.18637/jss.v098.i01>.
A collection of various R functions for the purpose of Luminescence
    dating data analysis. This includes, amongst others, data import, export,
    application of age models, curve deconvolution, sequence analysis and
    plotting of equivalent dose distributions.
Provides functions to calculate lunar and other related
    environmental covariates.
Input latitude and longitude values or an 'sf/sfc' POINT 
    object and get back the time zone in which they exist. Two methods are implemented. 
    One is very fast and uses 'Rcpp' in conjunction with data from the 'Javascript' library
    (<https://github.com/darkskyapp/tz-lookup/>). This method also works outside of countries' 
    borders and in international waters, however speed comes at the cost of accuracy - near time 
    zone borders away from populated centres there is a chance that it will return the incorrect
    time zone. The other method is slower but more accurate - it uses the 'sf' package to intersect 
    points with a detailed map of time zones from here: 
    <https://github.com/evansiroky/timezone-boundary-builder/>. The package also 
    contains several utility functions for helping to understand and visualize 
    time zones, such as listing of world time zones, including information about 
    daylight savings times and their offsets from UTC. You can also plot a 
    time zone to visualize the UTC offset over a year and when daylight savings 
    times are in effect.
A high level interface for 'torch' providing utilities to reduce the
    the amount of code needed for common tasks, abstract away torch details and 
    make the same code work on both the 'CPU' and 'GPU'. It's flexible enough to
    support expressing a large range of models. It's heavily inspired by 'fastai' by 
    Howard et al. (2020) <arXiv:2002.04688>, 'Keras' by Chollet et al. (2015) and 
    'PyTorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.
Provides flexible but lightweight logging facilities for R scripts.
    Supports priority levels for logs and messages, flagging messages, capturing
    script output, switching logs, and logging to files or connections.
Core functionality for working with vectors (numeric, integer,
    logical and character) that are too large to keep in memory. The vectors are
    kept (partially) on disk using memory mapping. This package contains the
    basic functionality for working with these memory mapped vectors (e.g.
    creating, indexing, ordering and sorting) and provides C++ headers which can
    be used by other packages to extend the functionality provided in this
    package.
Fit response surfaces for datasets with latent-variable Gaussian process modeling, predict responses for new inputs, and plot latent variables locations in the latent space (only 1D or 2D).
    The input variables of the datasets can be quantitative, qualitative/categorical or mixed.
    The output variable of the datasets is a scalar (quantitative).
    The optimization of the likelihood function is done using a successive approximation/relaxation algorithm similar to another GP modeling package "GPM".
    The modeling method is published in "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors"
    by Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018) <arXiv:1806.07504>. The package is developed in IDEAL of Northwestern University.
Latent variable models for network data using fast inferential
    procedures. For more information please visit: <http://igollini.github.io/lvm4net/>.
Provides stochastic EM algorithms for latent variable models
    with a high-dimensional latent space. So far, we provide functions for confirmatory item
    factor analysis based on the multidimensional two parameter logistic (M2PL) model and the 
    generalized multidimensional partial credit model. These functions scale well for problems
    with many latent traits (e.g., thirty or even more) and are virtually tuning-free.
    The computation is facilitated by multiprocessing 'OpenMP' API.
    For more information, please refer to:
    Zhang, S., Chen, Y., & Liu, Y. (2018). An Improved Stochastic EM Algorithm for Large-scale
    Full-information Item Factor Analysis. British Journal of Mathematical and Statistical
    Psychology. <doi:10.1111/bmsp.12153>.
Contains a collection of useful
    functions for basic data computation and manipulation,
    wrapper functions for generating 'ggplot2' graphics,
    including statistical model diagnostic plots, methods
    for computing statistical models quality measures (such
    as AIC, BIC, r squared, root mean squared error) and
    general utilities.
Estimate, fit and compare Structural Equation Models (SEM) and network models (Gaussian Graphical Models; GGM) using OpenMx. Allows for two possible generalizations to include GGMs in SEM: GGMs can be used between latent variables (latent network modeling; LNM) or between residuals (residual network modeling; RNM). For details, see Epskamp, Rhemtulla and Borsboom (2017) <doi:10.1007/s11336-017-9557-x>.
Implements the letter value 'boxplot' which extends the
    standard 'boxplot' to deal with both larger and smaller number of data
    points by dynamically selecting the appropriate number of letter
    values to display.
The manual approach of Litchfield and Wilcoxon (1949)
    <http://jpet.aspetjournals.org/content/96/2/99.abstract>
    for evaluating dose-effect experiments
    is automated so that the computer can do the work.
Provides a flexible and easy-to use interface for the soil vegetation 
    atmosphere transport (SVAT) model LWF-BROOK90, written in Fortran.
    The model simulates daily transpiration, interception, soil and snow evaporation, 
    streamflow and soil water fluxes through a soil profile covered with vegetation, 
    as described in Hammel & Kennel (2001, ISBN:978-3-933506-16-0) and Federer et al. (2003) 
    <doi:10.1175/1525-7541(2003)004%3C1276:SOAETS%3E2.0.CO;2>. A set of high-level functions
    for model set up, execution and parallelization provides easy access to plot-level SVAT 
    simulations, as well as multi-run and large-scale applications.
Access to selected functions found in 'liblwgeom' <https://github.com/postgis/postgis/tree/master/liblwgeom>, the light-weight geometry library used by 'PostGIS' <http://postgis.net/>.
Wrapper functions for the implementation of lagged weighted quantile sum regression, as per 'Gennings et al' (2020) <doi:10.1016/j.envres.2020.109529>.
An implementation of algorithms described in Jewell and Witten (2017) <arXiv:1703.08644>.
Prediction of behaviour from movement 
	characteristics using observation and random forest for the analyses of movement
	data in ecology.
	From movement information (speed, bearing...) the model predicts the
	observed behaviour (movement, foraging...) using random forest. The
	model can then extrapolate behavioural information to movement data
	without direct observation of behaviours.
	The specificity of this method relies on the derivation of multiple predictor variables from the
	movement data over a range of temporal windows. This procedure allows to capture
	as much information as possible on the changes and variations of movement and
	ensures the use of the random forest algorithm to its best capacity. The method
	is very generic, applicable to any set of data providing movement data together with
	observation of behaviour.
Persistent interface to 'Macaulay2' <http://www.math.uiuc.edu/Macaulay2/>
    and front-end tools facilitating its use in the 'R' ecosystem. For details see 
    Kahle et. al. (2020) <doi:10.18637/jss.v093.i09>.
A new method to implement clustering from multiple modality data of certain samples, 
    the function M2SMF() jointly factorizes multiple similarity matrices into a shared sub-matrix 
    and several modality private sub-matrices, which is further used for clustering. Along with 
    this method, we also provide function to calculate the similarity matrix and function to 
    evaluate the best cluster number from the original data.
A new method to implement clustering from multiple modality data of certain samples, 
    the function M2SMjF() jointly factorizes multiple similarity matrices into a shared sub-matrix 
    and several modality private sub-matrices, which is further used for clustering. Along with 
    this method, we also provide function to calculate the similarity matrix and function to 
    evaluate the best cluster number from the original data.
Contains functions, which facilitate downloading, loading and preparing data from 'M5 Forecasting' challenges (by 'University of Nicosia', hosted on 'Kaggle'). 
  The data itself is set of time series of different product sales in 'Walmart'.
  The package also includes a ready-to-use built-in M5 subset named 'tiny_m5'.
  For detailed information about the challenges, see:
  Makridakis, S. & Spiliotis, E. & Assimakopoulos, V. (2020). <doi:10.1016/j.ijforecast.2021.10.009>.
Data manipulation in one package and in base R.
  Minimal. No dependencies.
  'dplyr' and 'tidyr'-like in one place.
  Nothing else than base R to build the package.
A computational method developed for model-based analysis of alternative polyadenylation (APA) using 3' end-linked reads. It accurately assigns 3' RNA-seq reads to polyA sites through statistical modeling, and generates multiple statistics for APA analysis. Please also see Li WV, Zheng D, Wang R, Tian B (2021) <doi:10.1186/s13059-021-02429-5>.
Provides an extension of the shadow-test approach to computerized adaptive
    testing (CAT) implemented in the 'TestDesign' package for the assessment framework
    involving multiple tests administered periodically throughout the year. This framework
    is referred to as the Multiple Administrations Adaptive Testing (MAAT) and supports
    multiple item pools vertically scaled and multiple phases (stages) of CAT within each test.
    Between phases and tests, transitioning from one item pool (and associated constraints)
    to another is allowed as deemed necessary to enhance the quality of measurement.
Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) <doi:10.1080/10485252.2016.1163349> and Guan (2017) <doi:10.1080/10485252.2017.1374384>. For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data.
Conducts and simulates the MABOUST design, including making interim decisions to stop a treatment for inferiority or stop the trial early for superiority or equivalency.
Performs causal mediation analysis under confounding or correlated errors. This package includes a single level mediation model, a two-level mediation model, and a three-level mediation model for data with hierarchical structures. Under the two/three-level mediation model, the correlation parameter is identifiable and is estimated based on a hierarchical-likelihood, a marginal-likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2014), Estimating Mediation Effects under Correlated Errors with an Application to fMRI, <arXiv:1410.7217> for details.
To assist biological researchers in assembling taxonomically and marker focused molecular sequence data sets. 'MACER' accepts a list of genera as a user input and uses NCBI-GenBank and BOLD as resources to download and assemble molecular sequence datasets. These datasets are then assembled by marker, aligned, trimmed, and cleaned. The use of this package allows the publication of specific parameters to ensure reproducibility. The 'MACER' package has four core functions and an example run through using all of these functions can be found in the associated repository <https://github.com/rgyoung6/MACER_example>.
Meta-package for statistical and machine learning with a unified
    interface for model fitting, prediction, performance assessment, and
    presentation of results.  Approaches for model fitting and prediction of
    numerical, categorical, or censored time-to-event outcomes include
    traditional regression models, regularization methods, tree-based methods,
    support vector machines, neural networks, ensembles, data preprocessing,
    filtering, and model tuning and selection.  Performance metrics are provided
    for model assessment and can be estimated with independent test sets, split
    sampling, cross-validation, or bootstrap resampling.  Resample estimation
    can be executed in parallel for faster processing and nested in cases of
    model tuning and selection.  Modeling results can be summarized with
    descriptive statistics; calibration curves; variable importance; partial
    dependence plots; confusion matrices; and ROC, lift, and other performance
    curves.
Download data from the Ada and Archibald MacLeish Field 
    Station in Whately, MA. The Ada 
    and Archibald MacLeish Field Station is a 260-acre patchwork of 
    forest and farmland located in West Whately, MA that provides opportunities 
    for faculty and students to pursue environmental research, outdoor 
    education, and low-impact recreation 
    (see <https://www.smith.edu/about-smith/sustainable-smith/macleish> for more information). 
    This package contains 
    weather data over several years, and spatial data on various man-made and 
    natural structures.
Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) <doi:10.1007/s11749-020-00737-9>. The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package.
This repository aims to contribute to the econometric models' production
	with Colombian data, by providing a set of web-scrapping functions 
	of some of the main macro-financial indicators. All the sources are public and
	free, but the advantage of these functions is that they directly download 
	and harmonize the information in R's environment. No need to import or download
	additional files. You only need an internet connection!
Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models.  M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model.  M-activation can detect high-order interactions -- a traditionally non-trivial challenge.  Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About."
Performs causal mediation analysis for count and zero-inflated
    count data without or with a post-treatment confounder; calculates power
    to detect prespecified causal mediation effects, direct effects, and 
    total effects; performs sensitivity analysis when there is a treatment-
    induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F., 
    Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018) 
    <doi:10.1177/0962280216686131>. Implements Instrumental Variable (IV) 
    method to estimate the controlled (natural) direct and mediation effects, 
    and compute the bootstrap Confidence Intervals as described by Guo, Z., 
    Small, D.S., Gansky, S.A., Cheng, J. (2018) <doi:10.1111/rssc.12233>. This 
    software was made possible by Grant R03DE028410 from the The National 
    Institute of Dental and Craniofacial Research, a component of the National 
    Institutes of Health.
A collection of functions for conducting a meta-analysis with mean differences data.  It uses recommended procedures as	described in The 	Handbook of Research Synthesis and Meta-Analysis	(Cooper, Hedges, & Valentine, 2009).
Provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter & Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.
Contains the Maddison Project 2018 database, which provides 
    estimates of GDP per capita for all countries in the world between AD 1 and
    2016. See <https://www.rug.nl/ggdc/historicaldevelopment/maddison/> for more information.
A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic 
  Optimization algorithm. MADGRAD is a 'best-of-both-worlds' optimizer with the 
  generalization performance of stochastic gradient descent and at least as fast 
  convergence as that of Adam, often faster. A drop-in optim_madgrad() implementation
  is provided based on Defazio et al (2020) <arxiv:2101.11075>.
Provides pipe-style interface for 'data.table'. Package preserves all 'data.table' features without
              significant impact on performance. 'let' and 'take' functions are simplified interfaces for most common data
              manipulation tasks. For example, you can write 'take(mtcars, mean(mpg), by = am)' for aggregation or 
              'let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg)' for modification. Use 'take_if/let_if' for conditional
              aggregation/modification. Additionally there are some conveniences such as automatic 'data.frame' 
              conversion to 'data.table'.
An object that supports automatic differentiation
    of matrix- and multidimensional-valued functions with 
    respect to multidimensional independent variables. 
    Automatic differentiation is via 'forward accumulation'.
Tools for the analysis of population differences
    using the Major Histocompatibility Complex (MHC) genotypes of samples
    having a variable number of alleles (1-4) recorded for each
    individual.  A hierarchical Dirichlet-Multinomial model on the
    genotype counts is used to pool small samples from multiple
    populations for pairwise tests of equality.  Bayesian inference is
    implemented via the 'rstan' package.  Bootstrapped and posterior
    p-values are provided for chi-squared and likelihood ratio tests of
    equal genotype probabilities.
Estimates average treatment effects using model average double robust (MA-DR) estimation. The MA-DR estimator is defined as weighted average of double robust estimators, where each double robust estimator corresponds to a specific choice of the outcome model and the propensity score model. The MA-DR estimator extend the desirable double robustness property by achieving consistency under the much weaker assumption that either the true propensity score model or the true outcome model be within a specified, possibly large, class of models.
Provides a framework which should improve reproducibility and transparency in data processing. It provides functionality such as automatic meta data creation and management, rudimentary quality management, data caching, work-flow management and data aggregation.
    * The title is a wish not a promise. By no means we expect this package to deliver everything what is needed to achieve full reproducibility and transparency, but we believe that it supports efforts in this direction.
Performs distance sampling analyses on a number of species 
    at once and can account for unidentified sightings, model uncertainty 
    and covariate uncertainty. Unidentified sightings refer to sightings 
    which cannot be allocated to a single species but may instead be 
    allocated to a group of species. The abundance of each unidentified 
    group is estimated and then prorated to the species estimates. Model 
    uncertainty should be incorporated when multiple models give equally
    good fit to the data but lead to large differences in estimated
    density / abundance. Covariate uncertainty should be incorporated
    when covariates cannot be measured accurately, for example this is 
    often the case for group size in marine mammal surveys. Variance 
    estimation for these methods is via a non parametric bootstrap. 
    The methods implemented are described in Gerodette T. and Forcada 
    J. (2005) <doi:10.3354/meps291001> Non-recovery of two spotted and 
    spinner dolphin populations in the eastern tropical Pacific Ocean.
This function allows to generate two biological conditions synthetic 
             microarray dataset which has similar behavior to those currently 
             observed with common platforms. User provides a subset of parameters.
             Available default parameters settings can be modified.
A bundle of functions for modifying MAESTRA/MAESPA input files,reading output files, and visualizing the stand in 3D. Handy for running sensitivity analyses, scenario analyses, etc.
Data class for increased interoperability working with spatial-
    temporal data together with corresponding functions and methods (conversions,
    basic calculations and basic data manipulation). The class distinguishes
    between spatial, temporal and other dimensions to facilitate the development
    and interoperability of tools build for it. Additional features are name-based
    addressing of data and internal consistency checks (e.g. checking for the right
    data order in calculations).
Use a 'glmmkin' class object (GMMAT package) from the null model to perform generalized linear mixed model-based single-variant and variant set main effect tests, gene-environment interaction tests, and joint tests for association, as proposed in Wang et al. (2020) <DOI:10.1002/gepi.22351>.
Provides fast and accurate inference for the parameter estimation problem in Ordinary Differential
    Equations, including the case when there are unobserved system components. Implements the MAGI method
    (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) <doi:10.1073/pnas.2020397118>.
A collection of functions for the manipulation and
 analysis of arbitrarily dimensioned arrays.  The original motivation
 for the package was the development of efficient, vectorized
 algorithms for the creation and investigation of magic squares and
 high-dimensional magic hypercubes.
Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.
Magic functions to obtain results from for loops.
Bindings to 'ImageMagick': the most comprehensive open-source image
    processing library available. Supports many common formats (png, jpeg, tiff,
    pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc).
    All operations are vectorized via the Magick++ STL meaning they operate either
    on a single frame or a series of frames for working with layers, collages,
    or animation. In RStudio images are automatically previewed when printed to
    the console, resulting in an interactive editing environment. The latest 
    version of the package includes a native graphics device for creating 
    in-memory graphics or drawing onto images using pixel coordinates.
Enables us to use the functions of the package 'magick' interactively.
Set of utility functions to interact with 'WeMo Switch', a smart 
    plug that can be remotely controlled via wifi. The provided functions make 
    it possible to turn one or more 'WeMo Switch' plugs on and off in a 
    scriptable fashion. More information about 'WeMo Switch' can be found at 
    <http://www.belkin.com/us/p/P-F7C027/>.
An implementation for the multi-task Gaussian processes with common 
    mean framework. Two main algorithms, called 'Magma' and 'MagmaClust', 
    are available to perform predictions for supervised learning problems, in
    particular for time series or any functional/continuous data applications.
    The corresponding articles has been respectively proposed by Arthur Leroy, 
    Pierre Latouche, Benjamin Guedj and Servane Gey (2022) 
    <doi:10.1007/s10994-022-06172-1>, and Arthur Leroy, Pierre Latouche, 
    Benjamin Guedj and Servane Gey (2020) <arXiv:2011.07866>.
    Theses approaches leverage the learning of cluster-specific mean processes,
    which are common across similar tasks, to provide enhanced prediction
    performances (even far from data) at a linear computational cost (in
    the number of tasks).  'MagmaClust' is a generalisation of 'Magma'
    where the tasks are simultaneously clustered into groups, each being
    associated to a specific mean process.  User-oriented functions in the
    package are decomposed into training, prediction and plotting
    functions. Some basic features (classic kernels, training, prediction) of
    standard Gaussian processes are also implemented.
A client for interacting with 'magma', the data warehouse of the 'UCSF
    Mount Etna Data Library'. 'magmaR' includes functions for querying and
    downloading data from 'magma', in order to enable working with such data in R,
    as well as for uploading local data to 'magma'.
Correlates variation within the meta-genome to target species
    phenotype variations in meta-genome with association studies. Follows
    the pipeline described in Chaston, J.M. et al. (2014) <doi:10.1128/mBio.01631-14>.
Implements an interface to the legacy Fortran code from O'Connell and Dobson (1984) <DOI:10.2307/2531148>. Implements Fortran 77 code for the methods developed by Schouten (1982) <DOI:10.1111/j.1467-9574.1982.tb00774.x>. Includes estimates of average agreement for each observer and average agreement for each subject.
Provides a mechanism for chaining commands with a new
    forward-pipe operator, %>%. This operator will forward a value, or the
    result of an expression, into the next function call/expression.
    There is flexible support for the type of right-hand side expressions.
    For more information, see package vignette.  To quote Rene Magritte,
    "Ceci n'est pas un pipe."
Provides a comprehensive graphical user interface for analysis of Affymetrix, Agilent, Illumina, Nimblegen and other microarray data. It can perform miscellaneous tasks such as gene set enrichment and test analyses, identifying gene symbols and building co-expression network. It can also estimate sample size for atleast two-fold expression change. The current version is its slenderized form for compatable and flexible implementation.
A generalised workflow for generation of subject weights to be 
    used in Matching-Adjusted Indirect Comparison (MAIC) per Signorovitch et 
    al. (2012) <doi:10.1016/j.jval.2012.05.004>, Signorovitch et al (2010) 
    <doi:10.2165/11538370-000000000-00000>. In MAIC, unbiased 
    comparison between outcomes of two trials is facilitated by weighting the
    subject-level outcomes of one trial with weights derived such that the 
    weighted aggregate measures of the prognostic or effect modifying variables 
    are equal to those of the sample in the comparator trial. The functions and
    classes included in this package wrap and abstract the process demonstrated
    in the UK National Institute for Health and Care Excellence Decision 
    Support Unit (NICE DSU)'s example (Phillippo et al, (2016) [see URL]),
    providing a repeatable and easily specifiable workflow for producing 
    multiple comparison variable sets against a variety of target studies, with
    preprocessing for a number of aggregate target forms (e.g. mean, median, 
    domain limits).
A collection of easy-to-implement tools for checking whether a MAIC can be conducted. An alternative way of calculating weights is also included. These methods are introduced in Glimm & Yau (2021) <arXiv:2108.01896>.
Easy to use package for sending email notifications with status information from R
Collect  your data on digital marketing campaigns from Mailchimp using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Perform a mail merge (mass email) using the message defined in 
    markdown, the recipients in a 'csv' file, and gmail as the mailing engine. 
    With this package you can parse markdown documents as the body of email, and 
    the 'yaml' header to specify the subject line of the email.  Any '{}' braces 
    in the email will be encoded with 'glue::glue()'. You can preview the email 
    in the RStudio viewer pane, and send (draft) email using 'gmailr'.
Interface to Apache Commons Email to send emails
    from R.
Allows the user to generate a friendly user interface for emails sending. 
    The user can choose from the most popular free email services ('Gmail', 'Outlook', 'Yahoo') and his default email application. 
    The package is a wrapper for the 'Mailtoui' 'JavaScript' library.  See <https://mailtoui.com/#menu> for more information.
Shiny for Open Science to visualize, share, and inventory the
    main existing human datasets for researchers.
Implements methodologies for modelling interval data by Normal
    and Skew-Normal distributions, considering appropriate parameterizations of
    the variance-covariance matrix that takes into account the intrinsic nature of
    interval data, and lead to four different possible configuration structures.
    The Skew-Normal parameters can be estimated by maximum likelihood, while Normal
    parameters may be estimated by maximum likelihood or robust trimmed maximum
    likelihood methods.
A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package 'MajKMeans' are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering.
Create dummy variables from categorical data.
    This package can convert categorical data (factor and ordered) into
    dummy variables and handle multiple columns simultaneously.
    This package enables to select whether a dummy variable for base group
    is included (for principal component analysis/factor analysis) or
    excluded (for regression analysis) by an option.
    'makedummies' function accepts 'data.frame', 'matrix', and
    'tbl' (tibble) class (by 'tibble' package).
    'matrix' class data is automatically converted to 'data.frame' class.
A user-friendly interface for the construction of
        'Makefiles'.
A user-friendly tool for visualizing categorical or group movement.
Tool for easy prior construction and visualization. It helps to formulates joint prior distributions for variance parameters in latent Gaussian models. The resulting prior is robust and can be created in an intuitive way. A graphical user interface (GUI) can be used to choose the joint prior, where the user can click through the model and select priors. An extensive guide is available in the GUI. The package allows for direct inference with the specified model and prior. Using a hierarchical variance decomposition, we formulate a joint variance prior that takes the whole model structure into account. In this way, existing knowledge can intuitively be incorporated at the level it applies to. Alternatively, one can use independent variance priors for each model components in the latent Gaussian model.
Functions that allow you to create your own color palette from an image, using mathematical algorithms.
A suite of tools for transforming an existing workflow into a
    self-documenting pipeline with very minimal upfront costs. Segments of
    the pipeline are specified in much the same way a 'Make' rule is, by
    declaring an executable recipe (which might be an R script), along
    with the corresponding targets and dependencies. When the entire
    pipeline is run through, only those recipes that need to be executed
    will be. Meanwhile, execution metadata is captured behind the scenes
    for later inspection.
This package creates an empty framework of files and
        directories for the "Load, Clean, Func, Do" structure described
        by Josh Reich.
A collection of helper functions that interface with the appropriate
    system utilities to learn about the build environment. Lets you explore 'make' 
    rules to test the local configuration, or query 'pkg-config' to find compiler
    flags and libs needed for building packages with external dependencies. Also
    contains tools to analyze which libraries that a installed R package linked to
    by inspecting output from 'ldd' in combination with information from your
    distribution package manager, e.g. 'rpm' or 'dpkg'. Finally the package provides
    Windows-specific utilities to automatically find or install the suitable version
    of the 'Rtools' build environment, and diagnose some common problems.
Make all elements of a character vector unique. 
    Differs from 'make.unique' by starting at 1 and allowing users to customise suffix format.
Functions that allow you to create your own color palette from an image, using the KMeans algorithm.
R package associated with the Multiple Approximate Kernel Learning (MAKL) algorithm proposed in <doi:10.1093/bioinformatics/btac241>. The algorithm fits multiple approximate kernel learning (MAKL) models that are fast, scalable and interpretable.
MAle Lineage ANalysis by simulating 
    genealogies backwards and imposing short tandem repeats (STR) mutations forwards. 
    Intended for forensic Y chromosomal STR (Y-STR) haplotype analyses. 
    Numerous analyses are possible, e.g. number of matches and meiotic distance to matches. 
    Refer to papers mentioned in citation("malan") (DOI's: 
    <doi:10.1371/journal.pgen.1007028>, 
    <doi:10.21105/joss.00684> and 
    <doi:10.1016/j.fsigen.2018.10.004>).
Find dark genes. These genes are often disregarded due to no detected mutation or differential expression, but are important in coordinating the functionality in cancer networks.
A suite of tools to allow you to download all 
  publicly available parasite rate survey points, mosquito occurrence points and raster surfaces from 
  the 'Malaria Atlas Project' <https://malariaatlas.org/> servers as well as utility functions for plotting
  the downloaded data.
It is designed to work with text written in Bahasa Malaysia. We provide 
    functions and data sets that will make working with Bahasa Malaysia text much easier. 
    For word stemming in particular, we will look up the Malay words in a dictionary and
    then proceed to remove "extra suffix" as explained in Khan, Rehman Ullah, 
    Fitri Suraya Mohamad, Muh Inam UlHaq, Shahren Ahmad Zadi Adruce, 
    Philip Nuli Anding, Sajjad Nawaz Khan, and Abdulrazak Yahya Saleh Al-Hababi
    (2017) <https://ijrest.net/vol-4-issue-12.html> . This package includes
    a dictionary of Malay words that may be used to perform word stemming.
A complete analysis pipeline for matrix-assisted laser
        desorption/ionization-time-of-flight (MALDI-TOF) and other
        two-dimensional mass spectrometry data. In addition to commonly
        used plotting and processing methods it includes distinctive
        features, namely baseline subtraction methods such as
        morphological filters (TopHat) or the statistics-sensitive
        non-linear iterative peak-clipping algorithm (SNIP), peak
        alignment using warping functions, handling of replicated
        measurements as well as allowing spectra with different
        resolutions.
Functions for reading (tab, csv, Bruker fid, Ciphergen
        XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and
        writing (tab, csv, mMass MSD, mzML, imzML) different file
        formats of mass spectrometry data into/from 'MALDIquant'
        objects.
Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data (Palarea-Albaladejo et al. (2018) <doi:10.1093/bioinformatics/btx628>).
An R interface for the Java Machine Learning for Language Toolkit (mallet)
  <http://mallet.cs.umass.edu/> to estimate probabilistic topic models, such
  as Latent Dirichlet Allocation. We can use the R package to read textual 
  data into mallet from R objects, run the Java implementation of mallet 
  directly in R, and extract results as R objects. The Mallet toolkit 
  has many functions, this wrapper focuses on the topic modeling sub-package 
  written by David Mimno. The package uses the rJava package to connect to a 
  JVM.
Data sets related to the Islas Malvinas /// Sets de datos relacionados a las Islas 
  Malvinas - La Nación Argentina ratifica su legítima e imprescriptible soberanía sobre las islas 
  Malvinas, Georgias del Sur y Sándwich del Sur y los espacios marítimos e insulares 
  correspondientes, por ser parte integrante del territorio nacional. La recuperación de dichos 
  territorios y el ejercicio pleno de la soberanía, respetando el modo de vida de sus habitantes y 
  conforme a los principios del Derecho Internacional, constituyen un objetivo permanente e 
  irrenunciable del pueblo argentino.
Designing multi-arm multi-stage studies with (asymptotically) normal endpoints and known variance.
Providing the kubernetes-like class 'ManagedCloudProvider' as a child class of 
  the 'CloudProvider' class in the 'DockerParallel' package. The class is able to
  manage the cloud instance made by the non-kubernetes cloud service.
  For creating a provider for the non-kubernetes cloud service, the developer
  needs to define a reference class inherited from 'ManagedCloudProvider' and
  define the method for the generics runDockerWorkerContainers(),
  getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see
  the vignette in this package and <https://CRAN.R-project.org/package=DockerParallel>.
This will allow easier management of a CRAN-style repository on
    local networks (i.e. not on CRAN). This might be necessary where hosted
    packages contain intellectual property owned by a corporation.
High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. We present MANCIE, a computational method for integrating two genomic data sets with homogenous dimensions from different sources based on a PCA procedure as an approximation to a Bayesian approach.
Several functions can be used to analyze neuroimaging data using multivariate methods based on the 'msma' package. The functions used in the book entitled "Multivariate Analysis for Neuroimaging Data" (2021, ISBN-13: 978-0367255329) are contained.
Provides an algorithm for creating mandalas. From the perspective of classic mathematical curves and rigid movements on the plane, the package allows you to select curves and produce mandalas from the curve. The algorithm was developed based on the book by Alcoforado et. al. entitled "Art, Geometry and Mandalas with R" (2022) in press by the USP Open Books Portal.
Estimates membership for the Mandelbrot set.
Mixture model with overlapping clusters for binary actor-event data. Parameters are estimated in a Bayesian framework. Model and inference are described in Ranciati, Vinciotti, Wit (2017) Modelling actor-event network data via a mixture model under overlapping clusters. Submitted.
Datasets to be used primarily in conjunction with Mango Solutions
    training materials but also for the book 'SAMS Teach Yourself R in 24 Hours' (ISBN: 978-0-672-33848-9).
    Version 1.0-7 is largely for use with the book; however, version 1.1 has a much greater focus on use with
    training materials, whilst retaining compatibility with the book.
Methods for performing genetic risk
        prediction from genotype data.  You can use it to perform risk
        prediction for individuals, or for families with missing data.
Create interactive manhattan, Q-Q and volcano plots that are usable from the R console, 
    in 'Dash' apps, in the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps.
    Hover the mouse pointer over a point to show details or drag a rectangle to
    zoom. A manhattan plot is a popular graphical method for visualizing results
    from high-dimensional data analysis such as a (epi)genome wide association study
    (GWAS or EWAS), in which p-values, Z-scores, test statistics are plotted on a scatter
    plot against their genomic position. Manhattan plots are used for visualizing
    potential regions of interest in the genome that are associated with a phenotype.
    Interactive manhattan plots allow the inspection of specific value (e.g. rs number or
    gene name) by hovering the mouse over a cell, as well as zooming into a region of the
    genome (e.g. a chromosome) by dragging a rectangle around the relevant area.
    This work is based on the 'qqman' package and the 'plotly.js'
    engine. It produces similar manhattan and Q-Q plots as the 'manhattan' and 'qq'
    functions in the 'qqman' package, with the advantage of including extra annotation 
    information and interactive web-based visualizations directly from R. 
    Once uploaded to a 'plotly' account, 'plotly' graphs (and the data behind them) 
    can be viewed and modified in a web browser.
This plot integrates annotation into a manhattan plot. The plot is implemented as a heatmap, which is binned using -log10(p-value) and chromosome position. Annotation currently supported is minor allele frequency and gene function high impact variants.
Provides access to coded election programmes from the Manifesto
    Corpus and to the Manifesto Project's Main Dataset and routines to analyse this
    data. The Manifesto Project <https://manifesto-project.wzb.eu> collects and
    analyses election programmes across time and space to measure the political
    preferences of parties. The Manifesto Corpus contains the collected and
    annotated election programmes in the Corpus format of the package 'tm' to enable
    easy use of text processing and text mining functionality. Specific functions
    for scaling of coded political texts are included.
Implements operations for Riemannian manifolds, e.g., geodesic distance, Riemannian metric, exponential and logarithm maps, etc. Also incorporates random object generator on the manifolds. See Dai, Lin, and Müller (2021) <doi:10.1111/biom.13385>.
An R interface to version 0.3 of the 'ROPTLIB' optimization library
    (see <https://www.math.fsu.edu/~whuang2/> for more information). Optimize real-
    valued functions over manifolds such as Stiefel, Grassmann, and Symmetric
    Positive Definite matrices. For details see Martin et. al. (2020) <doi:10.18637/jss.v093.i01>. 
    Note that the optional ldr package used in some of this package's examples can be obtained from either JSS 
    <https://www.jstatsoft.org/index.php/jss/article/view/v061i03/2886> or from the CRAN archives 
    <https://cran.r-project.org/src/contrib/Archive/ldr/ldr_1.3.3.tar.gz>.
Interactive plotting functions for use within RStudio.
  The manipulate function accepts a plotting expression and a set of
  controls (e.g. slider, picker, checkbox, or button) which are used
  to dynamically change values within the expression. When a value is
  changed using its corresponding control the expression is
  automatically re-executed and the plot is redrawn.
Like package 'manipulate' does for static graphics, this package
    helps to easily add controls like sliders, pickers, checkboxes, etc. that 
    can be used to modify the input data or the parameters of an interactive 
    chart created with package 'htmlwidgets'.
The utility of this package includes finite mixture modeling and model-based clustering through Manly mixture models by Zhu and Melnykov (2016) <DOI:10.1016/j.csda.2016.01.015>. It also provides capabilities for forward and backward model selection procedures.
Chromatin immunoprecipitation followed by high-throughput
    sequencing (ChIP-seq) is the premier technology for profiling genome-wide
    localization of chromatin-binding proteins, including transcription
    factors and histones with various modifications.
    This package provides a robust method for normalizing ChIP-seq
    signals across individual samples or groups of samples. It also designs
    a self-contained system of statistical models for calling differential
    ChIP-seq signals between two or more biological conditions as well as
    for calling hypervariable ChIP-seq signals across samples. Refer to
    Tu et al. (2021) <doi:10.1101/gr.262675.120> and
    Chen et al. (2022) <doi:10.1186/s13059-022-02627-9>
    for associated statistical details.
Implemented are various tests for semi-parametric repeated measures
    and general MANOVA designs that do neither assume multivariate normality nor
    covariance homogeneity, i.e., the procedures are applicable for a wide range
    of general multivariate factorial designs. In addition to asymptotic inference
    methods, novel bootstrap and permutation approaches are implemented as well. These provide more
    accurate results in case of small to moderate sample sizes. Furthermore, post-hoc 
    comparisons are provided for the multivariate analyses.
    Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.
The number of biological databases is growing rapidly, but different databases use different IDs to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed 'MantaID', a data-driven, machine-learning based approach that automates identifying IDs on a large scale. The 'MantaID' model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within two minutes. 'MantaID' supports the discovery and exploitation of ID patterns from large quantities of databases. (e.g., up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application, and APIs were also developed for 'MantaID' to improve applicability. To our knowledge, 'MantaID' is the first tool that enables an automatic, quick, accurate, and comprehensive identification of large quantities of IDs, and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases.
This is the core package for the many packages universe.
    It includes functions to help researchers work with and contribute to 
    event datasets on global governance.
Frequently one needs a convenient way to build and tune
             several models in one go.The goal is to provide a number of machine learning convenience 
             functions. It provides the ability to build, tune and obtain predictions of 
             several models in one function. The models are built using functions from
             'caret' with easier to read syntax.
             Kuhn(2014) <arXiv:1405.6974>.
Computes indirect effects, conditional effects, and conditional
  indirect effects in a structural equation model or path model after model
  fitting, with no need to define any user parameters or label any paths in
  the model syntax. Can also form bootstrap confidence intervals by doing
  bootstrapping only once and reusing the bootstrap estimates in all
  subsequent computations. Supports bootstrap confidence intervals for
  standardized (partially or completely) indirect effects, conditional
  effects, and conditional indirect effects as described in Cheung (2009)
  <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui, and Vong (2022)
  <doi:10.1037/hea0001188>. Model fitting can be done by structural equation
  modeling using lavaan() or regression using lm().
Performs the multiple testing procedures of Cox (2011) <doi:10.5170/CERN-2011-006> and Wong and Cox (2007) <doi:10.1080/02664760701240014>.
A set of evolutionary algorithms to solve many-objective optimization. 
    Hybridization between the algorithms are also facilitated. Available algorithms are:
    'SMS-EMOA' <doi:10.1016/j.ejor.2006.08.008>
    'NSGA-III' <doi:10.1109/TEVC.2013.2281535>
    'MO-CMA-ES' <doi:10.1145/1830483.1830573>
    The following many-objective benchmark problems are also provided: 
    'DTLZ1'-'DTLZ4' from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and
    'WFG4'-'WFG9' from Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.
Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).
Electronic health records (EHR) linked with biorepositories are 
    a powerful platform for translational studies. A major bottleneck exists 
    in the ability to phenotype patients accurately and efficiently. 
    Towards that end, we developed an automated high-throughput 
    phenotyping method integrating International 
    Classification of Diseases (ICD) codes and narrative data extracted 
    using natural language processing (NLP). Specifically, our proposed method, 
    called MAP (Map Automated Phenotyping algorithm), fits an ensemble of latent 
    mixture models on aggregated ICD and NLP counts along with healthcare 
    utilization. The MAP algorithm yields a predicted probability of phenotype 
    for each patient and a threshold for classifying subjects with phenotype 
    yes/no (See Katherine P. Liao, et al. (2019) <doi:10.1101/587436>.).
Allows the user to generate a list of features (gene, pseudo, RNA,
    CDS, and/or UTR) directly from NCBI database for any species with a current 
    build available. Option to save downloaded and formatted files is available, 
    and the user can prioritize the feature list based on type and assembly builds 
    present in the current build used. The user can then use the list of features 
    generated or provide a list to map a set of markers (designed for SNP markers 
    with a single base pair position available) to the closest feature based on 
    the map build. This function does require map positions of the markers to be 
    provided and the positions should be based on the build being queried through 
    NCBI.
Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) <doi:10.1016/j.ijforecast.2013.09.006>.
Unbiased estimators of overall and per-class thematic map accuracy and area published in Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015> and Stehman (2014) <doi:10.1080/01431161.2014.930207>.
Performs maximum a posteriori Bayesian estimation of individual pharmacokinetic parameters from a model defined in 'mrgsolve', typically for model-based therapeutic drug monitoring. Internally computes an objective function value from model and data, performs optimization and returns predictions in a convenient format. The performance of the package was described by Le Louedec et al (2021) <doi:10.1002/psp4.12689>.
Includes support for 'Mapbox' Navigation APIs, including directions, 
  isochrones, and route optimization; the Search API for forward and reverse geocoding; 
  the Maps API for interacting with 'Mapbox' vector tilesets and visualizing 
  'Mapbox' maps in R; and 'Mapbox Tiling Service' and 'tippecanoe' for generating map tiles.
  See <https://docs.mapbox.com/api/> for more information about the 'Mapbox' APIs.
Makes 'Mapbox GL JS' <https://docs.mapbox.com/mapbox-gl-js/api/>,
  an open source JavaScript library that uses WebGL to render interactive maps,
  available within R via the 'htmlwidgets' package. Visualizations can be used from the R console,
  in R Markdown documents and in Shiny apps.
A variety of functions that make it easy to plot standard choropleth maps as well as choropleth alternatives in 'ggplot2'.
Geospatial shapefile data of China administrative divisions to the county/district-level.
Supplement to maps package, providing some larger and/or
	higher-resolution databases. NOTE: this is a legacy package. The world map is out-dated.
Provides a mechanism to plot an interactive map using 'Mapbox GL' 
		(<https://docs.mapbox.com/mapbox-gl-js/api/>), a javascript library for interactive maps,
		and 'Deck.gl' (<https://deck.gl/>), a javascript library which uses 'WebGL' for 
		visualising large data sets.
It covers approaches to multivariate analysis of variance, PERMANOVA and a Bayesian analysis, presenting an assumption test section together with a decision diagram that will allow selecting the appropriate technique for the analysis. The presentation of results is through an interactive panel, in which you can view automatic conclusions based on the tests, dynamic graphics through 'Highchart', also the package uses 'Stan' for Bayesian analysis.
Suite of interactive functions and helpers for selecting and editing
    geospatial data.
Contains functions for mapping odds ratios, hazard ratios, or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) or Cox models for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting linearly for confounding variables, using methods described by Kelsall and Diggle (1998), Webster at al. (2006), and Bai et al. (2020).  Includes convenient functions for mapping point estimates and confidence intervals, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).
Mapping Averaged Pairwise Information (MAPI) is an
    exploratory method providing graphical representations summarizing the
    spatial variation of pairwise metrics (eg. distance, similarity
    coefficient, ...) computed between georeferenced samples.
Regularly spaced grids containing continuous data are transformed
    to contour polygons. A grid can be defined by a data.frame (x, y, value),
    an 'sf' object or a raster from 'terra'.
A genetic analysis tool and variance component 
    model for identifying marginal epistasis between pathways 
    and the rest of the genome. 'MAPITR' uses as input a matrix 
    of genotypes, a vector of phenotypes, and a list of 
    pathways. 'MAPITR' then iteratively tests each pathway for 
    epistasis between any variants within the pathway versus 
    any variants remaining in the rest of the genome. 'MAPITR'
    returns results in the form of p-values for every pathway 
    indicating whether the null model of there being no 
    epistatic interactions between a pathway and the rest of 
    the genome can be rejected.
Allows for robust probabilistic analysis of multi-sample spatial transcriptomics experiments (Allen et. al, 2021 <doi:10.1101/2021.06.23.449615>).
Biodiversity areas, especially primary forest, serve a
    multitude of functions for local economy, regional functionality of
    the ecosystems as well as the global health of our planet. Recently,
    adverse changes in human land use practices and climatic responses to
    increased greenhouse gas emissions, put these biodiversity areas under
    a variety of different threats. The present package helps to analyse a
    number of biodiversity indicators based on freely available
    geographical datasets. It supports computational efficient routines
    that allow the analysis of potentially global biodiversity portfolios.
    The primary use case of the package is to support evidence based
    reporting of an organization's effort to protect biodiversity areas
    under threat and to identify regions were intervention is most duly
    needed.
A minimal, light-weight set of tools for producing nice looking maps in R, with support for map projections.
Maps are an important tool to visualise variables distribution across different spatial object. The mapping process require to link the data with coordinates and then generate the correspondent map. This package provide coordinates, linking and mapping functions for an automatic, flexible and easy approach of mapping workflow of different geographical statistical unit.Geographical coordinates are provided in the package and automatically linked with the input data to generate maps with internal provided functions or external functions.provide an easy, flexible and automatic approach to potentially download updated coordinates, to link statistical units with coordinates and to aggregate variables based on the spatial hierarchy of units. The object returned from the package can be used for thematic maps with the build-in functions provided in mapping or with other packages already available.
Easily create functions to map between different sets of values,
  such as for re-labelling categorical variables.
Create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.
Construction of genetic maps in autopolyploid full-sib populations. 
             Uses pairwise recombination fraction estimation as the first 
             source of information to sequentially position allelic variants 
             in specific homologues. For situations where pairwise analysis has 
             limited power, the algorithm relies on the multilocus likelihood 
             obtained through a hidden Markov model (HMM). For more detail,
             please see  Mollinari and Garcia (2019) <doi:10.1534/g3.119.400378> 
             and Mollinari et al. (2020) <doi:10.1534/g3.119.400620>.
Provides one function, which is a wrapper around purrr::map() with some extras on top, including parallel computation, progress bars, error handling, and result caching.
Converts latitude/longitude into projected coordinates.
Utilities for visualizing species occurrence data. Includes
    functions to visualize occurrence data from 'spocc', 'rgbif',
    and other packages. Mapping options included for base R plots, 'ggplot2',
    'leaflet' and 'GitHub' 'gists'.
Display of maps.  Projection code and larger maps are in
             separate packages ('mapproj' and 'mapdata').
Interface to the 'Google Maps' APIs: (1) routing directions based on the 'Directions' API, returned as 'sf' objects, either as single feature per alternative route, or a single feature per segment per alternative route; (2) travel distance or time matrices based on the 'Distance Matrix' API; (3) geocoded locations based on the 'Geocode' API, returned as 'sf' objects, either points or bounds; (4) map images using the 'Maps Static' API, returned as 'stars' objects.
Enables preparation of maps to be printed and drawn on.
    Modified maps can then be scanned back in, and hand-drawn marks
    converted to spatial objects.
Create and integrate thematic maps in your workflow. This package
    helps to design various cartographic representations such as proportional
    symbols, choropleth or typology maps. It also offers several functions to
    display layout elements that improve the graphic presentation of maps
    (e.g. scale bar, north arrow, title, labels). 'mapsf' maps 'sf' objects on
    'base' graphics.
Administrative Boundaries of Spain at several levels
    (Autonomous Communities, Provinces, Municipalities) based on the
    'GISCO' 'Eurostat' database <https://ec.europa.eu/eurostat/web/gisco>
    and 'CartoBase SIANE' from 'Instituto Geografico Nacional'
    <https://www.ign.es/>.  It also provides a 'leaflet' plugin and the
    ability of downloading and processing static tiles.
Information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru.
Local adaptation and evaluation of digital soil maps in raster 
    format by use of point location soil property data.
To create maps from tiles, 'maptiles' downloads, composes and
    displays tiles from a large number of providers (e.g. 'OpenStreetMap',
    'Stamen', 'Esri', 'CARTO', or 'Thunderforest').
Please note that 'maptools' will be retired during 2023, plan transition at your earliest convenience; some functionality will be moved to 'sp'. Set of tools for manipulating geographic data. It includes binary access to 'GSHHG' shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as 'PBSmapping', 'spatstat.geom', 'maps', and others.
Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet Allocation) in text analysis,
	as described in Taddy (2012) 'On estimation and selection for topic models'.  Previous versions of this code were included as part of the 'textir' package.  If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.
Functions with example data for graphing, pruning, and
        mapping models from hierarchical clustering, and classification
        and regression trees.
Quickly and conveniently create interactive
    visualisations of spatial data with or without background maps.
    Attributes of displayed features are fully queryable via pop-up
    windows. Additional functionality includes methods to visualise true-
    and false-color raster images and bounding boxes.
R functions for the estimation and eigen-decomposition of multivariate autoregressive models.
Multiplicative AR(1) with Seasonal is a stochastic
  process model built on top of AR(1). The package provides the
  following procedures for MAR(1)S processes: fit, compose, decompose,
  advanced simulate and predict.
Library that supports visual interpretation of hierarchical composite
    endpoints (HCEs). HCEs are complex constructs used as primary endpoints in
    clinical trials, combining outcomes of different types into ordinal endpoints,
    in which each patient contributes the most clinically important event (one and
    only one) to the analysis.
Computation of various Markovian models for categorical data
    including homogeneous Markov chains of any order, MTD models, Hidden Markov
    models, and Double Chain Markov Models.
A set of tools for likelihood-based estimation, model selection and testing of two- and three-range shift and migration models for animal movement data as described in Gurarie et al. (2017) <doi: 10.1111/1365-2656.12674>.  Provided movement data (X, Y and Time), including irregularly sampled data, functions estimate the time, duration and location of one or two range shifts, as well as the ranging area and auto-correlation structure of the movment.  Tests assess, for example, whether the shift was "significant", and whether a two-shift migration was a true return migration.
Datasets, constants, conversion factors, and utilities for 'MArine', 'Riverine',
             'Estuarine', 'LAcustrine' and 'Coastal' science. 
             The package contains among others: (1) chemical and
             physical constants and datasets, e.g. atomic weights, gas
             constants, the earths bathymetry; (2) conversion factors
             (e.g. gram to mol to liter, barometric units,
             temperature, salinity); (3) physical functions, e.g. to
             estimate concentrations of conservative substances, gas
             transfer and diffusion coefficients, the Coriolis force
             and gravity; (4) thermophysical properties of the
             seawater, as from the UNESCO polynomial or from the more
             recent derivation based on a Gibbs function.
Local recombination rates are graphically estimated across a genome using Marey maps.
Likelihood inference based on higher order approximations 
  for linear nonnormal regression models.
The target of 'margaret' is help to extract data from Minciencias to analyze scientific production in Colombia.
Fits joint marginal conditional models for multivariate longitudinal data, as in Proudfoot, Faig, Natarajan, and Xu (2018) <doi:10.1002/sim.7552>. Development of this package was supported by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Compute and plot adjusted predictions, contrasts, marginal effects, and marginal means for over 70 classes of statistical models in R. Conduct linear and non-linear hypothesis tests using the delta method.
Estimates risk as a function of a marker by integrating over other covariates in a conditional risk model.
Provides the ability to perform "Marginal Mediation"--mediation
   wherein the indirect and direct effects are in terms of the average marginal effects 
   (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). 
   The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package.
   This framework allows the use of categorical mediators and outcomes with little change in interpretation
   from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details 
   on the method.
An R port of Stata's 'margins' command, which can be used to
    calculate marginal (or partial) effects from model objects.
Multivariate ARIMA and ARIMA-X estimation using Spliid's 
    algorithm (marima()) and simulation (marima.sim()).
Miscellaneous functions and wrappers for development in other 
    packages created, maintained by Jordan Mark Barbone.
Render Markdown to full HTML documents with the 'commonmark'
    package. Markdown is a plain-text formatting
    syntax that can be converted to 'XHTML' or other formats. See
    <https://en.wikipedia.org/wiki/Markdown> for more information about Markdown.
An R-Shiny module containing a "markdownInput". This input allows
    the user to write some markdown code and to preview the result. This input
    has been inspired by the "comment" window of <https://github.com/>.
Functions for fitting various models to capture-recapture data
    including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and
    the multi-variate state model structure for survival
    estimation and POPAN structured Jolly-Seber models for abundance estimation.
    There are also Hidden Markov model (HMM) implementations of CJS and multistate
    models with and without state uncertainty and a simulation capability for HMM
    models.
Implementation of the 'MarkerPen' algorithm, short for marker gene detection
    via penalized principal component analysis, described in the paper by Qiu, Wang, Lei,
    and Roeder (2020, <doi:10.1101/2020.11.07.373043>). 'MarkerPen' is a semi-supervised
    algorithm for detecting marker genes by combining prior marker information with bulk
    transcriptome data.
For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be a marketing event or some other local business tactic that is being tested. The workflow implemented in the Market Matching package utilizes dynamic time warping (the 'dtw' package) to do the matching and the 'CausalImpact' package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages. In addition, if you don't have a chosen set of test markets to match, the Market Matching package can provide suggested test/control market pairs and pseudo prospective power analysis (measuring causal impact at fake interventions).
Facilitates tidy calculation of popular quantitative marketing 
    metrics. It also includes functions for doing analysis that will help
    marketers and data analysts better understand the drivers and/or trends
    of these metrics. These metrics include Customer Experience Index 
    <https://go.forrester.com/analytics/cx-index/> and Net Promoter Score 
    <https://www.netpromoter.com/know/>.
Provides estimation methods for markets in equilibrium and
    disequilibrium. Supports the estimation of an equilibrium and
    four disequilibrium models with both correlated and independent shocks.
    Also provides post-estimation analysis tools, such as aggregation,
    marginal effect, and shortage calculations. The estimation methods are
    based on full information maximum likelihood techniques given in
    Maddala and Nelson (1974) <doi:10.2307/1914215>. They are implemented
    using the analytic derivative expressions calculated in
    Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>. Standard
    errors can be estimated by adjusting for heteroscedasticity or clustering.
    The equilibrium estimation constitutes a case of a system of linear,
    simultaneous equations. Instead, the disequilibrium models replace the
    market-clearing condition with a non-linear,
    short-side rule and allow for different specifications of price dynamics.
Automatic marking of R assignments for students and teachers based
    on 'testthat' test suites.
Allows for fitting of maximum likelihood models using Markov chains
    on phylogenetic trees for analysis of discrete character data. Examples of such
    discrete character data include restriction sites, gene family presence/absence,
    intron presence/absence, and gene family size data. Hypothesis-driven user-
    specified substitution rate matrices can be estimated. Allows for biologically
    realistic models combining constrained substitution rate matrices, site rate
    variation, site partitioning, branch-specific rates, allowing for non-stationary
    prior root probabilities, correcting for sampling bias, etc. See Dang and Golding 
    (2016) <doi:10.1093/bioinformatics/btv541> for more details.
Functions and S4 methods to create and manage discrete time Markov
    chains more easily. In addition functions to perform statistical (fitting
    and drawing random variates) and probabilistic (analysis of their structural
    proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>.
Functions for cost-optimal control charts with a focus on health care applications. Compared to assumptions in traditional control chart theory, here, we allow random shift sizes, random repair and random sampling times. The package focuses on X-bar charts with a sample size of 1 (representing the monitoring of a single patient at a time). The methods are described in Zempleni et al. (2004) <doi:10.1002/asmb.521>, Dobi and Zempleni (2019) <doi:10.1002/qre.2518> and Dobi and Zempleni (2019) <http://ac.inf.elte.hu/Vol_049_2019/129_49.pdf>.
The inference in multi-state models is traditionally performed under
             a Markov assumption that claims that past and future of the process
             are independent given the present state. In this package, we 
             consider tests of the Markov assumption that are applicable to 
             general multi-state models. Three approaches using existing 
             methodology are considered: a simple method based on including
             covariates depending on the history in Cox models for the transition
             intensities; methods based on measuring the discrepancy of the 
             non-Markov estimators of the transition probabilities to the 
             Markov Aalen-Johansen estimators; and, finally, methods that were
             developed by considering summaries from families of log-rank 
             statistics where patients are grouped by the state occupied of the
             process at a particular time point (see Soutinho G, Meira-Machado L
             (2021) <doi:10.1007/s00180-021-01139-7> and Titman AC, Putter H
             (2020) <doi:10.1093/biostatistics/kxaa030>).
A collection of tools for analyzing significance of 
    Markowitz portfolios.
Import xyz data from the NOAA (National Oceanic and Atmospheric Administration, <https://www.noaa.gov>), GEBCO (General Bathymetric Chart of the Oceans, <https://www.gebco.net>) and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.
To implement a model-averaging approach with different renewal
    models, with a primary focus on forecasting large earthquakes. Based on
    six renewal models (i.e., Poisson, Gamma, Log-Logistics, Weibull,
    Log-Normal and BPT), model-averaged point estimates are calculated using
    AIC (or BIC) weights. Additionally, both percentile and studentized
    bootstrapped model-averaged confidence intervals are constructed. In
    comparison, point and interval estimation from the individual or "best"
    model (determined via model selection) can be retrieved.
This algorithm provides a numerical solution to the
        problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than
        the Gauss-Newton-like algorithm when starting from points very
        far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 <doi:10.32614/RJ-2021-089>.
Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.
The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models fit to multivariate time-series data.  Fitting is primarily via an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided.  MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model.  Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the Hessian approximation and via bootstrapping and calculation of auxiliary residuals for detecting outliers and shocks.  The user guide shows examples of using MARSS for parameter estimation for a variety of applications, model selection, dynamic factor analysis, outlier and shock detection, and addition of covariates.  Online workshops (lectures, eBook, and computer labs) at <https://atsa-es.github.io/>  See the NEWS file for update information.
Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables.
Alternative splicing represents an additional and underappreciated layer of complexity underlying gene expression profiles. Nevertheless, there remains hitherto a paucity of software to investigate splicing dynamics at single-cell resolution. 'MARVEL' enables splicing analysis of single-cell RNA-sequencing data generated from plate- and droplet-based library preparation methods.
Genome-wide association analysis with membership information, variance adjustment, and correlated responses.
An S4 implementation of the unbiased extension of
    the model- assisted synthetic-regression estimator proposed by
    Mandallaz (2013) <DOI:10.1139/cjfr-2012-0381>, Mandallaz et al. (2013)
    <DOI:10.1139/cjfr-2013-0181> and Mandallaz (2014)
    <DOI:10.1139/cjfr-2013-0449>.  It yields smaller variances than the
    standard bias correction, the generalised regression estimator.
A set of model-assisted survey estimators and corresponding
    variance estimators for single stage, unequal probability, without replacement
    sampling designs.  All of the estimators can be written as a generalized 
    regression estimator with the Horvitz-Thompson, ratio, post-stratified, and 
    regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6).
    Two of the estimators employ a statistical learning model as the assisting model:
    the elastic net regression estimator, which is an extension of the lasso regression
    estimator given by McConville et al. (2017) <doi:10.1093/jssam/smw041>, and the 
    regression tree estimator described in McConville and Toth (2017) <arXiv:1712.05708>. 
    The variance estimators which approximate the joint inclusion probabilities can
    be found in Berger and Tille (2009) <doi:10.1016/S0169-7161(08)00002-3> and the
    bootstrap variance estimator is presented in Mashreghi et al. (2016) 
    <doi:10.1214/16-SS113>.
Implements the multivariate adaptive shrinkage (mash)
    method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for
    estimating and testing large numbers of effects in many conditions
    (or many outcomes). Mash takes an empirical Bayes approach to
    testing and effect estimation; it estimates patterns of similarity
    among conditions, then exploits these patterns to improve accuracy
    of the effect estimates. The core linear algebra is implemented in
    C++ for fast model fitting and posterior computation.
Mask ranges based on expert 
    knowledge or remote sensing layers. These tools can 
    be combined to quantitatively and reproducibly 
    generate a new map or to update an existing map.
    Methods include expert opinion and data-driven 
    tools to generate thresholds for binary masks.
Use a consistent syntax to create data structures
    of common statistical techniques that can be continued in a pipe
    chain.  Design the analysis, add settings and variables, construct the
    results, and polish the final structure. Rinse and repeat for any
    number of statistical techniques.
Functions and datasets to support Venables and Ripley,
  "Modern Applied Statistics with S" (4th edition, 2002).
Mass measurement corrections and uncertainties using calibration
    data, as recommended by EURAMET's guideline No. 18 (2015) 
    ISBN:978-3-942992-40-4 . The package provides classes, functions, and 
    methods for storing information contained in calibration certificates
    and converting balance readings to both conventional mass and real mass.
    For the latter, the Magnitude of the Air Buoyancy Correction factor employs
    models (such as the CIMP-2007 formula revised by Picard, Davis, Gläser, and
    Fujii (2008) <doi:10.1088/0026-1394/45/2/004>) to estimate the local air
    density using measured environmental conditions.
Some enhancements, extensions and additions
    to the facilities of the recommended 'MASS' package 
    that are useful mainly for teaching purposes, with
    more convenient default settings and user interfaces.
    Key functions from 'MASS' are imported and re-exported
    to avoid masking conflicts.  In addition we provide
    some additional functions mainly used to illustrate
    coding paradigms and techniques, such as Gramm-Schmidt
    orthogonalisation and generalised eigenvalue problems.
Constructing matrices for quick prototyping can be a nuisance, 
    requiring the user to think about how to fill the matrix with values using 
    the matrix() function. The %<-% operator solves that issue by allowing the 
    user to construct matrices using code that shows the actual matrices.
Friendly implementation of the Mann-Whitney-Wilcoxon test for competitive gene set enrichment analysis.
Estimation equations are from a variety of sources and associated error estimation.
The primary aim of 'MasterBayes' is to use MCMC techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data.  Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled 'C++' for efficiency.
Analyzes production and dispersal of seeds dispersed from trees and recovered in seed traps.  Motivated by long-term inventory plots where seed collections are used to infer seed production by each individual plant.
Simulates Multidimensional Adaptive Testing using the multidimensional three-parameter logistic model as described in Segall (1996) <doi:10.1007/BF02294343>, van der Linden (1999) <doi:10.3102/10769986024004398>, Reckase (2009) <doi:10.1007/978-0-387-89976-3>, and Mulder & van der Linden (2009) <doi:10.1007/s11336-008-9097-5>.
Calculates Model-Averaged Tail Area Wald
    (MATA-Wald) confidence intervals, and MATA-Wald confidence densities
    and distributions, which are constructed using single-model
    frequentist estimators and model weights. See Turek and Fletcher
    (2012) <doi:10.1016/j.csda.2012.03.002> and Fletcher et al (2019)
    <doi:10.1007/s10651-019-00432-5> for details.
Conveniently log everything you type into the R console. Logs are
    are stored as tidy data frames which can then be analyzed using 'tidyverse'
    style tools.
Multivariate matching in observational studies typically has two goals: 1. to construct 
    treated and control groups that have similar distribution of observed covariates and 2. to produce 
    matched pairs or sets that are homogeneous in a few priority variables. This packages implements a
    network-flow-based method built around a tripartite graph that can simultaneously achieve both goals.
    The package also implements a template matching algorithm using a variant of the tripartite graph 
    design. A brief description of the workflow and some examples are given in the vignette. A more elaborated
    tutorial can be found at <https://www.researchgate.net/publication/359513837_Tutorial_for_R_Package_match2C>.
Provides a wrapper for the some basic functionality around
    the 'Matchbook' <http://www.matchbook.com> REST API. It features calls to get events, markets and runners
    in data frame format. It features functions for bet placement and position
    management and also allows reporting of settled bet transactions. Note: this
    package uses the back-lay format. The default for odds type, currency and
    language are set according to those of the registered user account.
Provides functions for multivariate and propensity score matching 
             and for finding optimal balance based on a genetic search algorithm. 
             A variety of univariate and multivariate metrics to
             determine if balance has been obtained are also provided. For
             details, see the paper by Jasjeet Sekhon 
             (2007, <doi:10.18637/jss.v042.i07>).
Implements structural estimators to correct for
    the sample selection bias from observed outcomes in matching
    markets. This includes one-sided matching of agents into
    groups as well as two-sided matching of students to schools.
    The package also contains algorithms to find stable matchings
    in the three most common matching problems: the stable roommates
    problem, the college admissions problem, and the house
    allocation problem.
Computes matching algorithms quickly using Rcpp.
    Implements the Gale-Shapley Algorithm to compute the stable
    matching for two-sided markets, such as the stable marriage
    problem and the college-admissions problem. Implements Irving's
    Algorithm for the stable roommate problem. Implements the top
    trading cycle algorithm for the indivisible goods trading problem.
Selects matched samples of the original treated and
    control groups with similar covariate distributions -- can be
    used to match exactly on covariates, to match on propensity
    scores, or perform a variety of other matching procedures.  The
    package also implements a series of recommendations offered in
    Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 
    'gurobi' package, which is not on CRAN, is optional and comes with 
    an installation of the Gurobi Optimizer, available at 
    <https://www.gurobi.com>.)
Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies.
Provides flexible dictionary-based
    cleaning that allows users to specify implicit and explicit missing data,
    regular expressions for both data and columns, and global matches, while
    respecting ordering of factors. This package is part of the 'RECON'
    (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Performs multilevel matches for data with cluster-
	level treatments and individual-level outcomes using a network 
	optimization algorithm.  Functions for checking balance at the 
	cluster and individual levels are also provided, as are methods 
	for permutation-inference-based outcome analysis.  Details in 
	Pimentel et al. (2018) <doi:10.1214/17-AOAS1118>.  The optmatch 
	package, which is useful for running many of the provided 
	functions, may be downloaded from Github at 
	<https://github.com/markmfredrickson/optmatch> if not available on 
	CRAN.
Inspired by pattern matching and enum types in Rust
    and many functional programming languages, this package offers
    an updated version of the 'switch' function called 'Match' that
    accepts atomic values, functions, expressions, and enum variants.
    Conditions and return expressions are separated by '->' and 
    multiple conditions can be associated with the same return expression
    using '|'. 'Match' also includes support for 'fallthrough'. The 
    package also replicates the Result and Option enums from Rust.
Provides the necessary tools for the pre-processing techniques of matching and weighting multiply imputed datasets to control for effects of confounders and to reduce the degree of dependence on certain modeling assumptions in studying the causal associations between an exposure and an outcome. This package includes functions to perform matching within and across the multiply imputed datasets using several matching methods, to estimate weights of units in the imputed datasets using several weighting methods, to calculate the causal effect estimate in each matched or weighted dataset using parametric or non-parametric statistical models, and to pool the obtained estimates from these models according to Rubin's rules (please see <https://journal.r-project.org/archive/2021/RJ-2021-073/> for details).
Transferring over a code base from Matlab to R is often a repetitive
    and inefficient use of time. This package provides a translator for Matlab /
    Octave code into R code. It does some syntax changes, but most of the heavy
    lifting is in the function changes since the languages are so similar.
    Options for different data structures and the functions that can be changed
    are given. The Matlab code should be mostly in adherence to the standard
    style guide but some effort has been made to accommodate different number of
    spaces and other small syntax issues. This will not make the code more R
    friendly and may not even run afterwards. However, the rudimentary syntax,
    base function and data structure conversion is done quickly so that the
    maintainer can focus on changes to the design structure.
You can apply image processing effects that modifies the perceived material properties of objects
    in photos, such as gloss, smoothness, and blemishes. This is an implementation of the algorithm proposed by
    Boyadzhiev et al. (2015) "Band-Sifting Decomposition for Image Based Material Editing".
    Documentation and practical tips of the package is available at <https://github.com/tsuda16k/materialmodifier>.
Provides 'MathJax' and macros to enable its use within Rd files for rendering equations in the HTML help files.
Given an image of a formula (typeset or handwritten) this package
    provides calls to the 'Mathpix' service to produce the 'LaTeX' code which should
    generate that image, and pastes it into a (e.g. an 'rmarkdown') document. 
    See <https://docs.mathpix.com/> for full details. 'Mathpix' is an external service 
    and use of the API is subject to their terms and conditions.
Allows users familiar with MATLAB to use MATLAB-named functions in
    R. Several basic MATLAB functions are written in this package to mimic the
    behavior of their original counterparts, with more to come as this package
    grows.
Provides users to call MATLAB from using the "system" command.
    Allows users to submit lines of code or MATLAB m files.
    This is in comparison to 'R.matlab', which creates a MATLAB server.
A collection of matrix functions for teaching and learning matrix
    linear algebra as used in multivariate statistical methods. These functions are
    mainly for tutorial purposes in learning matrix algebra ideas using R. In some
    cases, functions are provided for concepts available elsewhere in R, but where
    the function call or name is not obvious. In other cases, functions are provided
    to show or demonstrate an algorithm. In addition, a collection of functions are
    provided for drawing vector diagrams in 2D and 3D.
A set of functions, classes and methods for performing ABC and ABC/XYZ analyses, identifying overperforming, underperforming and constantly performing items, and plotting, analyzing as well as predicting the temporal development of items.
Matrix clustering with finite mixture models.
A general framework for computing powers of matrices.  A
   key feature is the capability for users to write callback functions,
   called after each iteration, thus enabling customization for specific
   applications.  Diverse types of matrix classes/matrix multiplication
   are accommodated.  If the multiplication type computes in parallel,
   then the package computation is also parallel.
Provides functions, which make matrix creation conciser 
             (such as the core package's function m() for rowwise matrix definition or 
             runifm() for random value matrices).
             Allows to set multiple matrix values at once, by using list of formulae. 
             Provides additional matrix operators and dedicated plotting function.
A rich hierarchy of matrix classes, including triangular,
   symmetric, and diagonal matrices, both dense and sparse and with
   pattern, logical and numeric entries.   Numerous methods for and
   operations on these matrices, using 'LAPACK' and 'SuiteSparse' libraries.
A collection of functions to support matrix calculations
        for probability, econometric and numerical analysis. There are
        additional functions that are comparable to APL functions which
        are useful for actuarial models such as pension mathematics.
        This package is used for teaching and research purposes at the
        Department of Finance and Risk Engineering, New York
        University, Polytechnic Institute, Brooklyn, NY 11201.
        Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326.
        Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507.
        Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563.
Computation and visualization of matrix correlation coefficients.
    The main method is the Similarity of Matrices Index, while various related
    measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2, adjusted RV, Rozeboom's
    linear correlation and Coxhead's coefficient are included
    for comparison and flexibility.
Tools for phase-type distributions including the following variants:
    continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. 
    Methods for functional evaluation, simulation and estimation using the 
    expectation-maximization (EM) algorithm are provided for all models.
    The methods of this package are based on the following references.
    Asmussen, S., Nerman, O., & Olsson, M. (1996) <https://www.jstor.org/stable/4616418>,
    Olsson, M. (1996) <https://www.jstor.org/stable/4616419>,
    Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>,
    Albrecher, H., Bladt, M., & Yslas, J. (2020) <doi:10.1111/sjos.12505>,
    Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>,
    Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>,
    Bladt, M. (2022) <doi:10.1017/asb.2021.40>,
    Bladt, M. (2022). <arXiv:2110.05179>,
    Albrecher, H., Bladt, M., & Mueller, A. (2022) <arXiv:2207.01279>.
Matrix eQTL is designed for fast eQTL analysis on large datasets.
        Matrix eQTL can test for association between genotype 
        and gene expression using linear regression 
        with either additive or ANOVA genotype effects.
        The models can include covariates to account for factors 
        as population stratification, gender, and clinical variables. 
        It also supports models with heteroscedastic and/or correlated errors,
        false discovery rate estimation and 
        separate treatment of local (cis) and distant (trans) eQTLs.
        For more details see Shabalin (2012) <doi:10.1093/bioinformatics/bts163>.
Extends sparse matrix and vector classes from the 'Matrix' package by providing: 
  (a) Methods and operators that work natively on CSR formats (compressed sparse row, 
  a.k.a. 'RsparseMatrix') such as slicing/sub-setting, assignment, rbind(), 
  mathematical operators for CSR and COO such as addition ("+") or sqrt(), and methods such as diag(); 
  (b) Multi-threaded matrix multiplication and cross-product for many <sparse, dense> types, 
  including the 'float32' type from 'float'; 
  (c) Coercion methods between pairs of classes which are not present in 'Matrix', 
  such as 'dgCMatrix' -> 'ngRMatrix', as well as convenience conversion functions; 
  (d) Utility functions for sparse matrices such as sorting the indices or removing 
  zero-valued entries; 
  (e) Fast transposes that work by outputting in the opposite storage format;
  (f) Faster replacements for many 'Matrix' methods for all sparse types, such as
  slicing and elementwise multiplication.
  (g) Convenience functions for sparse objects, such as 'mapSparse' or a shorter 'show' method.
Constructs the normalized Laplacian matrix of a square matrix, returns the eigenvectors (singular vectors) and visualization of normalized Laplacian map.
Fits the penalized matrix-normal model to be used for linear discriminant analysis with matrix-valued predictors. For a description of the method, see Molstad and Rothman (2018) <doi:10.1080/10618600.2018.1476249>.
Implements finite mixtures of matrix-variate contaminated normal distributions via expectation conditional-maximization algorithm for model-based clustering, as described in Tomarchio et al.(2020) <arXiv:2005.03861>. One key advantage of this model is the ability to automatically detect potential outlying matrices by computing their a posteriori probability of being typical or atypical points. Finite mixtures of matrix-variate t and matrix-variate normal distributions are also implemented by using expectation-maximization algorithms.
Modelling with sparse and dense 'Matrix' matrices, using
  modular prediction and response module classes.
Computes densities, probabilities, and random deviates of the Matrix Normal (Pocuca et al. (2019) <doi:10.48550/arXiv.1910.02859>). Also includes simple but useful matrix functions. See the vignette for more information.
A simple and the early stage package for matrix profile based on the paper of Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh (2016) <DOI:10.1109/ICDM.2016.0179>. This package calculates all-pairs-similarity for a given window size for time series data.
This is the core functions needed by the 'tsmp' package.  The
    low level and carefully checked mathematical functions are here.
    These are implementations of the Matrix Profile concept that was
    created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.
Provides samplers for various matrix variate distributions: Wishart, inverse-Wishart, normal, t, inverted-t, Beta type I, Beta type II, Gamma, confluent hypergeometric. Allows to simulate the noncentral Wishart distribution without the integer restriction on the degrees of freedom.
High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds().  Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized.  There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian().
Tests for block-diagonal structure in symmetric matrices (e.g. correlation matrices) under the null hypothesis of exchangeable off-diagonal elements. As described in Segal et al. (2019), these tests can be useful for construct validation either by themselves or as a complement to confirmatory factor analysis. Monte Carlo methods are used to approximate the permutation p-value with Hubert's Gamma (Hubert, 1976) and a t-statistic. This package also implements the chi-squared statistic described by Steiger (1980). Please see Segal, et al. (2019) <doi:10.1007/s11336-018-9647-4> for more information.
Functions to perform fast statistical hypothesis tests on rows/columns of matrices.
  The main goals are: 1) speed via vectorization, 2) output that is detailed and easy to use,
  3) compatibility with tests implemented in R (like those available in the 'stats' package).
An implementation of matrix mathematics wherein operations are performed "by name."
Provides functions to collapse a tidy data frame into matrices in a data frame
    and expand a data frame of matrices into a tidy data frame.
Performs matrix skew-t parameter estimation, Gallaugher and McNicholas (2017)  <doi: 10.1002/sta4.143>.
Identifying maturation stages across young athletes is paramount for talent identification. Furthermore, the concept of biobanding, or grouping of athletes based on their biological development, instead of their chronological age, has been widely researched. The goal of this package is to help professionals working in the field of strength & conditioning and talent ID obtain common maturation metrics and as well as to quickly visualize this information via several plotting options. For the methods behind the computed maturation metrics implemented in this package refer to Khamis, H. J., & Roche, A. F. (1994) <https://pubmed.ncbi.nlm.nih.gov/7936860/>, Mirwald, R.L et al., (2002) <https://pubmed.ncbi.nlm.nih.gov/11932580/> and Cumming, Sean P. et al., (2017) <doi:10.1519/SSC.0000000000000281>.
Provides functions for the creation, evaluation and test of decision models based in
    Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities
    for a set of indexes, compute utilities and weights for the whole decision tree defining the
    decision model and simulate weights employing Dirichlet distributions under addition constraints 
    in weights.
'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'BEAST2' is commonly accompanied by 'BEAUti 2' (<https://www.beast2.org>),
    which, among others, allows one to install 'BEAST2' package. 
    This package allows to work with 'BEAST2' packages from 'R'.
Functions for dimension reduction, using MAVE (Minimum Average Variance Estimation), OPG (Outer Product of Gradient) and KSIR (sliced inverse regression of kernel version). Methods for selecting the best dimension are also included. Xia (2002) <doi:10.1111/1467-9868.03411>; Xia (2007) <doi:10.1214/009053607000000352>; Wang (2008) <doi:10.1198/016214508000000418>.
Functions for comparing survival curves using the max-combo test at a single timepoint or repeatedly at successive respective timepoints while controlling type I error (i.e., the group sequential setting), as published by Prior (2020) <doi:10.1177/0962280220931560>.  The max-combo test is a generalization of the weighted log-rank test, which itself is a generalization of the log-rank test, which is a commonly used statistical test for comparing survival curves, e.g., during or after a clinical trial as part of an effort to determine if a new drug or therapy is more effective at delaying undesirable outcomes than an established drug or therapy or a placebo.
Complex niche models show low performance in identifying
    the most important range-limiting environmental variables and in
    transferring habitat suitability to novel environmental conditions
    (Warren and Seifert, 2011 <DOI:10.1890/10-1171.1>; Warren et al.,
    2014 <DOI:10.1111/ddi.12160>). This package helps to identify the
    most important set of uncorrelated variables and to fine-tune Maxent's
    regularization multiplier. In combination, this allows to constrain
    complexity and increase performance of Maxent niche models (assessed
    by information criteria, such as AICc (Akaike, 1974
    <DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver
    operating characteristic (AUC) (Fielding and Bell, 1997
    <DOI:10.1017/S0376892997000088>). Users of this package should be
    familiar with Maxent niche modelling.
Constructs a space-filling design under the criterion of maximum-minimum distance. Both discrete and continuous searches are provided.
Functions for Maximum Likelihood (ML) estimation, non-linear
   optimization, and related tools.  It includes a unified way to call
   different optimizers, and classes and methods to handle the results from
   the Maximum Likelihood viewpoint.  It also includes a number of convenience
   tools for testing and developing your own models.
Provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species' distribution.
Computes the maximum matching for unweighted graph and maximum
    matching for (un)weighted bipartite graph efficiently.
An implementation of the Monte Carlo techniques described in details by Dufour (2006) <doi:10.1016/j.jeconom.2005.06.007> and Dufour and Khalaf (2007) <doi:10.1002/9780470996249.ch24>. The two main features available are the Monte Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized Monte Carlo, mmc(), for statistics with nuisance parameters.
Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. Model structure is the same as for the 'Maxent' Java package, version 3.4.0, with the same feature types and regularization options.  See the 'Maxent' website <http://biodiversityinformatics.amnh.org/open_source/maxent> for more details.
Functions to generate graphs that maximise the NODF (nestedness metric based
    on overlap and decreasing fill) metric for a given number of rows, columns
    and links. NODF was originally defined by Almeida-Neto et al. (2008)
    <doi:10.1111/j.0030-1299.2008.16644.x>. As nestedness in ecological networks
    depends on the size of the networks we require normalisation to make them
    comparable. We offer three highly optimised algorithms to find the
    optimising graphs so that users can choose an appropriate trade off between
    computation time and NODF value for the task at hand.
Generate maximum projection (MaxPro) designs for quantitative and/or qualitative factors. Details of the MaxPro criterion can be found in: (1) Joseph, Gul, and Ba. (2015) "Maximum Projection Designs for Computer Experiments", Biometrika, 102, 371-380, and (2) Joseph, Gul, and Ba. (2018) "Designing Computer Experiments with Multiple Types of Factors: The MaxPro Approach", Journal of Quality Technology, to appear.
It finds Orthogonal Data Projections with Maximal Skewness. The first data projection in the output is the most skewed among all linear data projections. The second data projection in the output is the most skewed among all data projections orthogonal to the first one, and so on.
Maximally selected rank statistics with
 several p-value approximations.
The maybe type represents the possibility of some value or nothing. 
    It is often used instead of throwing an error or returning `NULL`.
    The advantage of using a maybe type over `NULL` is that it is both composable 
    and requires the developer to explicitly acknowledge the potential absence 
    of a value, helping to avoid the existence of unexpected behaviour.
A suite of utility functions providing functionality commonly
    needed for production level projects such as logging, error handling,
    cache management and date-time parsing. Functions for date-time parsing and 
    formatting require that time zones be specified explicitly, avoiding a common 
    source of error when working with environmental time series.
Utility functions for discovering and managing metadata 
    associated with spatially unique "known locations". Applications include
    all fields of environmental monitoring (e.g. air and water quality) where 
    data are collected at stationary sites.
A suite of compiled functions calculating rolling mins, means, 
    maxes and other statistics. This package is designed to meet the needs of
    data processing systems for environmental time series.
A suite of convenience functions for generating US state and county
    thematic maps using datasets from the MazamaSpatialUtils package.
A suite of conversion functions to create internally standardized
    spatial polygons data frames. Utility functions use these data sets to
    return values such as country, state, time zone, watershed, etc. associated
    with a set of longitude/latitude pairs. (They also make cool maps.)
Utility functions for working with environmental time series data from known 
    locations. The compact data model is structured as a list with two dataframes. A 
    'meta' dataframe contains spatial and measuring device metadata associated with 
    deployments at known locations. A 'data' dataframe contains a 'datetime' column 
    followed by columns of measurements associated with each "device-deployment".
    Ephemerides calculations are based on code originally found in NOAA's
    "Solar Calculator" <https://gml.noaa.gov/grad/solcalc/>.
Supports the generation of parallelogram, equilateral
    triangle, regular hexagon, isosceles trapezoid, Koch snowflake,
    'hexaflake', Sierpinski triangle, Sierpinski carpet and Sierpinski
    trapezoid mazes via 'TurtleGraphics'. Mazes are generated by the recursive method:
    the domain is divided into sub-domains in which mazes are generated,
    then dividing lines with holes are drawn between them, see
    J. Buck, Recursive Division, <http://weblog.jamisbuck.org/2011/1/12/maze-generation-recursive-division-algorithm>.
A maze generator that creates the Elithorn Maze (HTML file) and the functions to calculate the associated maze parameters (i.e. Difficulty and Ability).
Methods for calculating and testing the significance of
  pairwise monotonic association from and based on the work of
  Pimentel (2009) <doi:10.4135/9781412985291.n2>. Computation of association of vectors from one
  or multiple sets can be performed in parallel thanks to the
  packages 'foreach' and 'doMC'.
Functionality for generating and plotting random mazes. The mazes are based on matrices, so can only consist of vertical and horizontal lines along a regular grid. But there is no need to use every possible space, so they can take on many different shapes.
Functions to interpolate irregularly and regularly spaced data using Multilevel B-spline Approximation (MBA). Functions call portions of the SINTEF Multilevel B-spline Library written by Øyvind Hjelle which implements methods developed by Lee, Wolberg and Shin (1997; <doi:10.1109/2945.620490>).
Exploratory and predictive methods for the analysis of several blocks of variables measured on the same individuals. The methods included are: Multiblock Principal Components Analysis (MB-PCA), Common Dimensions analysis (ComDim), Multiblock Partial Least Squares (MB-PLS) regression and Multiblock Weighted Covariate analysis (MB-WCov). E. Tchandao Mangamana, V. Cariou, E. Vigneau, R. Glèlè Kakaï, E.M. Qannari (2019) <doi:10.1016/j.chemolab.2019.103856>;  E. Tchandao Mangamana, R. Glèlè Kakaï, E.M. Qannari (2021) <doi:10.1016/j.chemolab.2021.104388>.
Distributions that are typically used for exposure rating in
             general insurance, in particular to price reinsurance contracts.
             The vignette shows code snippets to fit the distribution to
             empirical data. See, e.g., Bernegger (1997) <doi:10.2143/AST.27.1.563208>
             freely available on-line.
Calibrate and apply multivariate bias correction algorithms
    for climate model simulations of multiple climate variables. Three methods
    described by Cannon (2016) <doi:10.1175/JCLI-D-15-0679.1> and 
    Cannon (2018) <doi:10.1007/s00382-017-3580-6> are implemented —
    (i) MBC Pearson correlation (MBCp), (ii) MBC rank correlation (MBCr),
    and (iii) MBC N-dimensional PDF transform (MBCn) — as is the Rank
    Resampling for Distributions and Dependences (R2D2) method.
The companion package provides all original data sets and functions that are used in the book "Model-Based Clustering and Classification for Data Science" by Charles Bouveyron, Gilles Celeux, T. Brendan Murphy and Adrian E. Raftery (2019, ISBN:9781108644181).
Perform clusterwise multiblock analyses (clusterwise multiblock Partial Least Squares, clusterwise multiblock Redundancy Analysis or a regularized method between the two latter ones) associated with a F-fold cross-validation procedure to select the optimal number of clusters and dimensions.
Bending non-positive-definite (symmetric) matrices to positive-definite, using weighted and unweighted methods.
   Jorjani, H., et al. (2003) <doi:10.3168/jds.S0022-0302(03)73646-7>.
   Schaeffer, L. R. (2014) <http://animalbiosciences.uoguelph.ca/~lrs/ELARES/PDforce.pdf>.
Implements methods that are useful in designing research studies and analyzing data, with 
	particular emphasis on methods that are developed for or used within the behavioral, 
	educational, and social sciences (broadly defined). That being said, many of the methods 
	implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a 
	suite of functions for a variety of related topics, such as effect sizes, confidence intervals 
	for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size
	planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and 
	minimum-risk point estimation perspectives), mediation analysis, various properties of 
	distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally 
	an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but MBESS became
	more general and now contains methods applicable and used in a wide variety of fields and is an 
	orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has 
	greatly benefited from others, see <https://www3.nd.edu/~kkelley/site/MBESS.html> for a detailed 
	list of those that have contributed and other details.
Fast moment-based hierarchical model fitting. Implements
    methods from the papers
    "Fast Moment-Based Estimation for Hierarchical Models," by Perry (2017)
    and
    "Fitting a Deeply Nested Hierarchical Model to a Large Book Review
    Dataset Using a Moment-Based Estimator," by Zhang, Schmaus, and Perry
    (2018).
Provides spatially survey balanced designs using the quasi-random number method described Robinson et al. (2013) <doi:10.1111/biom.12059>. These designs can accommodate, without substantial detrimental effects on spatial balance, legacy sites (Foster et al., 2017 <doi:10.1111/2041-210X.12782>), and for both point samples and transect samples (Foster et al. 2020, <doi:10.1111/2041-210X.13321>).  Additional information about the package use itself is given in Foster (2021) <doi:10.1111/2041-210X.13535>.
Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of 'mbir' is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently.
Provides linear models based on Theil-Sen
        single median and Siegel repeated medians. They are very robust
        (29 or 50 percent breakdown point, respectively), and if no
        outliers are present, the estimators are very similar to OLS.
Lightweight utilities for nucleic acid melting curve analysis are 
    important in life sciences and diagnostics. This software can be used for 
    the analysis and presentation of melting curve data from microbead-based 
    assays (surface melting curve analysis) and reactions in solution (e.g., 
    quantitative PCR (qPCR), real-time isothermal Amplification). Further 
    information are described in detail in two publications in The R Journal [
    <https://journal.r-project.org/archive/2013-2/roediger-bohm-schimke.pdf>; 
    <https://journal.r-project.org/archive/2015-1/RJ-2015-1.pdf>].
Evaluate whether a microbiome sample is a mixture of two
    samples, by fitting a model for the number of read counts as a
    function of single nucleotide polymorphism (SNP) allele and the
    genotypes of two potential source samples.
    Lobo et al. (2019) <doi:10.1101/529040>.
Fits Bayesian dose-response model-based network meta-analysis (MBNMA) 
    that incorporate multiple doses within an agent by modelling different dose-response functions, as
    described by Mawdsley et al. (2016) <doi:10.1002/psp4.12091>. 
    By modelling dose-response relationships this can connect networks of evidence that might
    otherwise be disconnected, and can improve precision on treatment estimates. Several common 
    dose-response functions are provided; others may be added by the user. Various characteristics
    and assumptions can be flexibly added to the models, such as shared class effects. The consistency 
    of direct and indirect evidence in the network can be assessed using unrelated mean effects models 
    and/or by node-splitting at the treatment level.
Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple
  time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions,
  following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>. 
  The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple 
  treatment comparisons. Several general time-course functions are provided; others may be added 
  by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared 
  class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects 
  models and/or by node-splitting.
Functional gradient descent algorithm
  (boosting) for optimizing general risk functions utilizing
  component-wise (penalised) least squares estimates or regression
  trees as base-learners for fitting generalized linear, additive
  and interaction models to potentially high-dimensional data.
  Models and algorithms are described in <doi:10.1214/07-STS242>,
  a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>.
  The package allows user-specified loss functions and base-learners.
Importing and converting an mbox file into a tibble object.
Mass-balance-adjusted Regression algorithm for streamflow reconstruction at sub-annual resolution (e.g., seasonal or monthly). The algorithm implements a penalty term to minimize the differences between the total sub-annual flows and the annual flow. The method is described in Nguyen et al (2020) <DOI:10.1002/essoar.10504791.1>.
Functions for model-based response dimension reduction. Usual dimension reduction methods in multivariate regression focus on the reduction of predictors, not responses.  The response dimension reduction is theoretically founded in Yoo and Cook (2008) <doi:10.1016/j.csda.2008.07.029>. Later, three model-based response dimension reduction approaches are proposed in Yoo (2016) <doi:10.1080/02331888.2017.1410152> and Yoo (2019) <doi:10.1016/j.jkss.2019.02.001>. The method by Yoo and Cook (2008) is based on non-parametric ordinary least squares, but the model-based approaches are done through maximum likelihood estimation. For two model-based response dimension reduction methods called principal fitted response reduction and unstructured principal fitted response reduction, chi-squared tests are provided for determining the dimension of the response subspace.
Summarize multiple biomarker responses of aquatic organisms to
    contaminants using Cliff’s delta, as described in Pham & Sokolova (2022)
    <doi:10.1002/ieam.4676>.
Fit generalized linear models with binomial responses using  a median modified score approach (Kenne Pagui et al., 2016, <https://arxiv.org/abs/1604.04768>) to median bias reduction. This method respects equivariance under reparameterizations for each parameter component and also solves the infinite estimates problem (data separation).
An implementation of a Bayesian sparse group model using spike and slab priors in a regression context. It is designed for regression with a multivariate response variable, but also provides an implementation for univariate response.
Gibbs sampler for fitting multivariate Bayesian linear regression with shrinkage priors (MBSP), using the three parameter beta normal family. The method is described in Bai and Ghosh (2018) <doi:10.1016/j.jmva.2018.04.010>.
Tools for data analysis with multivariate Bayesian structural time series (MBSTS) models.  Specifically, the package provides facilities for implementing general structural time series models, flexibly adding on different time series components (trend, season, cycle, and regression), simulating them, fitting them to multivariate correlated time series data, conducting feature selection on the regression component.
Bayesian variable selection methods for data with multivariate responses and multiple covariates. The package contains implementations of multivariate Bayesian variable selection methods for continuous data (Lee et al., Biometrics, 2017 <doi: 10.1111/biom.12557>) and zero-inflated count data (Lee et al., Biostatistics, 2020 <doi:10.1093/biostatistics/kxy067>).
Implements a Monte Carlo Based Heterogeneity Test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (OR) in Meta-Analysis Studies. Depending on the presence of moderators, this Monte Carlo Based Test can be implemented in the random or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihood, so installation of R package 'metafor' is required. This approach refers to the studies of Hedges (1981) <doi:10.3102/10769986006002107>, Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) <doi:10.1002/14651858.CD000146.pub2>, Viechtbauer (2010) <doi:10.18637/jss.v036.i03>, and Zuckerman (1994, ISBN:978-0521432009).
A complete framework to build and study Two-Dimensional Monte-Carlo simulations, aka Second-Order Monte-Carlo simulations. Also includes various distributions (pert, triangular, Bernoulli, empirical discrete and continuous).
Provides functions for carrying out nonparametric hypothesis tests of the MCAR hypothesis based on the theory of Frechet classes and compatibility. Also gives functions for computing halfspace representations of the marginal polytope and related geometric objects.
Provides two variants of multiple correspondence analysis (ca):
                     multiple ca and ordered multiple ca via orthogonal polynomials of Emerson.
When choosing proper variable selection methods, it is important to consider the uncertainty of a certain method. The model confidence bound for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level. A good variable selection method is the one of which the model confidence bound under a certain confidence level has the shortest width. When visualizing the variability of model selection and comparing different model selection procedures, model uncertainty curve is a good graphical tool. A good variable selection method is the one of whose model uncertainty curve will tend to arch towards the upper left corner. This function aims to obtain the model confidence bound and draw the model uncertainty curve of certain single model selection method under a coverage rate equal or little higher than user-given confidential level. About what model confidence bound is and how it work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. <DOI:10.1111/biom.13024>. Besides, 'flare' is needed only you apply the SQRT or LAD method ('mcb' totally has 8 methods). Although 'flare' has been archived by CRAN, you can still get it in <https://CRAN.R-project.org/package=flare> and the latest version is useful for 'mcb'.
Monte Carlo simulation is a stochastic method computing trajectories of photons in media. Surface backscattering is performing calculations in semi-infinite media and summarizing photon flux leaving the surface. This simulation is modeling the optical measurement of diffuse reflectance using an incident light beam. The semi-infinite media is considered to have flat surface. Media, typically biological tissue, is described by four optical parameters: absorption coefficient, scattering coefficient, anisotropy factor, refractive index. The media is assumed to be homogeneous.
    Computational parameters of the simulation include: number of photons, radius of incident light beam, lowest photon energy threshold, intensity profile (halo) radius, spatial resolution of intensity profile.
    You can find more information and validation in the Open Access paper.
    Laszlo Baranyai (2020) <doi:10.1016/j.mex.2020.100958>.
'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'mcbette' allows to do a Bayesian model comparison over some
    site and clock models, 
    using 'babette' (<https://github.com/ropensci/babette/>).
Monte Carol based tests for the Behrens Fisher Problem enhance the statistical power 
           and performs better than Welch's t-approximation, see Ullah et al. (2019).
Computes the prime implicants or a minimal disjunctive normal form for a
  logic expression presented by a truth table or a logic tree. Has been particularly 
  developed for logic expressions resulting from a logic regression analysis, i.e.
  logic expressions typically consisting of up to 16 literals, where the prime implicants 
  are typically composed of a maximum of 4 or 5 literals.
Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and
    'Multi-Accuracy Boosting' (2019) <arXiv:1805.12317> for the multi-calibration of a machine learning model's prediction.
    'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups.
    Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are.
    This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.
It contains six common multi-category classification accuracy evaluation measures.
 All of these measures could be found in Li and Ming (2019) <doi:10.1002/sim.8103>. Specifically,
 Hypervolume Under Manifold (HUM), described in
 Li and Fine (2008) <doi:10.1093/biostatistics/kxm050>.
 Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in
 Li, Jiang and Fine (2013) <doi:10.1093/biostatistics/kxs047>.
 Polytomous Discrimination Index (PDI), described in
 Van Calster et al. (2012) <doi:10.1007/s10654-012-9733-3>.
 Li et al. (2018) <doi:10.1177/0962280217692830>.
 We described all these above measures and our mcca package in
 Li, Gao and D'Agostino (2019) <doi:10.1002/sim.8103>.
Performing multiple-class cluster correspondence analysis(MCCCA). The main functions are create.MCCCAdata() to create a list to be applied to MCCCA, MCCCA() to apply MCCCA, and plot.mccca() for visualizing MCCCA result. Methods used in the 
  package refers to Mariko Takagishi and Michel van de Velden (2022)<doi:10.1080/10618600.2022.2035737>.
The MCC-F1 analysis is a method to evaluate the performance of binary classifications. 
    The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth.
    The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification.
Implements methods for processing a sample of (hard)
        clusterings, e.g. the MCMC output of a Bayesian clustering
        model. Among them are methods that find a single best
        clustering to represent the sample, which are based on the
        posterior similarity matrix or a relabelling algorithm.
Applying the methodology from Manuel et al. to estimate parameters using a matched 
    case control data with a mismeasured exposure variable that is accompanied by 
    instrumental variables (Submitted).
The Matthews correlation coefficient (MCC) score is calculated (Matthews BW  (1975) <DOI:10.1016/0005-2795(75)90109-9>).
Support for the analyst in a Multicriteria Decision Aiding (MCDA) process with algorithms, 
    preference elicitation and data visualisation functions. Sébastien Bigaret, Richard Hodgett, Patrick Meyer, 
    Tatyana Mironova, Alexandru Olteanu (2017) Supporting the multi-criteria decision aiding process : 
    R and the MCDA package, Euro Journal On Decision Processes, Volume 5, Issue 1 - 4, 
    pages 169 - 194 <doi:10.1007/s40070-017-0064-1>.
Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM.
    For a description of the algorithm see Brian S. Caffo, Wolfgang Jank and Galin L. Jones (2005)
	<DOI:10.1111/j.1467-9868.2005.00499.x>.
Fits the Multivariate Cluster Elastic Net (MCEN) presented in Price & Sherwood (2018) <arXiv:1707.03530>. The MCEN model simultaneously estimates regression coefficients and a clustering of the responses for a multivariate response model. Currently accommodates the Gaussian and binomial likelihood.
Machine coded genetic algorithm (MCGA) is a fast tool for
    real-valued optimization problems. It uses the byte
    representation of variables rather than real-values. It
    performs the classical crossover operations (uniform) on these
    byte representations. Mutation operator is also similar to
    classical mutation operator, which is to say, it changes a
    randomly selected byte value of a chromosome by +1 or -1 with
    probability 1/2. In MCGAs there is no need for
    encoding-decoding process and the classical operators are
    directly applicable on real-values. It is fast and can handle a
    wide range of a search space with high precision. Using a
    256-unary alphabet is the main disadvantage of this algorithm
    but a moderate size population is convenient for many problems.
    Package also includes multi_mcga function for multi objective
    optimization problems. This function sorts the chromosomes
    using their ranks calculated from the non-dominated sorting
    algorithm.
Fitting multivariate covariance generalized linear
     models (McGLMs) to data.  McGLM is a general framework for non-normal
     multivariate data analysis, designed to handle multivariate response
     variables, along with a wide range of temporal and spatial correlation
     structures defined in terms of a covariance link function combined
     with a matrix linear predictor involving known matrices.
     The models take non-normality into account in the conventional way
     by means of a variance function, and the mean structure is modelled
     by means of a link function and a linear predictor.
     The models are fitted using an efficient Newton scoring algorithm
     based on quasi-likelihood and Pearson estimating functions, using
     only second-moment assumptions. This provides a unified approach to
     a wide variety of different types of response variables and covariance
     structures, including multivariate extensions of repeated measures,
     time series, longitudinal, spatial and spatio-temporal structures.
     The package offers a user-friendly interface for fitting McGLMs
     similar to the glm() R function. 
     See Bonat (2018) <doi:10.18637/jss.v084.i04>, for more information 
     and examples.
Performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 <DOI:10.1198/106186007X257025>) and a boundary proposed by Besag and Clifford, 1991 <DOI:10.1093/biomet/78.2.301>. Gives valid p-values and confidence intervals on p-values.
Market area models are used to analyze and predict store choices and market areas concerning retail and service locations. This package implements two market area models (Huff Model, Multiplicative Competitive Interaction Model) into R, while the emphases lie on 1.) fitting these models based on empirical data via OLS regression and nonlinear techniques and 2.) data preparation and processing (esp. interaction matrices and data preparation for the MCI Model).
Apply the marginal classification method to achieve the purpose of providing 
    the point and interval estimates for the minimal clinically important difference 
    based on the classical anchor-based method. For more details of the methodology, please 
    see Zehua Zhou, Leslie J. Bisson and Jiwei Zhao (2021) <arXiv:2108.11589>.
Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.
The likelihood of direct CAR models and Binomial and Poisson GLM with latent CAR variables are approximated by the Monte Carlo likelihood. The Maximum Monte Carlo likelihood estimator is found either by an iterative procedure of directly maximising the Monte Carlo approximation or by a response surface design method.Reference for the method can be found in the DPhil thesis in Z. Sha (2016). For application a good reference is R.Bivand et.al (2017) <doi:10.1016/j.spasta.2017.01.002>.
Read, inspect and process corpus files for quantitative corpus linguistics.
  Obtain concordances via regular expressions, tokenize texts,
  and compute frequencies and association measures. Useful for collocation analysis,
  keywords analysis and variationist studies (comparison of linguistic variants
  and of linguistic varieties).
Provides estimators for multinomial logit models in their
    conditional logit and baseline logit variants, with or without random effects,
    with or without overdispersion. 
    Random effects models are estimated using the PQL technique (based on a Laplace approximation)
    or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated
    with caution if the group sizes are small.
Gaussian finite mixture models fitted via EM algorithm for
  model-based clustering, classification, and density estimation, 
  including Bayesian regularization, dimension reduction for 
  visualisation, and resampling-based inference.
Extend the functionality of the 'mclust' package for 
  Gaussian finite mixture modelling by including: 
  density estimation for data with bounded support 
  (Scrucca, 2019 <doi:10.1002/bimj.201800174>);
  modal clustering using MEM algorithm for Gaussian mixtures 
  (Scrucca, 2021 <doi:10.1002/sam.11527>).
Given a set of data points, a clustering is defined as a disjoint partition
    where each pair of sets in a partition has no overlapping elements. 
    This package provides 25 methods that play a role somewhat similar to 
    distance or metric that measures similarity of two clusterings - or partitions.
    For a more detailed description, see Meila, M. (2005) <doi:10.1145/1102351.1102424>.
Estimate and test inter-generational social mobility effect on an outcome with cross-sectional or longitudinal data.
Simulates continuous distributions of random vectors using
    Markov chain Monte Carlo (MCMC).  Users specify the distribution by an
    R function that evaluates the log unnormalized density.  Algorithms
    are random walk Metropolis algorithm (function metrop), simulated
    tempering (function temper), and morphometric random walk Metropolis
    (Johnson and Geyer, 2012, <doi:10.1214/12-AOS1048>,
    function morph.metrop),
    which achieves geometric ergodicity by change of variable.
Poisson-lognormal generalized linear mixed model analysis of multivariate counts data using MCMC, aiming to infer the changes in relative proportions of individual variables. The package was originally designed for sequence-based analysis of microbial communities ("metabarcoding", variables = operational taxonomic units, OTUs), but can be used for other types of multivariate counts, such as in ecological applications (variables = species). The results are summarized and plotted using 'ggplot2' functions. Includes functions to remove sample and variable outliers and reformat counts into normalized log-transformed values for correlation and principal component/coordinate analysis. Walkthrough and examples: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/walkthroughExample_mcmcOTU_R.txt.
Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.
Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions. It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots.
Generates derived parameter(s) from Monte Carlo Markov Chain
    (MCMC) samples using R code. This allows Bayesian models to be fitted
    without the inclusion of derived parameters which add unnecessary
    clutter and slow model fitting. For more information on MCMC samples
    see Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
Provides ensemble samplers for
    affine-invariant Monte Carlo Markov Chain, which allow a faster
    convergence for badly scaled estimation problems. Two samplers are
    proposed: the 'differential.evolution' sampler from ter Braak and
    Vrugt (2008) <doi:10.1007/s11222-008-9104-9> and the 'stretch' sampler
    from Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65>.
Fits Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques (Hadfield 2010 J. Stat. Soft.).
Implements a class ('mcmcOutput') for efficiently storing and handling Markov chain Monte Carlo (MCMC) output, intended as an aid for those writing customized MCMC samplers. A range of constructor methods are provided covering common output formats. Functions are provided to generate summary and diagnostic statistics and to display histograms or density plots of posterior distributions, for the entire output, or subsets of draws, nodes, or parameters.
Contains functions to perform Bayesian
        inference using posterior simulation for a number of
        statistical models. Most simulation is done in compiled C++
        written in the Scythe Statistical Library Version 1.0.3. All
        models return 'coda' mcmc objects that can then be summarized
        using the 'coda' package. Some useful
        utility functions such as density functions,
	pseudo-random number generators for statistical
        distributions, a general purpose Metropolis sampling algorithm,
        and tools for visualization are provided.
Functions for convenient plotting and viewing of MCMC output.
Estimates the precision of transdimensional Markov chain Monte Carlo 
    (MCMC) output, which is often used for Bayesian analysis of models with different 
    dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible 
    jump MCMC) relies on sampling a discrete model-indicator variable to estimate 
    the posterior model probabilities. If only few switches occur between the models, 
    precision may be low and assessment based on the assumption of independent 
    samples misleading. Based on the observed transition matrix of the indicator 
    variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, 
    Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws 
    posterior samples of the stationary distribution to (a) assess the uncertainty 
    in the estimated posterior model probabilities and (b) estimate the effective 
    sample size of the MCMC output.
Functions and classes to store, manipulate and summarise
    Monte Carlo Markov Chain (MCMC) samples. For more information see
    Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
Fit multi-level models with possibly correlated
    random effects using Markov Chain Monte Carlo simulation.
    Such models allow smoothing over space and time and are useful in,
    for example, small area estimation.
Provides tools for computing Monte Carlo standard
        errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE
        computation for expectation and quantile estimators is
        supported as well as multivariate estimations. The package also provides 
	functions for computing effective sample size and for plotting
	Monte Carlo estimates versus sample size.
Provides functions to prepare time priors for 'MCMCtree' analyses in the 'PAML' software from Yang (2007)<doi:10.1093/molbev/msm088> and plot time-scaled phylogenies from any Bayesian divergence time analysis. Most time-calibrated node prior distributions require user-specified parameters. The package provides functions to refine these parameters, so that the resulting prior distributions accurately reflect confidence in known, usually fossil, time information. These functions also enable users to visualise distributions and write 'MCMCtree' ready input files. Additionally, the package supplies flexible functions to visualise age uncertainty on a plotted tree with using node bars, using branch widths proportional to the age uncertainty, or by plotting the full posterior distributions on nodes. Time-scaled phylogenetic plots can be visualised with absolute and geological timescales . All plotting functions are applicable with output from any Bayesian software, not just 'MCMCtree'.
Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with 'Stan', 'NIMBLE', 'JAGS', and other software.
Algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems. Additionally, a modular toolbox for the generation of multi-objective benchmark graph problems is included.
A collection of function to solve multiple criteria optimization problems 
  using genetic algorithms (NSGA-II). Also included is a collection of test functions.
The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.
Provides a class for multi-companion matrices with methods for
    arithmetic and factorization.  A method for generation of
    multi-companion matrices with prespecified spectral properties is
    provided, as well as some utilities for periodically correlated and
    multivariate time series models. See Boshnakov (2002)
    <doi:10.1016/S0024-3795(01)00475-X> and Boshnakov & Iqelan (2009)
    <doi:10.1111/j.1467-9892.2009.00617.x>.
Implementation of marginalized models for zero-inflated count data. This package provides a tool to implement an estimation algorithm for the marginalized count models, which
  directly makes inference on the effect of each covariate on the marginal mean of the outcome. 
  The method involves the marginalized zero-inflated Poisson model described 
  in Long et al. (2014) <doi:10.1002/sim.6293>.
Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.
Multiple contrast tests and simultaneous confidence
 intervals based on normal approximation. With implementations for
 binomial proportions in a 2xk setting (risk difference and odds ratio),
 poly-3-adjusted tumour rates, biodiversity indices (multinomial data) 
 and expected values under lognormal assumption. Approximative power 
 calculation for multiple contrast tests of binomial and Gaussian data.
Provides a function that wraps 
    mcparallel() and mccollect() from 'parallel' with temporary variables and a 
    task handler.  Wrapped in this way the results of an mcparallel() call 
    can be returned to the R session when the fork is complete 
    without explicitly issuing a specific mccollect() to retrieve the value.
    Outside of top-level tasks, multiple mcparallel() jobs can be retrieved with 
    a single call to mcparallelDoCheck().
Implements a methodology for the design and analysis of dose-response studies that
             combines aspects of multiple comparison procedures  and modeling approaches
	     (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748, <doi: 10.1111/j.1541-0420.2005.00344.x>).
             The package provides tools for the analysis of dose finding trials as well as a variety
             of tools necessary to plan a trial to be conducted with the MCP-Mod methodology.
             Please note: The 'MCPMod' package will not be further developed, all future development of 
             the MCP-Mod methodology will be done in the 'DoseFinding' R-package.
Analyzes non-normal data via the Multiple Comparison Procedures and Modeling approach (MCP-Mod). Many functions rely on the 'DoseFinding' package. This package makes it so the user does not need to provide or calculate the mu vector and S matrix. Instead, the user typically supplies the data in its raw form, and this package will calculate the needed objects and passes them into the 'DoseFinding' functions. If the user wishes to primarily use the functions provided in the 'DoseFinding' package, a singular function (prepareGen()) will provide mu and S. The package currently handles power analysis and the MCP-Mod procedure for negative binomial, Poisson, and binomial data. The MCP-Mod procedure can also be applied to survival data, but power analysis is not available.
	Bretz, F., Pinheiro, J. C., and Branson, M. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>.
	Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997) <doi:10.2307/2533961>.
	Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) <doi:10.1002/sim.6052>.
An efficient implementation of the MCPMod (Multiple Comparisons and Modeling) method to support a simulation-based design and analysis of dose-finding trials with normally distributed, binary and count endpoints (Bretz et al. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>).
Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided.
Performs the execution of the main procedures of multiple 
    comparisons in the literature, Scott-Knott (1974) <http://www.jstor.org/stable/2529204>, 
    Batista (2016) <http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations 
    and export to different extensions of its results. An additional 
    part of the package is the presence of the performance evaluation 
    of the tests (Type I error per experiment and the power). This will 
    assist the user in making the decision for the chosen test.
Regression methods to quantify the relation between two measurement
    methods are provided by this package. In particular it addresses regression
    problems with errors in both variables and without repeated measurements. It
    implements the CLSI recommendations (see J. A. Budd et al. 
    (2018, <https://clsi.org/standards/products/method-evaluation/documents/ep09/>) 
    for analytical method comparison and bias estimation using patient samples.
    Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators 
    are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and 
    J. Raymaekers and F. Dufey (2022, <arXiv:2202:08060>).
    A comprehensive overview over the implemented methods and references can be found 
    in the manual pages "mcr-package" and "mcreg".
Multi-core replication function to make it easier to do fast 
  Monte Carlo simulation. Based on the mcreplicate() function from the 
  'rethinking' package. The 'rethinking' package requires installing 'rstan', 
  which is onerous to install, while also not adding capabilities to this 
  function.
Perform the model confidence set procedure of Hansen et al (2011) <doi:10.3982/ECTA5771>.
Identifies the optimal number of clusters by calculating the similarity between
             two clustering methods at the same number of clusters using the corrected indices of Rand and
             Jaccard as described in Albatineh and Niewiadomska-Bugaj (2011). The number of clusters at
             which the index attain its maximum more frequently is a candidate for being the optimal
             number of clusters.
Provides functionality to produce graphs of sampling distributions of test 	statistics from a variety of common statistical tests. With only a few keystrokes, 	the user can conduct a hypothesis test and visualize the test statistic and 		corresponding p-value through the shading of its sampling distribution. Initially       	created for statistics at Middlebury College.
Package computes popular and widely used multicollinearity diagnostic measures <doi:10.17576/jsm-2019-4809-26> and <doi:10.32614/RJ-2016-062> . Package also indicates which regressors may be the reason of collinearity among regressors.
Transition matrices (probabilities or counts) estimation for discrete Markov Chains of order n (1 <= n <= 5).
A complete toolkit to process the Munich ChronoType 
    Questionnaire (MCTQ) for its three versions (standard, micro, and shift). 
    MCTQ is a quantitative and validated tool to assess chronotypes using 
    peoples' sleep behavior, originally presented by Till Roenneberg, Anna 
    Wirz-Justice, and Martha Merrow (2003, <doi:10.1177/0748730402239679>).
Unit testing for Monte Carlo methods, particularly Markov Chain Monte Carlo (MCMC) methods, are implemented as extensions of the 'testthat' package. The MCMC methods check whether the MCMC chain has the correct invariant distribution. They do not check other properties of successful samplers such as whether the chain can reach all points, i.e. whether is recurrent. The tests require the ability to sample from the prior and to run steps of the MCMC chain. The methodology is described in Gandy and Scott (2020) <arXiv:2001.06465>.
Visualize the relationship between linear regression variables and causes of multi-collinearity. 
    Implements the method in Lin et. al. (2020) <doi:10.1080/10618600.2020.1779729>.
In the context of multistate models, which are popular in sociology,
    demography, and epidemiology, Markov chain with rewards calculations can
    help to refine transition timings and so obtain more accurate estimates. The
    package code accommodates up to nine transient states and irregular age (time)
    intervals. Traditional demographic life tables result as a special case.
    Formulas and methods involved are explained in detail in the accompanying
    article: Schneider / Myrskyla / van Raalte (2021): Flexible Transition Timing
    in Discrete-Time Multistate Life Tables Using Markov Chains with Rewards,
    MPIDR Working Paper WP-2021-002.
Selects bandwidth for the kernel density estimator with minimum distance method as proposed by Devroye and Lugosi (1996). The minimum distance method directly selects the optimal kernel density estimator from countably infinite kernel density estimators and indirectly selects the optimal bandwidth. This package selects the optimal bandwidth from finite kernel density estimators.
Produces clean and neat Markdown log file
    and also provide an argument to include the function call inside the Markdown log.
Mixture and flexible discriminant analysis, multivariate
        adaptive regression splines (MARS), BRUTO, and vector-response smoothing splines.
	Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York.
Learning a mixed directed acyclic graph based on both continuous and categorical data.
An implementation of functions for medical data analysis which perform basic data pre processing,
             univariate and bivariate analysis.Kirkwood et al. (2003) <doi:10.1002/sim.1961>.
Projection based methods for preprocessing,
    exploring and analysis of multivariate data used in chemometrics.
    S. Kucheryavskiy (2020) <doi: 10.1016/j.chemolab.2020.103937>.
Provides 3D plots of the Moran-Downton bivariate exponential distribution (MDBED), 
  generates bivariate random values, and also provides values of the joint and conditional PDFs and CDFs. 
  Nagao M, Kadoya M (1971) <http://hdl.handle.net/2433/124795>.
  Balakrishna N, Lai CD (2009) <doi:10.1007/b101765>.
Use the open source 'MDB Tools' utilities
    <https://github.com/mdbtools/mdbtools/>. Primarily used for converting
    proprietary Microsoft Access files to simple text files and then
    reading those as data frames.
Correct identification and handling of missing data is one of the most important steps in any analysis. To aid this process, 'mde' provides a very easy to use yet robust framework to quickly get an idea of where the missing data
             lies and therefore find the most appropriate action to take.
             Graham WJ (2009) <doi:10.1146/annurev.psych.58.110405.085530>.
Provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.
A comprehensive collection of linkage methods for agglomerative
  hierarchical clustering on a matrix of proximity data (distances or
  similarities), returning a multifurcated dendrogram or multidendrogram.
  Multidendrograms can group more than two clusters when ties in proximity data
  occur, and therefore they do not depend on the order of the input data.
  Descriptive measures to analyze the resulting dendrogram are additionally
  provided.
Functions for MultiDimensional Feature Selection (MDFS):
 calculating multidimensional information gains, scoring variables,
 finding important variables, plotting selection results.
 This package includes an optional CUDA implementation that speeds up
 information gain calculation using NVIDIA GPGPUs.
 R. Piliszek et al. (2019) <doi:10.32614/RJ-2019-019>.
'MTrackJ' is an 'ImageJ' plugin for motion tracking and analysis (see 
    <https://imagescience.org/meijering/software/mtrackj/>). This package reads 
    and writes 'MTrackJ Data Files' ('.mdf', see 
    <https://imagescience.org/meijering/software/mtrackj/format/>). It supports
    2D data and read/writes cluster, point, and channel information. If desired, 
    generates track identifiers that are unique over the clusters.
    See the project page for more information and examples.
Provides functions to impute missing values using Gaussian 
    copulas for mixed data types as described by Christoffersen et al. 
    (2021) <arXiv:2102.02642>. The method is related to Hoff (2007) 
    <doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <arXiv:1910.12845> 
    but differs by making a direct approximation of the log marginal likelihood 
    using an extended version of the Fortran code created by Genz and Bretz 
    (2002) <doi:10.1198/106186002394> in addition to also support multinomial 
    variables.
Allows various models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance can be further modeled as random-effect models.
Normalize data to minimize the difference between sample plates 
    (batch effects). For given data in a matrix and grouping variable (or
	plate), the function 'normn_MA' normalizes the data on MA coordinates. 
	More details are in the citation. The primary method is 'Multi-MA'. Other 
	fitting functions on MA coordinates can also be employed e.g. loess.
Implemented are the one-sided and two-sided 
  multiple-direction logrank test for two-sample right 
  censored data. In addition to the statistics p-values are calculated: 
  1. For the one-sided testing problem one p-value based on a
   wild bootstrap approach is determined. 2. In the two-sided case
   one p-value based on a chi-squared approximation and 
   a second p-values based on a permutation approach are calculated.
 Ditzhaus, M. and Friedrich, S. (2018) <arXiv:1807.05504>.
 Ditzhaus, M. and Pauly, M. (2018) <arXiv:1808.05627>.
Runs a Shiny web application that merges raw 'qPCR' fluorescence data with related 
    metadata to visualize species presence/absence detection patterns and assess data quality. 
    The application calculates threshold values from raw fluorescence data using a method based 
    on the second derivative method, Luu-The et al (2005) <doi:10.2144/05382RR05>,  and utilizes 
    the ‘chipPCR’ package by Rödiger, Burdukiewicz, & Schierack (2015) <doi:10.1093/bioinformatics/btv205> 
    to calculate Cq values. The application has the ability to connect to a custom developed MySQL 
    database to populate the applications interface. The application allows users to interact with 
    visualizations such as a dynamic map, amplification curves and standard curves, that allow for 
    zooming and/or filtering. It also enables the generation of customized exportable reports based
    on filtered mapping data.
Contains model-based treatment of missing data for regression 
    models with missing values in covariates or the dependent 
    variable using maximum likelihood or Bayesian estimation 
    (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>;
    Luedtke, Robitzsch, & West, 2020a, 2020b;
    <doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>).
    The regression model can be nonlinear (e.g., interaction 
    effects, quadratic effects or B-spline functions). 
    Multilevel models with missing data in predictors are
    available for Bayesian estimation. Substantive-model compatible 
    multiple imputation can be also conducted.
Allows users to conduct multivariate distance matrix regression using analytic p-values and compute measures of effect size. For details on the method, see McArtor, Lubke, & Bergeman (2017) <doi:10.1007/s11336-016-9527-8>.
Statistical inference for quadratic functional of the moderate-dimensional linear model in Guo and Cheng (2021) <DOI:10.1080/01621459.2021.1893177>.
Provides graph-constrained regression methods in which
    regularization parameters are selected automatically via estimation of
    equivalent Linear Mixed Model formulation. 'riPEER' (ridgified Partially
    Empirical Eigenvectors for Regression) method employs a penalty term being
    a linear combination of graph-originated and ridge-originated penalty terms,
    whose two regularization parameters are ML estimators from corresponding
    Linear Mixed Model solution; a graph-originated penalty term allows imposing
    similarity between coefficients based on graph information given whereas
    additional ridge-originated penalty term facilitates parameters estimation:
    it reduces computational issues arising from singularity in a graph-originated
    penalty matrix and yields plausible results in situations when graph information
    is not informative. 'riPEERc' (ridgified Partially Empirical Eigenvectors
    for Regression with constant) method utilizes addition of a diagonal matrix
    multiplied by a predefined (small) scalar to handle the non-invertibility of
    a graph Laplacian matrix. 'vrPEER' (variable reducted PEER) method performs
    variable-reduction procedure to handle the non-invertibility of a graph
    Laplacian matrix.
Provides automatization for plot generation succeeding common molecular dynamics analyses.
             This includes straightforward plots, such as RMSD (Root-Mean-Square-Deviation) and
             RMSF (Root-Mean-Square-Fluctuation) but also more sophisticated ones such as
             dihedral angle maps, hydrogen bonds, cluster bar plots and
             DSSP (Definition of Secondary Structure of Proteins) analysis. Currently able to load
             GROMOS, GROMACS and AMBER formats, respectively.
The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.
A set of core functions for handling medical device event data in
    the context of post-market surveillance, pharmacovigilance, signal detection
    and trending, and regulatory reporting. Primary inputs are data on events by
    device and data on exposures by device. Outputs include: standardized
    device-event and exposure datasets, defined analyses, and time series.
A set of functions to obtain modified score test for generalized linear models.
Tools associated with General
    Recognition Theory (Townsend & Ashby, 1986), including Gaussian model fitting of 2x2 and more
    general designs, associated plotting and model comparison tools,
    and tests of marginal response invariance and report independence.
A graphical user interface (GUI) for performing Multidimensional Scaling applications and interactively analysing the results all within the GUI environment. The MDS-GUI provides means of performing Classical Scaling, Least Squares Scaling, Metric SMACOF, Non-Metric SMACOF, Kruskal's Analysis and Sammon Mapping with animated optimisation.
Estimate genetic linkage maps for markers on a single chromosome (or in a single linkage group) from pairwise recombination fractions or intermarker distances using weighted metric multidimensional scaling. The methods are suitable for autotetraploid as well as diploid populations. Options for assessing the fit to a known map are also provided. Methods are discussed in detail in Preedy and Hackett (2016) <doi:10.1007/s00122-016-2761-8>.
Selecting the optimal multidimensional scaling (MDS) procedure for metric data via metric MDS (ratio, interval, mspline) and nonmetric MDS (ordinal). Selecting the optimal multidimensional scaling (MDS) procedure for interval-valued data via metric MDS (ratio, interval, mspline).Selecting the optimal multidimensional scaling procedure for interval-valued data by varying all combinations of normalization and optimization methods.Selecting the optimal MDS procedure for statistical data referring to the evaluation of tourist attractiveness of Lower Silesian counties.
 (Borg, I., Groenen, P.J.F., Mair, P. (2013) <doi:10.1007/978-3-642-31848-1>, 
 Walesiak, M. (2016) <doi:10.15611/ekt.2016.2.01>, 
 Walesiak, M. (2017) <doi:10.15611/ekt.2017.3.01>).
An interactive document on  the topic of multidimensional scaling and principal component analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/MDS_PCAShiny/>.
Modeling microstructures of
	human tooth dentin and
	horizontal serial-sectioning of the dentin.
	Corresponding age range of dentin serial sections,
	that is used in stable isotope analyses,
	can be calculated by using this package.
A collection of 'ggplot2' themes that render text as markdown/HTML. This enables
    the creation of complex formatted plot labels, e.g. titles with individual words highlighted
    in different colors.
Dimension-reduction methods aim at defining a score that maximizes signal diversity. Three approaches, tree weight, maximum entropy weights, and maximum variance weights are provided. These methods are described in He and Fong (2019) <DOI:10.1002/sim.8212>.
Sentiment analysis is a popular technique in text mining that
    attempts to determine the emotional state of some text. We provide a new
    implementation of a common method for computing sentiment, whereby words are
    scored as positive or negative according to a dictionary lookup. Then the
    sum of those scores is returned for the document. We use the 'Hu' and 'Liu'
    sentiment dictionary ('Hu' and 'Liu', 2004) <doi:10.1145/1014052.1014073>
    for determining sentiment. The scoring function is 'vectorized' by document,
    and scores for multiple documents are computed in parallel via 'OpenMP'.
Performs mean shift classification using linear and 
  k-d tree based nearest neighbor implementations for the Gaussian,
  Epanechnikov, and biweight product kernels.
Quickly make tables of descriptive statistics (i.e., counts, 
    means, confidence intervals) for continuous variables. This 
    package is designed to work in a Tidyverse pipeline, and consideration
    has been given to get results from R to 'Microsoft Word' ® with minimal pain.
Send server-side tracking data from R.
  The Measurement Protocol version 2 
  <https://developers.google.com/analytics/devguides/collection/protocol/ga4>
  allows sending HTTP tracking events from R code.
Collection of tools to make working with physical measurements
		easier. Convert between metric and imperial units, or calculate a dimension's
		unknown value from other dimensions' measurements.
Provides the biggest amount of statistical measures in the whole R world. Includes measures of regression, (multiclass) classification and multilabel classification. The measures come mainly from the 'mlr' package and were programed by several 'mlr' developers.
Identification of ring borders on scanned image sections from dendrochronological samples. Processing of image reflectances to produce gray matrices and time series of smoothed gray values. Luminance data is plotted on segmented images for users to perform both: visual identification of ring borders or control of automatic detection. Routines to visually include/exclude ring borders on the R graphical devices, or automatically detect ring borders using a linear detection algorithm. This algorithm detects ring borders according to positive/negative extreme values in the smoothed time-series of gray values. Most of the in-package routines can be recursively implemented using the multiDetect() function.
Maximum entropy density based dependent data bootstrap. 
  An algorithm is provided to create a population of time series (ensemble) 
  without assuming stationarity. The reference paper (Vinod, H.D., 2004 <DOI: 10.1016/j.jempfin.2003.06.002>) explains
  how the algorithm satisfies the ergodic theorem and the central limit theorem.
Functions to facilitate model-based clustering of nodes in a network in a mixture of experts setting, which incorporates covariate information on the nodes in the modelling process. Isobel Claire Gormley and Thomas Brendan Murphy (2010) <doi:10.1016/j.stamet.2010.01.002>.
Compare microbial co-occurrence networks created from 'trans_network' class of 'microeco' package <https://github.com/ChiLiubio/microeco>.
    This package is the extension of 'trans_network' class of 'microeco' package and especially useful when different networks are constructed and analyzed simultaneously.
Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) <doi:10.1002/1097-0258(20010115)20:1%3C139::AID-SIM644%3E3.0.CO;2-K> and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) <doi:10.1002/sim.3713>. Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) <doi:10.1002/sim.7011>. Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) <doi:10.1093/oxfordjournals.aje.a115715> and Rosner B, Spiegelman D & Willett WC (1992) <doi:10.1093/oxfordjournals.aje.a116453>, the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).
Nonparametric estimation and inference for natural direct and indirect effects by Chan, Imai, Yam and Zhang (2016) <arXiv:1601.03501>.
Multi Calculator of different scores to measure adherence to Mediterranean Diet, to compute them in nutriepidemiological data. Additionally, a sample dataset of this kind of data is provided, and some other minor tools useful in epidemiological studies.
Two distinct but related statistical approaches to the problem of identifying the combinations of medication error characteristics that are more likely to result in harm are implemented in this package: 1) a Bayesian hierarchical model with optimal Bayesian ranking on the log odds of harm, and 2) an empirical Bayes model that estimates the ratio of the observed count of harm to the count that would be expected if error characteristics and harm were independent. In addition, for the Bayesian hierarchical model, the package provides functions to assess the sensitivity of results to different specifications of the random effects distributions.
Function and support for medication and dosing information extraction from free-text clinical notes. Medication entities for the basic medExtractR implementation that can be extracted include drug name, strength, dose amount, dose, frequency, intake time, dose change, and time of last dose. The basic medExtractR is outlined in Weeks, Beck, McNeer, Williams, Bejan, Denny, Choi (2020) <doi: 10.1093/jamia/ocz207>. The extended medExtractR_tapering implementation is intended to extract dosing information for more tapering schedules, which are far more complex. The tapering extension allows for the extraction of additional entities including dispense amount, refills, dose schedule, time keyword, transition, and preposition.
Functions to simulate Mediterranean forest functioning and dynamics using cohort-based description of vegetation [De Caceres et al. (2015) <doi:10.1016/j.agrformet.2015.06.012>; De Caceres et al. (2021) <doi:10.1016/j.agrformet.2020.108233>].
Run flexible mediation analyses using natural effect models as described in 
  Lange, Vansteelandt and Bekaert (2012) <DOI:10.1093/aje/kwr525>, 
  Vansteelandt, Bekaert and Lange (2012) <DOI:10.1515/2161-962X.1014> 
  and Loeys, Moerkerke, De Smet, Buysse, Steen and Vansteelandt (2013) <DOI:10.1080/00273171.2013.832132>.
API wrapper to gather news stories, media information and tags from the 'mediacloud.org' API, based on a multilevel query <https://mediacloud.org/>. A personal API key is required.
Calculates MeDiA_K (means Mean Distance Association by K-nearest neighbor) in order to detect nonlinear associations.
Provides a general framework for clinical trial simulations based
    on the Clinical Scenario Evaluation (CSE) approach. The package supports a
    broad class of data models (including clinical trials with continuous, binary,
    survival-type and count-type endpoints as well as multivariate outcomes that are
    based on combinations of different endpoints), analysis strategies and commonly
    used evaluation criteria.
Efficient simulation-based power and sample size calculations are supported for a
  broad class of late-stage clinical trials. The following modules are included in
  the package:
  Adaptive designs with data-driven sample size or event count re-estimation,
  Adaptive designs with data-driven treatment selection,
  Adaptive designs with data-driven population selection,
  Optimal selection of a futility stopping rule,
  Event prediction in event-driven trials,
  Adaptive trials with response-adaptive randomization (experimental module),
  Traditional trials with multiple objectives (experimental module).
  Traditional trials with cluster-randomized designs (experimental module).
Extract textual data from different media channels 
    through its source based on users choice of keywords. 
    These data can be used to perform text analysis to 
    identify patterns in respective media reporting.
    The media channels used in this package are print media.
    The data (or news) used are publicly available to consumers.
Functions for calculating the point and interval estimates of the natural indirect effect (NIE), total effect (TE), and mediation proportion (MP), based on the product approach. We perform the methods considered in Cheng, Spiegelman, and Li (2021) Estimating the natural indirect effect and the mediation proportion via the product method.
We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, Imai and Yamamoto (2013) <DOI:10.1093/pan/mps040> and Yamamoto (2013) <http://web.mit.edu/teppei/www/research/IVmediate.pdf>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.
Simulation-based sensitivity analysis for causal mediation studies. It numerically and graphically evaluates the sensitivity of causal mediation analysis results 
 to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods. 
 First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as 
 sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the 
 strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured 
 pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in 
 different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based 
 methods. It is applicable to both randomized experiments and observational studies.
Provides access to well-documented medical datasets for teaching.
    Featuring several from the Teaching of Statistics in the Health Sciences 
    website <https://www.causeweb.org/tshs/category/dataset/>, a few reconstructed datasets of historical significance in medical
    research, some reformatted and extended from existing R packages, 
    and some data donations.
Generates risk estimates and comorbidity flags from ICD-9-CM
    codes available in administrative medical datasets. The package supports
    the Charlson Comorbidity Index, the Elixhauser Comorbidity
    classification, the Revised Cardiac Risk Index, and the Risk Stratification
    Index.  Methods are table-based, fast, and use the 'plyr' package, so
    parallelization is possible for large jobs. Also includes a sample of
    real ICD-9 data for 100 patients from a publicly available dataset.
Publicly available data from Medicare frequently requires extensive
    initial effort to extract desired variables and merge them; this package
    formalizes the techniques I've found work best. More information on the 
    Medicare program, as well as guidance for the publicly available data this package 
    targets, can be found on CMS's website covering publicly available data. See <https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.html>.
Prints a random quote from Marcus Aurelius' book Meditations.
Set of functions working with survey data in the format of the MEDITS project <https://www.sibm.it/SITO%20MEDITS/principaleprogramme.htm>. In this version, functions use TA, TB and TC tables respectively containing haul, catch and aggregated biological data.
Contains a dataset of morphological and structural features of 'Medicinal LEAves (MedLEA)'. The features of each species is recorded by manually viewing the medicinal plant repository available at (<http://www.instituteofayurveda.org/plants/>). You can also download repository of leaf images of 1099 medicinal plants in Sri Lanka.
An increasingly important source of health-related bibliographic 
    content are preprints - preliminary versions of research articles that have
    yet to undergo peer review. The two preprint repositories most relevant to 
    health-related sciences are medRxiv <https://www.medrxiv.org/> and
    bioRxiv <https://www.biorxiv.org/>, both of which are operated by the Cold 
    Spring Harbor Laboratory. 'medrxivr' provides programmatic access to the 
    'Cold Spring Harbour Laboratory (CSHL)' API <https://api.biorxiv.org/>,
    allowing users to easily download medRxiv and bioRxiv preprint metadata
    (e.g. title, abstract, publication date, author list, etc) into R. 
    'medrxivr' also provides functions to search the downloaded preprint records 
    using regular expressions and Boolean logic, as well as helper functions 
    that allow users to export their search results to a .BIB file for easy 
    import to a reference manager and to download the full-text PDFs of 
    preprints matching their search criteria.
A collection of methods for large scale single mediator hypothesis
    testing. The six included methods for testing the mediation effect are Sobel's
    test, Max P test, joint significance test under the composite null hypothesis,
    high dimensional mediation testing, divide-aggregate composite null test,
    and Sobel's test under the composite null hypothesis. Du, J., Zhou, X., Hao, W.,
    Liu, Y., Smith, J. A., & Mukherjee, B (2022) "Methods for Large-scale Single
    Mediator Hypothesis Testing: Possible Choices and Comparisons."
    arXiv preprint <arXiv:2203.13293>.
Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics. Sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.
It is a computer tool to conduct linear mediation analysis for complex surveys using multi-stage sampling and Balanced Repeated Replication (BRR). Specifically, the mediation analysis method using balanced repeated replications was proposed by Mai, Ha, and Soulakova (2019) <DOI:10.1080/10705511.2018.1559065>. The current version can only handle continuous mediators and outcomes. The development of 'MedSurvey' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of MedSurvey do not necessarily represent the policy of the ALSAC.
Allows management of 'Meetup' groups via the <https:www.meetup.com/meetup_api/>.
    Provided are a set of functions that enable fetching information of joined meetups, attendance,
    and members. This package requires the use of an API key.
A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.
An S4 update of the 'mefa' package
  using sparse matrices for enhanced efficiency.
  Sparse array-like objects are supported via
  lists of sparse matrices.
Uses as input genetic data that have been reformatted and
     stored in a 'SQLite' database; this database is initially created
     by the standalone 'mega2' C++ program (available freely from
     <https://watson.hgen.pitt.edu/register/>). Loads and manipulates
     data frames containing genotype, phenotype, and family
     information from the input 'SQLite' database, and decompresses
     needed subsets of the genotype data, on the fly, in a memory
     efficient manner.  We have also created several more functions
     that illustrate how to use the data frames as well as perform
     useful tasks: these permit one to run the 'pedgene' package to
     carry out gene-based association tests on family data using
     selected marker subsets, to run the 'SKAT' package to carry out
     gene-based association tests using selected marker subsets, to
     run the 'famSKATRC' package to carry out gene-based association
     tests on families (optionally) and with rare or common variants
     using selected marker subsets, to output the 'Mega2R' data as a
     VCF file and related files (for phenotype and family data), and
     to convert the data frames into CoreArray Genomic Data Structure
     (GDS) format.
Co-Expression Network Analysis by adopting network embedding technique. Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.
Exploratory model analysis with <http://ggobi.org>.  
    Fit and graphical explore ensembles of linear models.
Create variable width bar charts i.e.
    "bar mekko" charts to include important quantitative context.
    Closely related to mosaic, spine (or spinogram), matrix,
    submarine, olympic, Mondrian or product plots and tree maps.
Performs multiple empirical likelihood tests for linear and
    generalized linear models. The package offers an easy-to-use interface
    and flexibility in specifying hypotheses and calibration methods,
    extending the framework to simultaneous inferences. The core
    computational routines are implemented using the 'Eigen' C++ library
    and 'RcppEigen' interface, with OpenMP for parallel computation.
    Details of the testing procedures are given in Kim, MacEachern, and
    Peruggia (2021) <arxiv:2112.09206>. This work was supported by the
    U.S. National Science Foundation under Grants No. SES-1921523 and
    DMS-2015552.
The goal of 'meltr' is to provide a fast and friendly way to
    read non-rectangular data, such as ragged forms of csv (comma-separated
    values), tsv (tab-separated values), and fwf (fixed-width format) files.
Framework for merging and disambiguating event data based on spatiotemporal co-occurrence and secondary event characteristics. It can account for intrinsic "fuzziness" in the coding of events, varying event taxonomies and different geo-precision codes.
The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week.
The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week. 'memapp' is a web application created in the Shiny framework for the 'mem' R package.
The word 'Meme' was originated from the book, 'The Selfish Gene', authored by Richard Dawkins (1976).
             It is a unit of culture that is passed from one generation to another and correlates to the gene, the unit of physical heredity.
             The internet memes are captioned photos that are intended to be funny, ridiculous.
             Memes behave like infectious viruses and travel from person to person quickly through social media.
             The 'meme' package allows users to make custom memes.
Generates internet memes that optionally include a superimposed inset plot and other atypical features, 
    combining the visual impact of an attention-grabbing meme with graphic results of data analysis.
    The package differs from related packages that focus on imitating and reproducing standard memes.
    Some packages do this by interfacing with online meme generators whereas others achieve this natively.
    This package takes the latter approach. It does not interface with online meme generators or require any authentication with external websites.
    It reads images directly from local files or via URL and meme generation is done by the package.
    While this is similar to the 'meme' package available on CRAN, it differs in that the focus is on 
    allowing for non-standard meme layouts and hybrids of memes mixed with graphs.
    While this package can be used to make basic memes like an online meme generator would produce, 
    it caters primarily to hybrid graph-meme plots where the meme presentation can be seen as a backdrop highlighting 
    foreground graphs of data analysis results.
    The package also provides support for an arbitrary number of meme text labels with arbitrary size, position and other attributes 
    rather than restricting to the standard top and/or bottom text placement. 
    This is useful for proper aesthetic interleaving of plots of data between meme image backgrounds and overlain text labels.
    The package offers a selection of templates for graph placement and appearance with respect to the underlying meme.
    Graph templates also permit additional template-specific customization.
    Animated gif support is provided but this is optional and functional only if the 'magick' package is installed. 
    'magick' is not required unless gif functionality is desired.
Can detect relatively weak spatial genetic patterns by using Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation.  Has applications in landscape genetics where the movement and dispersal of organisms are studied using neutral genetic variation.
A simple way to construct and maintain functions that keep state i.e. remember their argument lists. This can be useful when one needs to repeatedly invoke the same function with only a small number of argument changes at each invocation.
An infrastructure for the management of survey data including
        value labels, definable missing values, recoding of variables,
        production of code books, and import of (subsets of) 'SPSS' and
        'Stata' files is provided. Further, the package allows to produce
        tables and data frames of arbitrary descriptive statistics and
        (almost) publication-ready tables of regression model
        estimates, which can be exported to 'LaTeX' and HTML.
A simple in-memory, LRU cache that can be wrapped
    around any function to memoize it. The cache is keyed on a hash of the
    input data (using 'digest') or on pointer equivalence.
A simple way to memoize function results to improve performance by eliminating unnecessary computation or data retrieval activities.
Producing high-quality documents suitable for publication directly from R is made possible by the R Markdown ecosystem.
  'memoiR' makes it easy.
  It provides templates to knit memoirs, articles and slideshows with helpers to publish the documents on GitHub Pages and activate continuous integration.
Cache the results of a function so that when you
    call it again with the same arguments it returns the previously computed
    value.
A 'rmarkdown' template that supports company logo, contact info, 
    watermarks and more. Currently restricted to 'Latex'/'Markdown'; a similar 
    'HTML' theme will be added in the future.
Tools to quantify ecological memory in long time-series with Random Forest models (Breiman 2001 <doi:10.1023/A:1010933404324>) fitted with the 'ranger' library (Wright and Ziegler 2017 <doi:10.18637/jss.v077.i01>). Particularly oriented to palaeoecological datasets and simulated pollen curves produced by the 'virtualPollen' package, but also applicable to other long time-series involving a set of environmental drivers and a biotic response.
Data sets and sample analyses from Pinheiro and Bates,
  "Mixed-effects Models in S and S-PLUS" (Springer, 2000).
How much ram do you need to store a 100,000 by 100,000 matrix?
    How much ram is your current R session using? How much ram do you even have?
    Learn the scintillating answer to these and many more such questions with
    the 'memuse' package.
Encodes several methods for performing Mendelian randomization
    analyses with summarized data. Summarized data on genetic associations with the
    exposure and with the outcome can be obtained from large consortia. These data
    can be used for obtaining causal estimates using instrumental variable methods.
Based on the input data an n-dimensional cube with sub cells of user specified side length is created.
    The number of sample points which fall in each sub cube is counted, and with the cell volume and overall sample
    size an empirical probability can be computed. A number of cubes of higher resolution can be superimposed. The
    basic method stems from J.L. Bentley in "Multidimensional Divide and Conquer".
    J. L. Bentley (1980) <doi:10.1145/358841.358850>.
    Furthermore a simple kernel density estimation method is made available, as well as an expansion of Bentleys
    method, which offers a kernel approach for the grid method.
Defines the classes used to explore, cluster and
  visualize distance matrices, especially those arising from binary
  data. See Abrams and colleagues, 2021, <doi:10.1093/bioinformatics/btab037>.
Compute case-wise and cluster-wise derivative for mixed effects models with respect to fixed effects parameter, random effect (co)variances, and residual variance. This material is partially based on work supported by the National Science Foundation under Grant Number 1460719.
Package to carry out merged block randomization (Van der Pas (2019), <doi:10.1177/1740774519827957>), a restricted randomization method designed for small clinical trials (at most 100 subjects) or trials with small strata, for example in multicentre trials. It can be used for more than two groups or unequal randomization ratios.
Aggregates a set of trees with the same leaves to create a consensus tree. The trees are typically 
  obtained via hierarchical clustering, hence the hclust format is used to encode both the aggregated trees and the final 
  consensus tree. The method is exact and proven to be O(nqlog(n)), n being the individuals and q being the number of trees to aggregate.
Fits linear, non-linear, and user-defined mixed effects regression models following 
  the framework developed by Crowther (2017) <arXiv:1710.02223>. 'merlin' can fit multivariate 
  outcome models of any type, each of which could be repeatedly measured (longitudinal), with 
  any number of levels, and with any number of random effects at each level. Standard 
  distributions/models available include the Bernoulli, Gaussian, Poisson, beta, 
  negative-binomial, and time-to-event/survival models include the exponential, 
  Gompertz, Royston-Parmar, Weibull and general hazard model. 'merlin' provides a 
  flexible predictor syntax, allowing the user to define variables, random effects, 
  spline and fractional polynomial functions, functions of 
  other outcome models, and any interaction between each of them. Non-linear and 
  time-dependent effects are seamlessly incorporated into the predictor. 'merlin' 
  allows multivariate normal random effects, which are integrated out using Gaussian 
  quadrature or Monte-Carlo integration. Note, 'merlin' is based on the 'Stata' package 
  of the same name, described in Crowther (2018) <arXiv:1806.01615>.
Perform missing value imputation for biological data using the random forest algorithm, the imputation aim to keep the original mean and standard deviation consistent after imputation.
N>=3 methods are used to measure each of n items. 
 The data are used to estimate simultaneously systematic error (bias)
 and random error (imprecision). Observed measurements for each method
 or device are assumed to be linear functions of the unknown true values
 and the errors are assumed normally distributed. Maximum likelihood 
 estimation is used for the imprecision standard deviation estimates. 
 Pairwise calibration curves and plots can be easily generated.
Provides methods for extracting results from mixed-effect model
    objects fit with the 'lme4' package. Allows construction of prediction intervals
    efficiently from large scale linear and generalized linear mixed-effects models.
Fits Bayesian regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889>, Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>, Peruzzi and Dunson (2022) <arXiv:2201.10080>. Funded by ERC grant 856506 and NIH grant R01ES028804.
Provides some utilities for 3D meshes: clipping of a mesh to
    the volume bounded by another mesh, decomposition into convex parts,
    distance between a mesh and a point, volume, area, and centroid. All
    algorithms are performed by the 'C++' library 'CGAL'
    (<https://www.cgal.org/>).
A mixed collection of useful and semi-useful diverse
    statistical functions, some of which may even be referenced in
    The R Primer book.
Provides tools for creating and issuing nicely-formatted
    text within R diagnostic messages and those messages given during
    warnings and errors. The formatting of the messages can be
    customized using templating features. Issues with singular and
    plural forms can be handled through specialized syntax.
Contains a set of tools for constructing and coercing
  into and from the "mdate" class. 
  This date class implements ISO 8601-2:2019(E) and
  allows regular dates to be annotated 
  to express unspecified date components,
  approximate or uncertain date components, 
  date ranges, and sets of dates. 
  This is useful for describing and analysing temporal information,
  whether historical or recent, where date precision may vary.
User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <DOI:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015):
 - common effect and random effects meta-analysis;
 - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble);
 - three-level meta-analysis model;
 - generalised linear mixed model;
 - Hartung-Knapp method for random effects model;
 - Kenward-Roger method for random effects model;
 - prediction interval;
 - statistical tests  for funnel plot asymmetry;
 - trim-and-fill method to evaluate bias in meta-analysis;
 - meta-regression;
 - cumulative meta-analysis and leave-one-out meta-analysis;
 - import data from 'RevMan 5';
 - produce forest plot summarising several (subgroup) meta-analyses.
Implement meta-analyses for simultaneously estimating individual means with shrinkage,
 isotonic regression and pretests. Include our original implementation of the isotonic regression via the pool-adjacent-violators algorithm (PAVA) algorithm.
 This methodology is published in Taketomi et al.(2021) <doi:10.3390/axioms10040267>.
Bayesian inference analysis for bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximation with INLA. A purpose built graphic user interface is available. The installation of R package INLA is compulsory for successful usage. The INLA package can be obtained from <https://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE).
An interactive application to visualise meta-analysis data as a
    physical weighing machine. The interface is based on the Shiny web application
    framework, though can be run locally and with the user's own data.
The sample mean and standard deviation are two commonly used statistics in meta-analyses, 
    but some trials use other summary statistics such as the median and quartiles to report the results. 
    Therefore, researchers need to transform those information back to the sample mean and 
    standard deviation. This package implemented sample mean estimators by Luo et al. (2016) <arXiv:1505.05687>, sample standard deviation estimators by Wan et al. (2014) <arXiv:1407.8038>, and the best linear unbiased estimators (BLUEs) of location and scale parameters by Yang et al. (2018, submitted) based on sample quantiles derived summaries in a meta-analysis.
Computes the posterior model probabilities for standard meta-analysis models 
    (null model vs. alternative model assuming either fixed- or random-effects, respectively).
    These posterior probabilities are used to estimate the overall mean effect size 
    as the weighted average of the mean effect size estimates of the random- and 
    fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & 
    Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define 
    a wide range of non-informative or informative priors for the mean effect size 
    and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, 
    meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) 
    priors can be fitted and tested. This allows to compute Bayes factors and 
    perform Bayesian model averaging across random- and fixed-effects meta-analysis 
    with and without moderators. For a primer on Bayesian model-averaged meta-analysis, 
    see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2020, <doi:10.31234/osf.io/97qup>).
Data sets from a variety of biological sample matrices, 
    analysed using a number of mass spectrometry based metabolomic analytical techniques.
    The example data sets are stored remotely using GitHub releases 
    <https://github.com/aberHRML/metaboData/releases> which can be accessed from R using the package.
    The package also includes the 'abr1' FIE-MS data set from the 'FIEmspro' package <https://users.aber.ac.uk/jhd/> <doi:10.1038/nprot.2007.511>.
Fits probabilistic principal components analysis,
        probabilistic principal components and covariates analysis and
        mixtures of probabilistic principal components models to
        metabolomic spectral data.
Dataset and functions from the meta-analysis published in Medicine & Science in Sports & Exercise. 
    It contains all the data and functions to reproduce the analysis.
    "Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk Factors in Health and Disease: A Meta-analysis".
    Felipe Mattioni Maturana, Peter Martus, Stephan Zipfel, Andreas M Nieß (2020) <doi:10.1249/MSS.0000000000002506>.
An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. 
 Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. 
 Several methods were implemented to reduce the metabolomics matrix such as the  principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9> , 
 the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, the 
 elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>. 
 Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. 
 Large scale cross validation can be performed in  order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.
Access to the 'Metabolights' REST API <https://www.ebi.ac.uk/metabolights/index>. Retrieve elements of publicly available 'Metabolights' studies.
A set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification. A detailed description of best practice usage may be found in the publication <https://link.springer.com/protocol/10.1007/978-1-4939-7819-9_20>.
Tools to preprocess, analyse, and visualize metabolomics data. 
  We included a set of functions for sample and metabolite quality control, 
  outlier detection, missing value imputation, dimensional reduction, normalization, 
  data integration, regression, metabolite annotation, enrichment analysis, 
  and visualization of data and results. The package is designed to be a comprehensive R package 
  that can be easily used by researchers with basic R programming skills. 
  The framework designed here is versatile and is extensible to other various methods.
Takes QC signal for each day and normalize metabolomic
    data that has been acquired in a certain period of time. At least
    three QC per day are required.
Contains functions that allow Bayesian meta-analysis (1) with binomial data, counts(y) and total counts (n) or, (2) with user-supplied point estimates and associated variances.   Case (1) provides an analysis based on the logit transformation of the sample proportion. This methodology is also appropriate for combining data from sample surveys and related sources. The functions can  calculate the corresponding similarity matrix. More details can be found in Cahoy and Sedransk (2023), Cahoy and Sedransk (2022)  <doi:10.1007/s42519-018-0027-2>, Evans and Sedransk (2001) <doi:10.1093/biomet/88.3.643>, and Malec and Sedransk (1992) <doi:10.1093/biomet/79.3.593>.
Meta-CART integrates classification and regression trees (CART) into meta-analysis. Meta-CART is a flexible approach to identify interaction effects between moderators in meta-analysis. The method is described in Dusseldorp et al. (2014) <doi:10.1037/hea0000018> and Li et al. (2017) <doi:10.1111/bmsp.12088>.
Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.
A set of tools for parsing, manipulating, and graphing data
    classified by a hierarchy (e.g. a taxonomy).
Functions to analyze coherence, boundary clumping, and turnover
    following the pattern-based metacommunity analysis of Leibold and Mikkelson
    2002  <doi:10.1034/j.1600-0706.2002.970210.x>. The package also includes 
		functions to visualize ecological networks, and to calculate modularity 
		as a replacement to boundary clumping.
Implements routines for metagenome sample taxonomy assignments collection, 
    aggregation, and visualization. Accepts the EDGE-formatted output from GOTTCHA/GOTTCHA2, 
    BWA, Kraken, MetaPhlAn, DIAMOND, and Pangia. Produces SVG and PDF heatmap-like plots 
    comparing taxa abundances across projects.
Visualize 'confounder' control in meta-analysis.
    'metaconfoundr' is an approach to evaluating bias in studies used in
    meta-analyses based on the causal inference framework. Study groups
    create a causal diagram displaying their assumptions about the
    scientific question. From this, they develop a list of important
    'confounders'. Then, they evaluate whether studies controlled for
    these variables well. 'metaconfoundr' is a toolkit to facilitate this
    process and visualize the results as heat maps, traffic light plots,
    and more.
Implement the DerSimonian-Laird (DSL) and Olkin-Pratt (OP)
        meta-analytical approaches with correlation coefficients as
        effect sizes.
Create an immutable container holding metadata for the purpose of better enabling programming activities and functionality of other packages within the clinical programming workflow.
Login, download, and analyze questions predicted by you and/or the 
    Metaculus community by interacting with the Metaculus API, currently
    located at <https://www.metaculus.com/api2/>.
There are two functions-meta2d and meta3d for
    detecting rhythmic signals from time-series datasets. For analyzing
    time-series datasets without individual information, 'meta2d' is 
    suggested, which could incorporates multiple methods from ARSER, 
    JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For 
    analyzing time-series datasets with individual information, 'meta3d' is 
    suggested, which takes use of any one of these three methods to analyze 
	time-series data individual by individual and gives out integrated values 
    based on analysis result of each individual.
A collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses.
High-throughput, flexible and reproducible extraction of data from figures in primary research papers. metaDigitise() can extract data and / or automatically calculate summary statistics for users from box plots, bar plots (e.g., mean and errors), scatter plots and histograms.
Metadynamics is a state of the art biomolecular simulation technique.
  'Plumed' Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes
  it possible to perform metadynamics using various simulation codes. The results of
  metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package
  'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package.
  It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037>
  to calculate a free energy surface from hills. Minima can be located and plotted on
  the free energy surface. Transition states can be analyzed by Nudged Elastic Band
  method by Henkelman, G. and Jonsson, H. (2000) <doi:10.1063/1.1323224>. Free energy
  surfaces, minima and transition paths can be plotted to produce publication quality
  images.
Metadynamics is a state of the art biomolecular simulation technique.
  'Plumed' Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes
  it possible to perform metadynamics using various simulation codes. The results of
  metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package
  'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package.
  As an addendum, 'metadynaminer3d' is used to visualize 3D hills. It uses
  a fast algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037>
  to calculate a free energy surface from hills. Minima can be located and plotted on
  the free energy surface. Free energy surfaces and minima can be plotted to produce
  publication quality images.
Extends the base classes and methods of 'caret' package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning <https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/> of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.
A tool to simulate salmon metapopulations and apply financial
    portfolio optimization concepts. The package accompanies the paper 
    Anderson et al. (2015) <doi:10.1101/2022.03.24.485545>.
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.
Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates, even with as little as 20 studies (Van Lissa, in preparation). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.
Fused lasso method to cluster and estimate regression coefficients
    of the same covariate across different data sets when a large number of
    independent data sets are combined. Package supports Gaussian, binomial,
    Poisson and Cox PH models.
Meta-analysis of generalized additive
    models and generalized additive mixed models. A typical use case is
    when data cannot be shared across locations, and an overall meta-analytic
    fit is sought. 'metagam' provides functionality for removing individual
    participant data from models computed using the 'mgcv' and 'gamm4' packages such
    that the model objects can be shared without exposing individual data.
    Furthermore, methods for meta-analysing these fits are provided. The implemented
    methods are described in Sorensen et al. (2020), <doi:10.1016/j.neuroimage.2020.117416>,
    extending previous works by Schwartz and Zanobetti (2000)
    and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.
Functionalities for facilitating systematic reviews, data
    extractions, and meta-analyses. It includes a GUI (graphical user interface)
    to help screen the abstracts and titles of bibliographic data; tools to assign
    screening effort across multiple collaborators/reviewers and to assess inter-
    reviewer reliability; tools to help automate the download and retrieval of
    journal PDF articles from online databases; figure and image extractions 
    from PDFs; web scraping of citations; automated and manual data extraction 
    from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for
    Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools
    to fill gaps in incomplete or missing study parameters; generation of random
    effects sizes for Hedges' d, log response ratio, odds ratio, and correlation
    coefficients for Monte Carlo experiments; covariance equations for modelling
    dependencies among multiple effect sizes (e.g., effect sizes with a common
    control); and finally summaries that replicate analyses and outputs from 
    widely used but no longer updated meta-analysis software (i.e., metawin).
	Funding for this package was supported by National Science Foundation (NSF) 
	grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) 
	Facilitating systematic reviews, data extraction and meta-analysis with the 
	metagear package for R. Methods in Ecology and Evolution 7, 323-330 
	<doi:10.1111/2041-210X.12472>.
According to a phenomenon known as "the wisdom of the crowds," 
    combining point estimates from multiple judges often provides a 
    more accurate aggregate estimate than using a point estimate from
    a single judge. However, if the judges use shared information in 
    their estimates, the simple average will over-emphasize this common
    component at the expense of the judges’ private information. 
    Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds
    Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions"
    <https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3504286> proposes
    a procedure for calculating a weighted average of the judges’ individual
    estimates such that resulting aggregate estimate appropriately combines
    the judges' collective information within a single estimation problem. 
    The authors use both simulation and data from six experimental studies
    to illustrate that the weighting procedure outperforms existing averaging-like
    methods, such as the equally weighted average, trimmed average, and median.
    This aggregate estimate -- know as "the knowledge-weighted estimate" --
    inputs a) judges' estimates of a continuous outcome (E) and 
    b) predictions of others' average estimate of this outcome (P).  
    In this R-package, the function knowledge_weighted_estimate(E,P) 
    implements the knowledge-weighted estimate. Its use is illustrated
    with a simple stylized example and on real-world experimental data.
A nature-inspired metaheuristics algorithm based on the pollination
    process of flowers. This R package makes it easy to implement the standard
    flower pollination algorithm for every user. The algorithm was first developed
    by Xin-She Yang in 2012 (<DOI:10.1007/978-3-642-32894-7_27>).
An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>),  whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).
An ensemble meta-prediction framework to integrate multiple regression 
    models into a current study. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020) 
    <arXiv:2010.09971>.
    A meta-analysis framework along with two weighted estimators as the ensemble 
    of empirical Bayes estimators, which combines the estimates from the different 
    external models. The proposed framework is flexible and robust in the ways 
    that (i) it is capable of incorporating external models that use a slightly 
    different set of covariates; (ii) it is able to identify the most relevant 
    external information and diminish the influence of information that is less 
    compatible with the internal data; and (iii) it nicely balances the bias-variance 
    trade-off while preserving the most efficiency gain. The proposed estimators 
    are more efficient than the naive analysis of the internal data and other 
    naive combinations of external estimators.
A pipeline for the meta-analysis  of gene expression data. We have
	assembled several analysis and plot functions to
    perform integrated multi-cohort analysis of gene expression data (meta-
    analysis). Methodology described in:
	<http://biorxiv.org/content/early/2016/08/25/071514>.
A set of tools to foster the development of reproducible analytical workflow by simplifying the download of data and 
    metadata from 'DataONE' (<https://www.dataone.org>) and easily importing this information into R.
Tools to generate random landscape graphs, evaluate species
    occurrence in dynamic landscapes, simulate future landscape occupation and
    evaluate range expansion when new empty patches are available (e.g. as a
    result of climate change). References: Mestre, F., Canovas, F., Pita, R., 
    Mira, A., Beja, P. (2016) <doi:10.1016/j.envsoft.2016.03.007>; Mestre, F., 
    Risk, B., Mira, A., Beja, P., Pita, R. (2017) 
    <doi:10.1016/j.ecolmodel.2017.06.013>; Mestre, F., Pita, R., Mira, A., Beja,
    P. (2020) <doi:10.1186/s12898-019-0273-5>.
First- and higher-order likelihood inference in
        meta-analysis and meta-regression models.
Identify time intervals of differentially abundant metagenomic features in longitudinal studies (Metwally AA, et al., Microbiome, 2018 <doi:10.1186/s40168-018-0402-y>).
The 'MetAlyzer' S4 object provides methods to read and reformat metabolomics data for convenient data handling, statistics and downstream analysis. The resulting format corresponds to input data of the Shiny app 'MetaboExtract' (<https://www.metaboextract.shiny.dkfz.de/MetaboExtract/>).
Combination of either p-values or modified effect sizes from different
    studies to find differentially expressed genes.
Implements several methods to meta-analyze studies that report the 
    sample median of the outcome. When the primary studies are one-group 
    studies, the methods of McGrath et al. (2019) <doi:10.1002/sim.8013> can 
    be applied to estimate the pooled median. In the two-group context, the 
    methods of McGrath et al. (2020) <doi:10.1002/bimj.201900036> can be 
    applied to estimate the pooled raw difference of medians across groups.
Creates data with identical statistics (metamers) using an iterative 
   algorithm proposed by Matejka & Fitzmaurice (2017) <DOI:10.1145/3025453.3025912>.
Generalized Additive Model for Location, Scale and Shape (GAMLSS) 
    with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data 
    (with various options for data transformation/normalization to address compositional effects) and 
    random effects meta-analysis models for meta-analysis pooling estimates across microbiome studies 
    are implemented. 
    Random Forest model to predict microbiome age based on relative abundances of  
    shared bacterial genera with the Bangladesh data (Subramanian et al 2014), 
    comparison of multiple diversity indexes using linear/linear mixed effect models 
    and some data display/visualization are also implemented.
    The reference paper is published by 
    Ho NT, Li F, Wang S, Kuhn L (2019) <doi:10.1186/s12859-019-2744-2> .
Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to  summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.
Resolves complex metagenomic mixtures by analysing
    deep sequencing data, using a mixture model based approach.
    The use of parallel Monte Carlo Markov chains for the exploration
    of the species space enables the identification of the set
    of species more likely to contribute to the mixture.
Performs stability analysis of multi-environment trial data
    using parametric and non-parametric methods. Parametric methods
    includes Additive Main Effects and Multiplicative Interaction (AMMI)
    analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>, Ecovalence
    by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot
    analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, geometric
    adaptability index by Mohammadi & Amri (2008)
    <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart
    & Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>,
    genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's
    (2004) method, power law residuals (POLAR) statistics by Doring et al.
    (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of
    variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>,
    stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>,
    weighted average of absolute scores by Olivoto et al. (2019a)
    <doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by
    Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>.
    Non-parametric methods includes superiority index by Lin & Binns
    (1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic
    stability by Huehn (1990)
    <https://link.springer.com/article/10.1007/BF00024241>, TOP third
    statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for
    computing biometrical analysis such as path analysis, canonical
    correlation, partial correlation, clustering analysis, and tools for
    inspecting, manipulating, summarizing and plotting typical
    multi-environment trial data are also provided.
Novel method to unbiasedly include studies with Non-statistically Significant Unreported Effects (NSUEs) in a meta-analysis. First, the method calculates the interval where the unreported effects (e.g., t-values) should be according to the threshold of statistical significance used in each study. Afterwards, the method uses maximum likelihood techniques to impute the expected effect size of each study with NSUEs, accounting for between-study heterogeneity and potential covariates. Multiple imputations of the NSUEs are then randomly created based on the expected value, variance, and statistical significance bounds. Finally, it conducts a restricted-maximum likelihood random-effects meta-analysis separately for each set of imputations and it conducts estimations from these meta-analyses. Please read the reference in 'metansue' for details of the procedure.
The canonical way to perform meta-analysis involves using effect sizes.
   When they are not available this package provides a number of methods for
   meta-analysis of significance values including the methods of Edgington, Fisher,
   Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results;
   and routines for graphical display.
Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015) <doi:10.1080/01621459.2015.1006065> and Hao Li, Daeyoung Lim, Ming-Hui Chen, Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin (2021) <doi:10.1002/sim.8983>. For maximal computational efficiency, the Markov chain Monte Carlo samplers for each model, written in C++, are fine-tuned. This software has been developed under the auspices of the National Institutes of Health and Merck & Co., Inc., Kenilworth, NJ, USA.
Designs plots in terms of core structure.  See 'example(metaplot)'.
 Primary arguments are (unquoted) column names; order and type (numeric or not)
 dictate the resulting plot.  Specify any y variables, x variable, any groups variable,
 and any conditioning variables to metaplot() to generate density plots, boxplots, 
 mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() 
 to arrange plots in grids. Wherever present, scalar column attributes 'label' and 'guide' 
 are honored, producing fully annotated plots with minimal effort. Attribute 'guide' 
 is typically units, but may be encoded() to provide interpretations of categorical 
 values (see '?encode').  Utility unpack() transforms scalar column attributes to row 
 values and pack() does the reverse, supporting tool-neutral storage of metadata along 
 with primary data. The package supports customizable aesthetics such as such as reference 
 lines, unity lines, smooths, log transformation, and linear fits. The user may choose
 between trellis and ggplot output. Compact syntax and integrated metadata promote workflow 
 scalability.
Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).
Provides an interface to 'MetaPost' (Hobby, 1998) 
    <http://www.tug.org/docs/metapost/mpman.pdf>.
    There are functions to generate an R description of a 'MetaPost'
    curve, functions to generate 'MetaPost' code from an R description,
    functions to process 'MetaPost' code, and functions to read
    solved 'MetaPost' paths back into R.
A simple and effective tool for computing and visualizing statistical power for meta-analysis,
    including power analysis of main effects (Jackson & Turner, 2017)<doi:10.1002/jrsm.1240>, 
    test of homogeneity (Pigott, 2012)<doi:10.1007/978-1-4614-2278-5>, subgroup analysis, 
    and categorical moderator analysis (Hedges & Pigott, 2004)<doi:10.1037/1082-989X.9.4.426>.
Set of tools for descriptive analysis of metaproteomics data generated from high-throughput 
    mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, 
    expressed as spectral counts, and to manipulate them in groups of metaproteins. This information 
    can be represented using multiple visualization functions to portray the global metaproteome 
    landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, 
    taxonomic levels and/or functional annotation. The provided tools allow to implement flexible 
    analytical pipelines that can be easily applied to studies interested in metaproteomics analysis.
User-friendly package for reporting replicability-analysis methods, affixed to meta-analyses summary. This package implements the methods introduced in Jaljuli et. al. (2022) <doi:10.1080/19466315.2022.2050291>. The replicability-analysis output provides an assessment of the investigated intervention, where it offers quantification of effect replicability and assessment of the consistency of findings.
 - Replicability-analysis for fixed-effects and random-effect meta analysis: 
 - r(u)-value;
 - lower bounds on the number of studies with replicated positive and\or negative effect;
 - Allows detecting inconsistency of signals;
 - forest plots with the summary of replicability analysis results;
 - Allows Replicability-analysis with or without the common-effect assumption.
R implementation of a multivariate meta-analysis of randomized controlled trials (RCT) with the difference in restricted mean survival times (RMSTD). Use this package with individual patient level data from an RCT for a time-to-event outcome to determine combined effect estimates according to 4 methods: 1)  a univariate meta-analysis using observed treatment effects, 2) a univariate meta-analysis using effects predicted by fitted Royston-Parmar flexible parametric models, 3) multivariate meta-analysis with analytically derived covariance, 4) multivariate meta-analysis with bootstrap derived covariance. This package computes all combined effects and provides an RMSTD curve with combined effect estimates and their confidence intervals.
Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.
Regression methods for the meta-SDT model. The package implements methods for cognitive experiments of metacognition as described in Kristensen, S. B., Sandberg, K., & Bibby, B. M. (2020). Regression methods for metacognitive sensitivity. Journal of Mathematical Psychology, 94. <doi:10.1016/j.jmp.2019.102297>.
A collection of functions for conducting meta-analysis using a
             structural equation modeling (SEM) approach via the 'OpenMx' and
             'lavaan' packages. It also implements various procedures to
			 perform meta-analytic structural equation modeling on the
             correlation and covariance matrices,
			 see Cheung (2015) <doi:10.3389/fpsyg.2014.01521>.
The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 5 'Small-Study Effects in Meta-Analysis':
 - Copas selection model described in Copas & Shi (2001) <DOI:10.1177/096228020101000402>;
 - limit meta-analysis by Rücker et al. (2011) <DOI:10.1093/biostatistics/kxq046>;
 - upper bound for outcome reporting bias by Copas & Jackson (2004) <DOI:10.1111/j.0006-341X.2004.00161.x>;
 - imputation methods for missing binary data by Gamble & Hollis (2005) <DOI:10.1016/j.jclinepi.2004.09.013> and Higgins et al. (2008) <DOI:10.1177/1740774508091600>;
 - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) <DOI:10.1097/XEB.0000000000000141>.
Functions for Meta-analysis Burden Test, Sequence Kernel Association Test (SKAT) and Optimal SKAT (SKAT-O) by  Lee et al. (2013) <doi:10.1016/j.ajhg.2013.05.010>. These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.
Performs Bayesian meta-analysis, meta-regression and model-based meta-analysis 
             using 'Stan'. Includes binomial-normal hierarchical models and option to use 
             weakly informative priors for the heterogeneity parameter and the treatment effect 
             parameter which are described in Guenhan, Roever, and Friede (2020) <doi:10.1002/jrsm.1370>.
If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. 
      The validation cohort needs to be independent from the meta-Genome-Wide-Association-Study (meta-GWAS) results. 
      'MetaSubtract' will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. 
      It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. 
      It can also handle different meta-analysis methods. It can be used for whole GWAS, but also for a limited set of genetic markers. 
      See for application: Nolte I.M. et al. (2017); <doi: 10.1038/ejhg.2017.50>.
To assess a summary survival curve from survival probabilities and number of at-risk patients collected at various points in time in various studies, and to test the between-strata heterogeneity.
Fits and tests meta regression models and generates a
 number of useful test statistics: next to t- and z-tests, the likelihood ratio,
 bartlett corrected likelihood ratio and permutation tests are performed on
 the model coefficients.
Create meta tags for 'R Markdown' HTML documents and 'Shiny'
    apps for customized social media cards, for accessibility, and quality
    search engine indexing. 'metathis' currently supports HTML documents
    created with 'rmarkdown', 'shiny', 'xaringan', 'pagedown', 'bookdown',
    and 'flexdashboard'.
Uses the metadata information stored in 'metacore' objects to check and build metadata associated columns.
A comprehensive range of facilities to perform umbrella reviews with stratification of the evidence in R. The package accomplishes this aim by building on three core functions that: (i) automatically perform all required calculations in an umbrella review (including but not limited to meta-analyses), (ii) stratify evidence according to various classification criteria, and (iii) generate a visual representation of the results. Note that if you are not familiar with R, the core features of this package are available from a web browser (<https://www.metaumbrella.org/>).
Contains functions to estimate the proportion of effects stronger than a threshold
    of scientific importance (function prop_stronger), to nonparametrically characterize the distribution of effects in a meta-analysis (calib_ests, pct_pval),
    to make effect size conversions (r_to_d, r_to_z, z_to_r, d_to_logRR), to compute and format inference in a meta-analysis (format_CI, format_stat, tau_CI), to scrape results from 
    existing meta-analyses for re-analysis (scrape_meta, parse_CI_string, ci_to_var).
Collection of functions to compute covariances for different effect sizes, data visualization, and single and multiple imputations for missing data. Effect sizes include correlation (r), mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD).
A compilation of functions to create visually appealing and information-rich 
    plots of meta-analytic data using 'ggplot2'. Currently allows to create forest plots, 
    funnel plots, and many of their variants, such as rainforest plots, thick forest plots, 
    additional evidence contour funnel plots, and sunset funnel plots. In addition, functionalities 
    for visual inference with the funnel plot in the context of meta-analysis are provided.
A tool for implementing so called 'deft' approach
    (see Fisher, David J., et al. (2017) <DOI:10.1136/bmj.j573>) and model
    visualization.
Palettes Inspired by Works at the Metropolitan Museum of Art in New York. Currently contains over 50 color schemes and checks for colorblind-friendliness of palettes. Colorblind accessibility checked using the '{colorblindcheck} package by Jakub Nowosad'<https://jakubnowosad.com/colorblindcheck/>.
Spatio-temporal geostatistical mapping of meteorological data. Global spatio-temporal models calculated using publicly available data are stored in package.
Energy-Vorticity theory (EVT) is the fundamental theory to describe processes in the atmosphere by combining conserved quantities from hydrodynamics and thermodynamics. The package 'meteoEVT' provides functions to calculate many energetic and vortical quantities, like potential vorticity, Bernoulli function and dynamic state index (DSI) [e.g. Weber and Nevir, 2008, <doi:10.1111/j.1600-0870.2007.00272.x>], for given gridded data, like ERA5 reanalyses. These quantities can be studied directly or can be used for many applications in meteorology, e.g., the objective identification of atmospheric fronts. For this purpose, separate function are provided that allow the detection of fronts based on the thermic front parameter [Hewson, 1998, <doi:10.1017/S1350482798000553>], the F diagnostic [Parfitt et al., 2017, <doi:10.1002/2017GL073662>] and the DSI [Mack et al., 2022, <arXiv:2208.11438>].
Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currently it works with GFS <https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs>, MeteoGalicia <https://www.meteogalicia.gal/web/modelos/threddsIndex.action>, NAM <https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-mesoscale-forecast-system-nam>, and RAP <https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/rapid-refresh-rap>.
Functions to estimate weather variables at any position of a landscape [De Caceres et al. (2018) <doi:10.1016/j.envsoft.2018.08.003>].
A set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modelling, particularly crop and crop disease modeling.
Provides a unified mixture-of-experts (ME) modeling and 
    estimation framework with several original and flexible ME models to 
    model, cluster and classify heterogeneous data in many complex 
    situations where the data are distributed according to non-normal, 
    possibly skewed distributions, and when they might be corrupted by 
    atypical observations. Mixtures-of-Experts models for complex and 
    non-normal distributions ('meteorits') are originally introduced and 
    written in 'Matlab' by Faicel Chamroukhi. The references are mainly the 
    following ones. The references are mainly the following ones.
    Chamroukhi F., Same A., Govaert, G. and Aknin P. (2009) <doi:10.1016/j.neunet.2009.06.040>.
    Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>.
    Chamroukhi F. (2015) <arXiv:1506.06707>.
    Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>.
    Chamroukhi F. (2016) <doi:10.1109/IJCNN.2016.7727580>.
    Chamroukhi F. (2016) <doi:10.1016/j.neunet.2016.03.002>.
    Chamroukhi F. (2017) <doi:10.1016/j.neucom.2017.05.044>.
Access to different Spanish meteorological stations data services and APIs (AEMET, SMC, MG, 
  Meteoclimatic...).
Fit and plot macroecological patterns predicted by the Maximum
    Entropy Theory of Ecology (METE).
Provides a function me.test() to test equality of distributions when observations are subject to measurement errors.
In the omics data association studies, it is common to conduct the p-value corrections to control the false significance. Among those p-value correction methods, E-value is recently studied based on V. Vovk and R. Wang (2021) <doi:10.1214/20-AOS2020>. This package provides e-value calculation for several types of omics data association studies. Currently, four data formats are supported: BiSeq, MDRfinder, methylKit and metilene data. The relevant references are listed below: Katja Hebestreit and Hans-Ulrich Klein (2022) <doi:10.18129/B9.bioc.BiSeq>; Altuna Akalin et.al (2012) <doi:10.18129/B9.bioc.methylKit>.
An adaptation of the multi-variable stochastic weather generator proposed in 'Rglimclim' to perform gap-filling and temporal extension at sub-daily resolution. Simulation is performed based on large scale variables and climatic observation data that could be generated from different gauged stations having geographical proximity. SWG relies on reanalyses. Multi-variable dependence is taking into account by using the decomposition of the product rule (in statistics) into conditional probabilities. See <https://hal.archives-ouvertes.fr/hal-02554676>.
Methods (standard and advanced) for analysis of agreement between measurement methods. These cover Bland-Altman plots, Deming regression, Lin's Total deviation index, and difference-on-average regression. See Carstensen B. (2010) "Comparing Clinical Measurement Methods: A Practical Guide (Statistics in Practice)" <doi:10.1002/9780470683019> for more information.
Identify and rank CpG DNA methylation conservation along the human 
    genome. Specifically it includes bootstrapping methods to provide ranking 
    which should adjust for the differences in length as without it short 
    regions tend to get higher conservation scores.
Implementation of the methodology from the paper entitled
    "Effective plots to assess bias and precision in method comparison studies"
    published in Statistical Methods in Medical Research, P. Taffé (2018) <doi:10.1177/0962280218759693>.
Meteorological Tools following the FAO56 irrigation paper of Allen et al. (1998) [1].
    Functions for calculating:
    reference evapotranspiration (ETref),
    extraterrestrial radiation (Ra),
    net radiation (Rn),
    saturation vapor pressure (satVP),
    global radiation (Rs),
    soil heat flux (G),
    daylight hours,
    and more.
    [1] Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9).
Provides a number of functions to facilitate the handling and production of reports using time series data.
    The package was developed to be understandable for beginners, so some functions aim to transform processes that would be
    complex into functions with a few lines. The main advantage of using the 'metools' package is the ease of producing reports and
    working with time series using a few lines of code, so the code is clean and easy to understand/maintain. 
    Learn more about the 'metools' at <https://metoolsr.wordpress.com>.
Split an untargeted metabolomics data set into a set of likely true 
    metabolites and a set of likely measurement artifacts. This process involves 
    comparing missing rates of pooled plasma samples and biological samples. The 
    functions assume a fixed injection order of samples where biological samples are 
    randomized and processed between intermittent pooled plasma samples. By comparing 
    patterns of missing data across injection order, metabolites that appear in blocks
    and are likely artifacts can be separated from metabolites that seem to have 
    random dispersion of missing data. The two main metrics used are: 1. the number of 
    consecutive blocks of samples with present data and 2. the correlation of missing rates 
    between biological samples and flanking pooled plasma samples.
Many useful functions and extensions for dealing
    with meteorological data in the tidy data framework. Extends 'ggplot2'
    for better plotting of scalar and vector fields and provides commonly
    used analysis methods in the atmospheric sciences.
A compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.
An implementation of evaluation metrics in R that are commonly
             used in supervised machine learning. It implements metrics for
             regression, time series, binary classification, classification,
             and information retrieval problems. It has zero dependencies and
             a consistent, simple interface for all functions.
Provides an 'htmlwidgets' interface to the
    'MetricsGraphics.js' ('D3'-based) charting library which is geared towards
    displaying time-series data. Chart types include line charts, scatterplots,
    histograms and rudimentary bar charts. Support for laying out multiple charts
    into a grid layout is also provided. All charts are interactive and many
    have an option for line, label and region annotations.
Provides weighted versions of several metrics, scoring
    functions and performance measures used in machine learning, including
    average unit deviances of the Bernoulli, Tweedie, Poisson, and Gamma
    distributions, see Jorgensen B. (1997, ISBN: 978-0412997112). The
    package also contains a weighted version of generalized R-squared, see
    e.g. Cohen, J. et al. (2002, ISBN: 978-0805822236). Furthermore,
    'dplyr' chains are supported.
Calculate different metrics
    based on aquatic macroinvertebrate density data (individuals per
    square meter) to assess water quality (Prat N et al. (2009) <http://www.ub.edu/riosandes/docs/MacroIndLatinAmcompag0908.pdf>).
The Washington Metropolitan Area Transit Authority
    is a government agency operating light rail and passenger buses in the
    Washington D.C. area. With a free developer account, access their
    'Metro Transparent Data Sets API' <https://developer.wmata.com/> to
    return data frames of transit data for easy analysis.
Provides classes and calculation and plotting functions 
 for metrology applications, including measurement uncertainty estimation
 and inter-laboratory metrology comparison studies.
Learning and using the Metropolis algorithm for
    Bayesian fitting of a generalized linear model. The package vignette
    includes examples of hand-coding a logistic model using several
    variants of the Metropolis algorithm. The package also contains R
    functions for simulating posterior distributions of Bayesian
    generalized linear model parameters using guided, adaptive,
    guided-adaptive and random walk Metropolis algorithms. The random walk
    Metropolis algorithm was originally described in Metropolis et al
    (1953); <doi:10.1063/1.1699114>.
Implementation of various statistical models for multivariate
    event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate
    cumulative incidence models <doi:10.1002/sim.6016>, and  bivariate random
    effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>.
    Also contains two-stage binomial modelling that can do pairwise odds-ratio
    dependence modelling based marginal logistic regression models. This is an
    alternative to the alternating logistic regression approach (ALR).
Provides a Shiny application to estimate the sample size required 
    for a metabolomic experiment to achieve a desired statistical power. 
    Estimation is possible with or without available data from a pilot study.
Provides an interface with the Meteo France Synop data API 
    (see <https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32> 
    for more information). 
    The Meteo France Synop data are made of meteorological data recorded 
    every three hours on 62 French meteorological stations.
Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.
Compute the average of a sequence of random vectors
  in a moving expanding window using a fixed amount of memory.
Fit flexible (excess) hazard regression models with the possibility of including non-proportional effects of covariables and of adding a random effect at the cluster level (corresponding to a shared frailty). A detailed description of the package functionalities is provided in Charvat and Belot (2021) <doi: 10.18637/jss.v098.i14>.
A color palette generator inspired by Mexican politics, with 
    colors ranging from red on the left to gray in the middle and green on the 
    right. Palette options range from only a few colors to several colors, but with
    discrete and continuous options to offer greatest flexibility to the user. 
    This package allows for a range of applications, from mapping brief discrete scales 
    (e.g., four colors for Morena, PRI, and PAN) to continuous interpolated arrays 
    including dozens of shades graded from red to green.
The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.
Multivariate functional principal component analysis via fast covariance estimation for
             multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) <doi: 10.1002/sta4.245>.
Performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions.
Computing functional traits-based distances between pairs of 
    species for species gathered in assemblages allowing to build several
    functional spaces. The package allows to compute functional diversity
    indices assessing the distribution of species (and of their dominance) in a
    given functional space for each assemblage and the overlap between
    assemblages in a given functional space, see: Chao et al. (2018)
    <doi:10.1002/ecm.1343>, Maire et al. (2015) <doi:10.1111/geb.12299>,
    Mouillot et al. (2013) <doi:10.1016/j.tree.2012.10.004>, Mouillot et al.
    (2014) <doi:10.1073/pnas.1317625111>, Ricotta and Szeidl (2009)
    <doi:10.1016/j.tpb.2009.10.001>. Graphical outputs are included.
    Visit the 'mFD' website for more information, documentation and examples.
Creates and manages a PostgreSQL database suitable for storing fisheries data
             and aggregating ready for use within a Gadget <https://gadget-framework.github.io/gadget2/> model.
             See <https://mareframe.github.io/mfdb/> for more information.
Contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), 
  MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale
  Multifractal Analysis (MMA). The MFDFA() function proposed in this package was 
  used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). 
  See references for more information. Interested users can find a parallel version of
  the MFDFA() function on GitHub.
Computes bounds for the median of the false discovery proportion (mFDP).
	These 50 percent confidence bounds for the FDP are simultaneously valid. 
	The method takes a vector of p-values as input. Also provides mFDP-adjusted p-values. 
	Can be used for flexible mFDP control.
Extracts meta-features from datasets to support the design of 
  recommendation systems based on Meta-Learning. The meta-features, also called 
  characterization measures, are able to characterize the complexity of datasets
  and to provide estimates of algorithm performance. The package contains not 
  only the standard characterization measures, but also more recent 
  characterization measures. By making available a large set of meta-feature 
  extraction functions, tasks like comprehensive data characterization, deep 
  data exploration and large number of Meta-Learning based data analysis can be
  performed. These concepts are described in the paper: Rivolli A., Garcia L., 
  Soares c., Vanschoren J. and Carvalho A. (2018) <arXiv:1808.10406>.
Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: Volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2020, <doi:10.1002/jae.2742>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.
The mFilter package implements several time series filters useful
        for smoothing and extracting trend and cyclical components of a
        time series. The routines are commonly used in economics and
        finance, however they should also be interest to other areas.
        Currently, Christiano-Fitzgerald, Baxter-King,
        Hodrick-Prescott, Butterworth, and trigonometric regression
        filters are included in the package.
A leadership-inference framework for multivariate time series. The framework for multiple-faction-leadership inference from coordinated activities or 'mFLICA' uses a notion of a leader as an individual who initiates collective patterns that everyone in a group follows. Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction. For each time step, the framework infers following relations between individual time series, then identifying a leader of each faction whom many individuals follow but it follows no one. A faction is defined as a group of individuals that everyone follows the same leader. 'mFLICA' reports following relations, leaders of factions, and members of each faction for each time step. Please see Chainarong Amornbunchornvej and Tanya Berger-Wolf (2018) <doi:10.1137/1.9781611975321.62> for methodology and Chainarong Amornbunchornvej (2021) <doi:10.1016/j.softx.2021.100781> for software when referring to this package in publications.
Calculate the maximal fat oxidation, the exercise intensity that elicits the 
              maximal fat oxidation and the SIN model to represent the fat oxidation kinetics. 
              Three variables can be obtained from the SIN model: dilatation, symmetry and translation. 
              Examples of these methods can be found in Montes de Oca et al (2021) <doi:10.1080/17461391.2020.1788650>
              and Chenevière et al. (2009) <doi:10.1249/MSS.0b013e31819e2f91>.
Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994.
Calculate a multivariate functional principal component analysis
    for data observed on different dimensional domains. The estimation algorithm
    relies on univariate basis expansions for each element of the multivariate
    functional data  (Happ & Greven, 2018) <doi:10.1080/01621459.2016.1273115>. 
    Multivariate and univariate functional data objects are
    represented by S4 classes for this type of data implemented in the package
    'funData'. For more details on the general concepts of both packages and a case 
    study, see Happ-Kurz (2020) <doi:10.18637/jss.v093.i05>.
Matrix-Based Flexible Project Planning. This package models, plans, and schedules flexible, such as agile, extreme, and hybrid project plans. The package contains project planning, scheduling, and risk assessment functions. Kosztyan (2022) <doi:10.1016/j.softx.2022.100973>.
Provides statistical tests and algorithms for the detection of change points in time series and point processes - particularly for changes in the mean in time series and for changes in the rate and in the variance in point processes. References - Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider (2014), A multiple filter test for the detection of rate changes in renewal processes with varying variance <doi:10.1214/14-AOAS782>. Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper, Gaby Schneider (2017), Multi-scale detection of variance changes in renewal processes in the presence of rate change points <doi:10.1111/jtsa.12254>. Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017), Multi-scale detection of rate changes in spike trains with weak dependencies <doi:10.1007/s10827-016-0635-3>. Michael Messer, Stefan Albert and Gaby Schneider (2018), The multiple filter test for change point detection in time series <doi:10.1007/s00184-018-0672-1>. Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+) Peak detection in time series.
Estimates probit, logit, Poisson, negative binomial, 
  and beta regression models, returning their marginal effects, odds ratios, 
  or incidence rate ratios as an output.
  Greene (2008, pp. 780-7) provides a textbook introduction to this topic.
Procedures to simulate, estimate and diagnose MGARCH
    processes of BEKK and multivariate GJR (bivariate asymmetric GARCH
    model) specification.
Compute the multiple Grubbs-Beck low-outlier test on positively distributed
 data and utilities for noninterpretive U.S. Geological Survey annual peak-streamflow
 data processing discussed in Cohn et al. (2013) <doi:10.1002/wrcr.20392> and
 England et al. (2017) <doi:10.3133/tm4B5>.
Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) <DOI:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.
Generalized additive (mixed) models, some of their extensions and 
             other generalized ridge regression with multiple smoothing 
             parameter estimation by (Restricted) Marginal Likelihood, 
             Generalized Cross Validation and similar, or using iterated 
             nested Laplace approximation for fully Bayesian inference. See 
             Wood (2017) <doi:10.1201/9781315370279> for an overview. 
             Includes a gam() function, a wide variety of smoothers, 'JAGS' 
             support and distributions beyond the exponential family.
Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.
Provides a model designed to be a reliable testbed where various gene 
    drive interventions for mosquito-borne diseases control. It is being developed to 
    accommodate the use of various mosquito-specific gene drive systems within a 
    population dynamics framework that allows migration of individuals between patches 
    in landscape. Previous work developing the population dynamics can be found in Deredec et al. 
    (2001) <doi:10.1073/pnas.1110717108> and Hancock & Godfray (2007) <doi:10.1186/1475-2875-6-98>, 
    and extensions to accommodate CRISPR homing dynamics in Marshall et al. (2017) 
    <doi:10.1038/s41598-017-02744-7>.
A simulation modeling framework which significantly extends capabilities from the
    'MGDrivE' simulation package via a new mathematical and computational framework based on stochastic Petri nets.
    For more information about 'MGDrivE', see our publication: <https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13318>.
    Some of the notable capabilities of 'MGDrivE2' include: incorporation of human populations,
    epidemiological dynamics, time-varying parameters, and a continuous-time simulation
    framework with various sampling algorithms for both deterministic and stochastic interpretations.
    'MGDrivE2' relies on the genetic inheritance structures provided in package 'MGDrivE', so we
    suggest installing that package initially.
Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates. 
 The naive analysis which is solely based on the observed data without adjustment may lead to bias.
 The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand. 
 The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given
 validation set. This package is an implementation 
 of Chen (2013) <doi:10.1002/bimj.201200195>.
Distance between multivariate generalised Gaussian distributions, as presented by N. Bouhlel and A. Dziri (2019) <doi:10.1109/LSP.2019.2915000>. Manipulation of multivariate generalised Gaussian distributions (methods presented by Gomez, Gomez-Villegas and Marin (1998) <doi:10.1080/03610929808832115> and Pascal, Bombrun, Tourneret and Berthoumieu (2013) <doi:10.1109/TSP.2013.2282909>).
An aggressive dimensionality reduction and network estimation
    technique for a high-dimensional Gaussian graphical model (GGM). Please
    refer to: Efficient Dimensionality Reduction for High-Dimensional Network
    Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of
    The 31st International Conference on Machine Learning, 2014, p. 1953--1961.
Inference of Multiscale graphical models with neighborhood
    selection approach.  The method is based on solving a convex
    optimization problem combining a Lasso and fused-group Lasso
    penalties.  This allows to infer simultaneously a conditional
    independence graph and a clustering partition. The optimization is
    based on the Continuation with Nesterov smoothing in a
    Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018)
    <doi:10.1109/TMI.2018.2829802> implemented in python.
Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data.  Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.
Tools for univariate and multivariate generalized linear models with model averaging and null model technique.
Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.
Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "MGMM: an R package for fitting Gaussian Mixture Models on Incomplete Data." <doi:10.1101/2019.12.20.884551>.
A glycolipid mass spectrometry technology has the potential to accurately identify individual bacterial species from polymicrobial samples. To develop bacterial identification algorithms (e.g. machine learning) using this glycolipid technology, it is necessary to generate a large number of various in-silico polymicrobial mass spectra that are similar to real mass spectra. 'MGMS2' (Membrane Glycolipid Mass Spectrum Simulator) generates such in-silico mass spectra, considering errors in m/z (mass-to-charge ratio) and variances of intensity values, occasions of missing signature ions, and noise peaks. It estimates summary statistics of monomicrobial mass spectra for each strain or species and simulates polymicrobial glycolipid mass spectra using the summary statistics of monomicrobial mass spectra. References: Ryu, S.Y., Wendt, G.A., Chandler, C.E., Ernst, R.K. and Goodlett, D.R. (2019) <doi:10.1021/acs.analchem.9b03340> "Model-based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids." Gibb, S. and Strimmer, K. (2012) <doi:10.1093/bioinformatics/bts447> "MALDIquant: a versatile R package for the analysis of mass spectrometry data."
Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2016), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA <doi:10.1080/01621459.2015.1034318>.
Data smoothing with penalized splines is a popular method and is well established for one- or two-dimensional covariates. The extension to multiple covariates is straightforward but suffers from exponentially increasing memory requirements and computational complexity. This toolbox provides a matrix-free implementation of a conjugate gradient (CG) method for the regularized least squares problem resulting from tensor product B-spline smoothing with multivariate and scattered data. It further provides matrix-free preconditioned versions of the CG-algorithm where the user can choose between a simpler diagonal preconditioner and an advanced geometric multigrid preconditioner. The main advantage is that all algorithms are performed matrix-free and therefore require only a small amount of memory. For further detail see Siebenborn & Wagner (2021).
Designed to enable simultaneous substitution in strings in a safe fashion.
    Safe means it does not rely on placeholders (which can cause errors in same length matches).
Functions for computing (Mixed) Geographycally Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.
Nonparametric survival function estimates and semiparametric
    regression for the multivariate failure time data with right-censoring.
    For nonparametric survival function estimates, the Volterra, Dabrowska,
    and Prentice-Cai estimates for bivariate failure time data may be
    computed as well as the Dabrowska estimate for the trivariate failure
    time data. Bivariate marginal hazard rate regression can be fitted for
    the bivariate failure time data. Functions are also provided to compute
    (bootstrap) confidence intervals and plot the estimates of the bivariate
    survival function. For details, see "The Statistical Analysis of
    Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice,
    R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press.
MHCnuggets (<https://github.com/KarchinLab/mhcnuggets>) is a Python
    tool to predict MHC class I and MHC class II epitopes.
    This package allows one to call MHCnuggets from R.
Three main functions about analyzing massive data (missing observations are allowed) considered from multiple layers of categories.
Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa.
An implementation of the multilevel (also known as mixed or random 
    effects) hidden Markov model using Bayesian estimation in R. The multilevel 
    hidden Markov model (HMM) is a generalization of the well-known hidden
    Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The 
    multilevel HMM is tailored to accommodate (intense) longitudinal data of 
    multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. 
    <doi:10.1080/00273171.2017.1370364>. Using a multilevel framework, we allow 
    for heterogeneity in the model parameters (transition probability matrix and 
    conditional distribution), while estimating one overall HMM. The model can 
    be fitted on multivariate data with a categorical distribution, and include 
    individual level covariates (allowing for e.g., group comparisons on model 
    parameters). Parameters are estimated using Bayesian estimation utilizing 
    the forward-backward recursion within a hybrid Metropolis within Gibbs 
    sampler. The package also includes various visualization options, a function 
    to simulate data, and a function to obtain the most likely hidden state 
    sequence for each individual using the Viterbi algorithm.
Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences.  Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.
A framework for multiple hypothesis testing based on distribution
    of p values. It is well known that the p values come from different
    distribution for null and alternatives, in this package we provide
    functions to detect that change. We provide a method for using the change
    in distribution of p values as a way to detect the true signals in the
    data.
A comprehensive tool for almost all existing multiple testing
    methods for discrete data. The package also provides some novel multiple testing
    procedures controlling FWER/FDR for discrete data. Given discrete p-values
    and their domains, the [method].p.adjust function returns adjusted p-values,
    which can be used to compare with the nominal significant level alpha and make
    decisions. For users' convenience, the functions also provide the output option 
    for printing decision rules.
A Comprehensive tool for almost all existing multiple testing
    methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea.
Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.
Estimation of models with zero left-censored variables.
             Null values may be caused by a selection process
	     Cragg (1971) <doi:10.2307/1909582>, insufficient resources
	     Tobin (1958) <doi:10.2307/1907382> or infrequency of purchase
	     Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>.
The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
A framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) <arxiv:2108.07086>. It is dedicated to dealing with multiple imputation for proteomics.
Tools for training, selecting, and evaluating maximum entropy
    (and standard logistic regression) distribution models. This package 
    provides tools for user-controlled transformation of explanatory variables, 
    selection of variables by nested model comparison, and flexible model 
    evaluation and projection. It follows principles based on the maximum-
    likelihood interpretation of maximum entropy modeling, and uses infinitely-
    weighted logistic regression for model fitting.
'Mica' is a server application used to create data web portals for 
    large-scale epidemiological studies or multiple-study consortia. 'Mica' helps
    studies to provide scientifically robust data visibility and web presence 
    without significant information technology effort. 'Mica' provides a 
    structured description of consortia, studies, annotated and searchable data
    dictionaries, and data access request management. This 'Mica' client allows
    to perform data extraction for reporting purposes.
Modified functions of the package 'pcalg' and some additional functions
            to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based 
            causal discovery in incomplete and multiply imputed datasets.
            Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) <doi:10.1111/rssa.12565>;
            Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395>; Witte J, Foraita R, Didelez V (2022) <doi:10.1002/sim.9535>.
Multiple imputation using Fully Conditional Specification (FCS)
    implemented by the MICE algorithm as described in Van Buuren and
    Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has
    its own imputation model. Built-in imputation models are provided for
    continuous data (predictive mean matching, normal), binary data (logistic
    regression), unordered categorical data (polytomous logistic regression)
    and ordered categorical data (proportional odds). MICE can also impute
    continuous two-level data (normal model, pan, second-level variables).
    Passive imputation can be used to maintain consistency between variables.
    Various diagnostic plots are available to inspect the quality of the
    imputations.
Contains functions for multiple imputation which
    complements existing functionality in R.
    In particular, several imputation methods for the
    mice package (van Buuren & Groothuis-Oudshoorn, 2011,
    <doi:10.18637/jss.v045.i03>) are implemented.
    Main features of the miceadds package include
    plausible value imputation (Mislevy, 1991,
    <doi:10.1007/BF02294457>), multilevel imputation for
    variables at any level or with any number of hierarchical
    and non-hierarchical levels (Grund, Luedtke & Robitzsch,
    2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, 
    Ch.7, <doi:10.1201/9780429492259>), imputation using 
    partial least squares (PLS) for high dimensional 
    predictors (Robitzsch, Pham & Yanagida, 2016), 
    nested multiple imputation (Rubin, 2003, 
    <doi:10.1111/1467-9574.00217>), substantive model
    compatible imputation (Bartlett et al., 2015,
    <doi:10.1177/0962280214521348>), and features
    for the generation of synthetic datasets
    (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>;
    Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).
Statistical Analyses and Pooling after Multiple Imputation. A large variety 
   of repeated statistical analysis can be performed and finally pooled. Statistical analysis 
   that are available are, among others, Levene's test, Odds and Risk Ratios, One sample 
   proportions, difference between proportions and linear and logistic regression models. 
   Functions can also be used in combination with the Pipe operator. 
   More and more statistical analyses and pooling functions will be added over time.
   Heymans (2007) <doi:10.1186/1471-2288-7-33>.
   Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
	 Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
	 Marshall (2009) <doi:10.1186/1471-2288-9-57>.
	 Sidi (2021) <doi:10.1080/00031305.2021.1898468>.
	 Lott (2018) <doi:10.1080/00031305.2018.1473796>.
	 Grund (2021) <doi:10.31234/osf.io/d459g>.
Various tools for microeconomic analysis and microeconomic modelling,
   e.g. estimating quadratic, Cobb-Douglas and Translog functions,
   calculating partial derivatives and elasticities of these functions,
   and calculating Hessian matrices, checking curvature
   and preparing restrictions for imposing monotonicity of Translog functions.
Functions and tools
   for analysing consumer demand
   with the Almost Ideal Demand System (AIDS)
   suggested by Deaton and Muellbauer (1980).
Tools for econometric analysis and economic modelling
   with the traditional two-input Constant Elasticity of Substitution (CES) function
   and with nested CES functions with three and four inputs.
   The econometric estimation can be done by the Kmenta approximation,
   or non-linear least-squares
   using various gradient-based or global optimisation algorithms.
   Some of these algorithms can constrain the parameters to certain ranges,
   e.g. economically meaningful values.
   Furthermore, the non-linear least-squares estimation
   can be combined with a grid-search for the rho-parameter(s).
Tools for calculating Laspeyres, Paasche, and Fisher
  price and quantity indices.
Tools for econometric production analysis
   with the Symmetric Normalized Quadratic (SNQ) profit function,
   e.g. estimation, imposing convexity in prices,
   and calculating elasticities and shadow prices.
Fast imputations under the object-oriented programming paradigm. 	
  Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'.
  The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used.
  A single evaluation of a quantitative model for the multiple imputations is another major enhancement.
  A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.
Addons for the 'mice' package to perform multiple imputation using chained equations with two-level data. Includes imputation methods dedicated to sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2018) <doi:10.1214/18-STS646>, the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation and overimputation for 'mice'.
Multiple Imputation has been shown to 
  be a flexible method to impute missing values by 
  Van Buuren (2007) <doi:10.1177/0962280206074463>. 
  Expanding on this, random forests have been shown 
  to be an accurate model by Stekhoven and Buhlmann 
  <arXiv:1105.0828> to impute missing values in datasets. 
  They have the added benefits of returning out of bag 
  error and variable importance estimates, as well as 
  being simple to run in parallel.
Michel Rodange was a Luxembourguish writer and poet who lived in the 19th century. 
    His most notable work is Rodange (1872, ISBN:1166177424), ("Renert oder de Fuuß am Frack an a Ma'nsgrëßt"),
    but he also wrote many more works, including Rodange, Tockert (1928) <https://www.autorenlexikon.lu/page/document/361/3614/1/FRE/index.html> 
    ("D'Léierchen - Dem Léiweckerche säi Lidd") and Rodange, Welter (1929) <https://www.autorenlexikon.lu/page/document/361/3615/1/FRE/index.html>
    ("Dem Grow Sigfrid seng Goldkuommer"). This package contains three datasets, each made from the
    plain text versions of his works available on 
    <https://data.public.lu/fr/datasets/the-works-in-luxembourguish-of-michel-rodange/>.
Implementation of a framework for cluster analysis with selection of the
    final number of clusters and an optional variable selection procedure. The package
    is designed to integrate the results of multiple imputed datasets while accounting
    for the uncertainty that the imputations introduce in the final results. In addition,
    the package can also be used for a cluster analysis of the complete cases of a single
    dataset. The package also includes specific methods to summarize and plot the results.
    The methods are described in Basagana et al. (2013) <doi:10.1093/aje/kws289>.
A procedure for comparing multivariate samples associated with
    different groups. It uses principal component analysis to convert
    multivariate observations into a set of linearly uncorrelated statistical
    measures, which are then compared using a number of statistical methods. The
    procedure is independent of the distributional properties of samples and
    automatically selects features that best explain their differences, avoiding
    manual selection of specific points or summary statistics. It is appropriate
    for comparing samples of time series, images, spectrometric measures or
    similar multivariate observations.
Fits Semiparametric Promotion Time Cure Models, taking into account (using a 
			 corrected score approach or the SIMEX algorithm) or not the measurement error
			 in the covariates, using a backfitting approach to maximize the likelihood.
A nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (<DOI:10.1007/978-3-642-12538-6_6>, <DOI:10.1109/CINTI.2014.7028669>).
Provides infrastructure to accurately measure and compare
        the execution time of R expressions.
Provides functions to enhance the available 
            statistical analysis procedures in R by providing simple functions to 
            analysis and visualize the 16S rRNA data.Here we present a tutorial 
            with minimum working examples to demonstrate usage and dependencies.
A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects (Zhou et al. (2021)<arXiv:2104.00242>).  The methods can be applied to the analysis of other (high-dimensional) compositional data arising from sequencing experiments.
Functions for assigning 16S sequence data to a
    taxonomic level in the tree-of-life for prokaryotes.
The consensus taxonomy for prokaryotes is a set of data-sets for
    best possible taxonomic classification based on 16S rRNA sequence data.
Provides utilities for reading and processing microdata from Spanish official statistics with R.
A series of statistical and plotting approaches in microbial community ecology based on the R6 class. The classes are designed for data preprocessing, taxa abundance plotting, alpha diversity analysis, beta diversity analysis, differential abundance test, null model analysis, network analysis, machine learning, environmental data analysis and functional analysis.
A downstream bioinformatics tool to construct and assist 
    curation of microhaplotypes from short read sequences.
Most multilevel methodologies can only model macro-micro
    multilevel situations in an unbiased way, wherein group-level predictors
    (e.g., city temperature) are used to predict an individual-level
    outcome variable (e.g., citizen personality). In contrast,
    this R package enables researchers to model micro-macro situations, wherein
    individual-level (micro) predictors (and other group-level predictors) are
    used to predict a group-level (macro) outcome variable in an unbiased way.
This group of functions simplifies the creation of linked micromap
    plots.
Provides the users with the ability to quickly create
     linked micromap plots for a collection of geographic areas.
     Linked micromap plots are visualizations of geo-referenced data
     that link statistical graphics to an organized series of small
     maps or graphic images. The Help description contains examples
     of how to use the 'micromapST' function. Contained in this
     package are border group datasets to support creating linked
     micromap plots for the 50 U.S. states and District of Columbia
     (51 areas), the U. S. 20 Seer Registries, the 105 counties in
     the state of Kansas, the 62 counties of New York, the 24
     counties of Maryland, the 29 counties of Utah, the 32
     administrative areas in China, the 218 administrative areas in
     the UK and Ireland (for testing only), the 25 districts in the
     city of Seoul South Korea, and the 52 counties on the Africa
     continent.
     A border group dataset contains the boundaries related to the
     data level areas, a second layer boundaries, a top or third
     layer boundary, a parameter list of run options, and a cross
     indexing table between area names, abbreviations, numeric
     identification and alias matching strings for the specific
     geographic area.  By specifying a border group, the package
     create linked micromap plots for any geographic region.  The
     user can create and provide their own border group dataset for
     any area beyond the areas contained within the package.
     References: Carr and Pickle, Chapman and Hall/CRC, Visualizing
     Data Patterns with Micromaps, CRC Press, 2010. Pickle, Pearson,
     and Carr (2015), micromapST: Exploring and Communicating
     Geospatial Patterns in US State Data., Journal of Statistical
     Software, 63(3), 1-25., <https://www.jstatsoft.org/v63/i03/>.
     Copyrighted 2013, 2014, 2015, 2016, and 2022 by Carr, Pearson
     and Pickle.
Provides a framework based on S3 dispatch for constructing models
  of mosquito-borne pathogen transmission which are constructed from submodels of various
  components (i.e. immature and adult mosquitoes, human populations). A consistent mathematical
  expression for the distribution of bites on hosts means that different models
  (stochastic, deterministic, etc.) can be coherently incorporated and updated
  over a discrete time step.
Measures niche breadth and overlap of microbial taxa from large matrices.
			Niche breadth measurements include Levins' niche breadth (Bn) index, Hurlbert's Bn 
			and Feinsinger's proportional similarity (PS) index. (Feinsinger, P., Spears, E.E.,
			Poole, R.W. (1981) <doi:10.2307/1936664>). Niche overlap measurements include 
			Levin's Overlap (Ludwig, J.A. and Reynolds, J.F. (1988, ISBN:0471832359)) and a Jaccard 
			similarity index of Feinsinger's PS values between taxa pairs, as Proportional Overlap.
A collection of functions for computations and visualizations of microbial pan-genomes.
The microplot function writes a set of R graphics files to be used as
  microplots (sparklines) in tables in either 'LaTeX', 'HTML', 'Word',
  or 'Excel' files.  For 'LaTeX', we provide methods for the
  Hmisc::latex() generic function to construct 'latex' tabular
  environments which include the graphs.  These can be used directly
  with the operating system 'pdflatex' or 'latex' command, or by using
  one of 'Sweave', 'knitr', 'rmarkdown', or 'Emacs org-mode' as an
  intermediary.  For 'MS Word', the msWord() function uses the
  'flextable' package to construct 'Word' tables which include the
  graphs.  There are several distinct approaches for constructing HTML
  files.  The simplest is to use the msWord() function with argument
  filetype="html".  Alternatively, use either 'Emacs org-mode' or the
  htmlTable::htmlTable() function to construct an 'HTML' file
  containing tables which include the graphs.  See the documentation
  for our as.htmlimg() function.  For 'Excel' use on 'Windows', the
  file examples/irisExcel.xls includes 'VBA' code which brings the
  individual panels into individual cells in the spreadsheet.
  Examples in the examples and demo subdirectories are shown with
  'lattice' graphics, 'ggplot2' graphics, and 'base' graphics.
  Examples for 'LaTeX' include 'Sweave' (both 'LaTeX'-style and
  'Noweb'-style), 'knitr', 'emacs org-mode', and 'rmarkdown' input
  files and their 'pdf' output files.  Examples for 'HTML' include
  'org-mode' and 'Rmd' input files and their webarchive 'HTML' output
  files.  In addition, the as.orgtable() function can display a
  data.frame in an 'org-mode' document.  The examples for 'MS Word'
  (with either filetype="docx" or filetype="html") work with all
  operating systems.  The package does not require the installation of
  'LaTeX' or 'MS Word' to be able to write '.tex' or '.docx' files.
Modelling interacting microbial populations - example applications
    include human gut microbiota, rumen microbiota and phytoplankton. Solves a
    system of ordinary differential equations to simulate microbial growth and
    resource uptake over time. This version contains network visualisation functions.
Find optimal microsampling designs for non-compartmental pharacokinetic analysis using a  general simulation methodology:
  Algorithm III of Barnett, Helen, Helena Geys, Tom Jacobs, and Thomas Jaki. (2017) "Optimal Designs for Non-Compartmental
  Analysis of Pharmacokinetic Studies. (currently unpublished)"
  This methodology consist of (1) specifying a pharmacokinetic model
  including variability among animals; (2) generating possible sampling times; (3)
  evaluating performance of each time point choice on simulated data; (4)
  generating possible schemes given a time point choice and additional constraints
  and finally (5) evaluating scheme performance on simulated data. The default
  settings differ from the article of Barnett and others, in the default pharmacokinetic model used and
  the parameterization of variability among animals. Details can be found in the package vignette. A 'shiny'
  web application is included, which guides users from model parametrization to
  optimal microsampling scheme.
Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data.
'Microservice' architectural style is an approach to
    developing a single application as a suite of small services, each
    running in its own process and communicating with lightweight
    mechanisms, often an 'HTTP' resource 'API'. These services are built
    around business capabilities and independently deployable by fully
    automated deployment machinery. There is a bare minimum of centralized
    management of these services, which may be written in different
    programming languages and use different data storage technologies.
Discrete event simulation using both R and C++ (Karlsson et al 2016; <doi:10.1109/eScience.2016.7870915>). The C++ code is adapted from the SSIM library <https://www.inf.usi.ch/carzaniga/ssim/>, allowing for event-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis.
An interface to the 'Microsoft 365' (formerly known as 'Office 365') suite of cloud services, building on the framework supplied by the 'AzureGraph' package. Enables access from R to data stored in 'Teams', 'SharePoint Online' and 'OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured 'Outlook' email client, with the ability to send emails and manage emails and mail folders.
The toolkit 'µSTASIS' has been developed for the stability analysis of microbiota in a temporal framework by leveraging on iterative clustering. Concretely, the core function uses Hartigan-Wong k-means algorithm as many times as possible for stressing out paired samples from the same individuals to test if they remain together for multiple numbers of clusters over a whole data set of individuals. Moreover, the package includes multiple functions to subset samples from paired times, validate the results or visualize the output.
A generalization of the 'Synth' package that is
    designed for data at a more granular level (e.g., micro-level).
    Provides functions to construct weights (including propensity
    score-type weights) and run analyses for synthetic control methods
    with micro- and meso-level data; see Robbins, Saunders, and Kilmer
    (2017) <doi:10.1080/01621459.2016.1213634> and Robbins and Davenport
    (2021) <doi:10.18637/jss.v097.i02>.
This entry-level toolkit allows performing continuous-time microsimulation for a wide range of life science (demography, social sciences, epidemiology) applications. Individual life-courses are specified by a continuous-time multi-state model.
Providing C implementation for the computing of monotonic spline bases, including M-splines, I-splines, and C-splines, denoted by MIC splines. The definitions of the spline bases are described in Meyer (2008) <doi: 10.1214/08-AOAS167>. The package also provides the computing of constrained least-squares estimates when a subset of or all of the regression coefficients are constrained to be non-negative.
Set of functions designed to simplify transcriptome analysis and identification of marker molecules using microarrays data. The package includes a set of functions that allows performing full pipeline of analysis including data normalization, summarisation, binary classification, FDR (False Discovery Rate) multiple comparison and the definition of potential biological markers.
Contains functions for converting existing HTML/JavaScript
   source into equivalent 'shiny' functions. Bootstraps the process of making
   new 'shiny' functions by allowing us to turn HTML snippets directly into 
   R functions.
Implementation of Bayesian drug-combination platform design. More and more immuno-oncology drug combinations make the traditional two-arm phase II trials inefficient, which stimulate the emerge of platform trials. In the case of multiple trial objectives such as candidates screening and subgroup analysis, we propose an information borrowing drug-combination Bayesian design for platform trials with subgroup exploration. MIDAS-2 consists of one control arm and several experimental agents. We use Bayesian spike and slab prior to identify factors that should be included in regression model and borrow information between combinations in the existence of subgroup interaction. Promising drug combinations are allowed to graduated early to move to next stage and new combination strategies can be added accordingly. Catch-up rule, curtail rule and early stopping rules are also applied to accelerate the trial process.
The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) <doi:10.1080/07350015.2021.1899933>. The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.
Methods and tools for mixed frequency time series data analysis.
    Allows estimation, model selection and forecasting for MIDAS regressions.
Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's  MIDAS.
This is a wrapper around the C++ implementation of 'MIDAS' (Bhatia et al., 2020) <https://www.comp.nus.edu.sg/~sbhatia/assets/pdf/midas.pdf> by Siddharth Bhatia for graph like data.
Implementation of the mid-n algorithms presented in 
             Wellek S (2015) <DOI:10.1111/stan.12063> Statistica Neerlandica 69, 358-373 for exact 
             sample size calculation for superiority trials with binary outcome.
Apply tests of multiple comparisons based
    on studentized 'midrange' and 'range' distributions. 
    The tests are: Tukey Midrange ('TM' test),
    Student-Newman-Keuls Midrange ('SNKM' test), 
    Means Grouping Midrange ('MGM' test) and 
    Means Grouping Range ('MGR' test). The first two tests were published by 
    Batista and Ferreira (2020) <doi:10.1590/1413-7054202044008020>. 
    The last two are being published.
Evolutionary black box optimization algorithms building on the
  'bbotk' package. 'miesmuschel' offers both ready-to-use optimization
  algorithms, as well as their fundamental building blocks that can be used to
  manually construct specialized optimization loops. The Mixed Integer Evolution
  Strategies as described by Li et al. (2013) <doi:10.1162/EVCO_a_00059> can be
  implemented, as well as the multi-objective optimization algorithms NSGA-II
  by Deb, Pratap, Agarwal, and Meyarivan (2002) <doi:10.1109/4235.996017>.
Impute the covariance matrix of incomplete data so that factor 
  analysis can be performed. Imputations are made using multiple imputation 
  by Multivariate Imputation with Chained Equations (MICE) and combined with 
  Rubin's rules. Parametric Fieller confidence intervals and nonparametric
  bootstrap confidence intervals can be obtained for the variance explained by 
  different numbers of principal components. The method is described in 
  Nassiri et al. (2018) <doi:10.3758/s13428-017-1013-4>.
Tools for estimating, measuring and working with migration data.
A set of tools for analysing multimodal networks.
   All functions operate with matrices, edge lists, 
   and 'igraph', 'network', and 'tidygraph' objects,
   and on one-mode, two-mode (bipartite), and sometimes three-mode networks.
   It includes functions for measuring 
   centrality, centralization, cohesion, closure, and constraint,
   as well as for network block-modelling and regression.
   The package is released as a complement to 
   'Multimodal Political Networks' (2021, ISBN:9781108985000),
   and includes various datasets used in the book in addition to other network data.
Tools to help convert credit risk data at two time points 
    into traditional credit state migration (aka, "transition") matrices.
    At a higher level, 'migrate' is intended to help an analyst understand 
    how risk moved in their credit portfolio over a time interval. 
    References to this methodology include: 
    1. Schuermann, T. (2008) <doi:10.1002/9780470061596.risk0409>.
    2. Perederiy, V. (2017) <arXiv:1708.00062>.
Calculate various indices, like Crude Migration Rate,
    different Gini indices or the Coefficient of Variation among others, to
    show the (un)equality of migration.
This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.
We report an information-theoretic method which learns a large
    class of causal or non-causal graphical models from purely observational
    data, while including the effects of unobserved latent variables, commonly
    found in many datasets. Starting from a complete graph, the method
    iteratively removes dispensable edges, by uncovering significant information
    contributions from indirect paths, and assesses edge-specific confidences
    from randomization of available data. The remaining edges are then oriented
    based on the signature of causality in observational data. This approach can
    be applied on a wide range of datasets and provide new biological insights
    on regulatory networks from single cell expression data, genomic alterations
    during tumor development and co-evolving residues in protein structures.
    For more information you can refer to:
    Cabeli et al. PLoS Comp. Bio. 2020 <doi:10.1371/journal.pcbi.1007866>,
    Verny et al. PLoS Comp. Bio. 2017 <doi:10.1371/journal.pcbi.1005662>.
Contains functions for data analysis of Repeated measurement continuous,categorical data using MCMC. Data may contain missing value in response and covariates. Mean Score Method and Inverse Probability Weighted method for parameter estimation when there is missing value in covariates are also included. 
             Reference for mean score method, inverse probability weighted method is
             Wang et al(2007)<doi:10.1093/biostatistics/kxl024>.
Functions for estimating structural equation models using 
    instrumental variables.
An interface to build machine learning models for
    classification and regression problems. 'mikropml' implements the ML
    pipeline described by Topçuoğlu et al. (2020)
    <doi:10.1128/mBio.00434-20> with reasonable default options for data
    preprocessing, hyperparameter tuning, cross-validation, testing, model
    evaluation, and interpretation steps.  See the website
    <https://www.schlosslab.org/mikropml/> for more information,
    documentation, and examples.
Weakly supervised (WS), multiple instance (MI) data lives in
    numerous interesting applications such as drug discovery, object
    detection, and tumor prediction on whole slide images. The 'mildsvm'
    package provides an easy way to learn from this data by training
    Support Vector Machine (SVM)-based classifiers. It also contains
    helpful functions for building and printing multiple instance data
    frames. The core methods from 'mildsvm' come from the following
    references: Kent and Yu (2022) <arXiv:2206.14704>; Xiao, Liu, and Hao
    (2018) <doi:10.1109/TNNLS.2017.2766164>; Muandet et al. (2012)
    <https://proceedings.neurips.cc/paper/2012/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper.pdf>;
    Chu and Keerthi (2007) <doi:10.1162/neco.2007.19.3.792>; and Andrews
    et al. (2003)
    <https://papers.nips.cc/paper/2232-support-vector-machines-for-multiple-instance-learning.pdf>.
    Many functions use the 'Gurobi' optimization back-end to improve the
    optimization problem speed; the 'gurobi' R package and associated
    software can be downloaded from <https://www.gurobi.com> after
    obtaining a license.
A variety of association tests for microbiome data analysis including Quasi-Conditional Association Tests (QCAT) described in Tang Z.-Z. et al.(2017) <doi:10.1093/bioinformatics/btw804> and Zero-Inflated Generalized Dirichlet Multinomial (ZIGDM) tests described in Tang Z.-Z. & Chen G. (2017, submitted).
Fast approximate methods for mixed logistic regression in genome-wide analysis studies (GWAS). Milet et al 2020 <doi:10.1101/2020.01.17.910109>.
The multiple instance data set consists of many independent
    subjects (called bags) and each subject is composed of several components
    (called instances). The outcomes of such data set are binary or categorical responses,
    and, we can only observe the subject-level outcomes. For example, in manufacturing
    processes, a subject is labeled as "defective" if at least one of its own
    components is defective, and otherwise, is labeled as "non-defective". The
    'milr' package focuses on the predictive model for the multiple instance
    data set with binary outcomes and performs the maximum likelihood estimation
    with the Expectation-Maximization algorithm under the framework of logistic
    regression. Moreover, the LASSO penalty is attached to the likelihood function
    for simultaneous parameter estimation and variable selection.
Guesses the MIME type from a filename extension using the data
    derived from /etc/mime.types in UNIX-type systems.
Generalized low-rank models for mixed and incomplete data frames. The main function may be used for dimensionality reduction of imputation of numeric, binary and count data (simultaneously). Main effects such as column means, group effects, or effects of row-column side information (e.g. user/item attributes in recommendation system) may also be modelled in addition to the low-rank model. Geneviève Robin, Olga Klopp, Julie Josse, Éric Moulines, Robert Tibshirani (2018) <arXiv:1806.09734>.
Provides an intuitive framework for ad-hoc statistical analysis of 1H-NMR metabolomics by Nightingale Health. It allows to easily explore new metabolomics measurements assayed by Nightingale Health, comparing the distributions with a large Consortium (BBMRI-nl); project previously published metabolic scores [<doi:10.1016/j.ebiom.2021.103764>, <doi:10.1161/CIRCGEN.119.002610>, <doi:10.1038/s41467-019-11311-9>, <doi:10.7554/eLife.63033>, <doi:10.1161/CIRCULATIONAHA.114.013116>, <doi:10.1007/s00125-019-05001-w>]; and calibrate the metabolic surrogate values to a desired dataset.
The MIMS-unit algorithm is developed to compute Monitor Independent
    Movement Summary Unit, a measurement to summarize raw accelerometer data 
    while ensuring harmonized results across different devices. It also includes
    scripts to reproduce results in the related publication 
    (John, D., Tang. Q., Albinali, F. and Intille, S. (2019) <doi:10.1123/jmpb.2018-0068>).
Calculate dissolved gas concentrations from raw MIMS 
	(Membrane Inlet Mass Spectrometer) signal data. Use mimsy() on 
	a formatted CSV file to return dissolved gas concentrations 
	(mg and microMole) of N2, O2, Ar based on 
	gas solubility at temperature, pressure, and salinity. See references 
	Benson and Krause (1984) <DOI:10.4319/lo.1992.37.6.1307>, Garcia and 
	Gordon (1992) <DOI:10.4319/lo.1984.29.3.0620>, Stull (1947) 
	<DOI:10.1021/ie50448a022>, and Hamme and Emerson (2004) 
	<DOI:10.1016/j.dsr.2004.06.009> for more information. Easily save the 
	output to a nicely-formatted multi-tab 'Excel' workbook with mimsy.save(). 
	Supports dual-temperature standard calibration for dual-bath MIMS setups.
A versatile tool that aims at (1) defining the minimum background extent necessary to fit Species Distribution Models reliable enough to extract ecologically relevant conclusions from them and (2) optimizing the modelling process in terms of computation demands. See Rotllan-Puig, X. & Traveset, A. (2021) <https://www.sciencedirect.com/science/article/pii/S0304380020304191>.
Allows users to produce estimates and MSE for multivariate variables using Linear Mixed Model. The package follows the approach of Datta, Day and Basawa (1999) <doi:10.1016/S0378-3758(98)00147-5>.
66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().
Convert Markdown ('.md') or R Markdown ('.Rmd') texts, R scripts, and directory structures, into mind map widgets or files ('.mm'), and vice versa. "FreeMind" mind map ('.mm') files can be opened by or imported to common mindmap software such as 'FreeMind' (<http://freemind.sourceforge.net/wiki/index.php/Main_Page>).
Citrus is a computational technique developed for the analysis
    of high dimensional cytometry data sets. This package extracts, statistically
    analyzes, and visualizes marker expression from 'citrus' data. This code was used to 
    generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. 
    “Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile 
    Idiopathic Arthritis with Mass Cytometry”, JCI-Insight. For more information on Citrus,
    please see: Bruggner et al. (2014) <doi:10.1073/pnas.1408792111>. To download
    the 'citrus' package, please see <https://github.com/nolanlab/citrus>.
This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2019) <DOI:10.1080/00401706.2018.1552203>. The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate. This research is supported by a U.S. National Science Foundation grant DMS-1712642.
The nonparametric two-stage Bayesian adaptive design is a novel phase II clinical trial design for finding the minimum effective dose (MinED). This design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. It is used to design single-agent trials.
Wrapper for 'minepy' implementation of Maximal
        Information-based Nonparametric Exploration statistics (MIC and
        MINE family). Detailed information of the ANSI C implementation of
	'minepy' can be found at <http://minepy.readthedocs.io/en/latest>.
Makes it possible to create an internally consistent
    repository consisting of selected packages from CRAN-like repositories.
    The user specifies a set of desired packages, and 'miniCRAN' recursively
    reads the dependency tree for these packages, then downloads only this
    subset. The user can then install packages from this repository directly,
    rather than from CRAN.  This is useful in production settings, e.g. server
    behind a firewall, or remote locations with slow (or zero) Internet access.
Create minimal, responsive, and style-agnostic HTML documents with
    the lightweight CSS frameworks such as 'sakura', 'Water.css', and 'spcss'.
    Powerful features include table of contents floating as a sidebar,
    folding codes and results, and more.
Quick and simple Tcl/Tk Graphical User Interface 
  to call functions. Also comprises a very simple experimental 
  GUI framework.
Generate central composite designs (CCD)with full as well 
    as fractional factorial points (half replicate) and Box Behnken 
    designs (BBD) with minimally changed run sequence.
Create tile grid maps, which are like choropleth maps except each
  region is represented with equal visual space.
The minimax family of distributions is a two-parameter
        family like the beta family, but computationally a lot more
        tractible.
Provides two main functions, minimax() and miniMaxPro(), for computing minimax 
    and minimax projection designs using the minimax clustering algorithm in Mak and 
    Joseph (2018) <DOI:10.1080/10618600.2017.1302881>. Current design region options 
    include the unit hypercube ("hypercube"), the unit simplex ("simplex"), the unit ball
    ("ball"), as well as user-defined constraints on the unit hypercube ("custom"). Minimax
    designs can also be computed on user-provided images using the function minimax.map(). 
    Design quality can be assessed using the function mMdist(), which computes the minimax
    (fill) distance of a design.
Shiny web application to run meta-analyses. 
    Essentially a graphical front-end to package 'meta' for R. 
    Can be useful as an educational tool, and for quickly 
    analyzing and sharing meta-analyses. 
    Provides output to quickly fill in GRADE (Grading of 
    Recommendations, Assessment, Development and Evaluations)
    Summary-of-Findings tables. 
    Importantly, it allows further processing of the results 
    inside R, in case more specific analyses are needed.
Randomization schedules are generated in the schemes with k (k>=2) treatment groups and any allocation ratios by minimization algorithms.
Provides UI widget and layout functions for writing Shiny apps
    that work well on small screens.
Computes the Minkowski sum of two 3D meshes, resulting in a
    new 3D mesh. The Minkowski addition has applications in mathematical
    morphology and 3D computer graphics. The computations are performed by
    the 'C++' library 'CGAL' (<https://www.cgal.org/>).
Performs treatment assignment for (field) experiments considering available, possibly multivariate and continuous, information (covariates, observable characteristics), that is: forms balanced treatment groups, according to the minMSE-method as proposed by Schneider and Schlather (2017) <DOI:10419/161931>.
The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds.  The implementation can be used via nls-like calls using the nlsLM function.
Derivative-free optimization by quadratic approximation
        based on an interface to Fortran implementations by M. J. D.
        Powell.
This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.
Using this package, one can determine the minimum sample size required so that the absolute deviation of the sample mean and the population mean of a distribution becomes less than some pre-determined epsilon, i.e. it helps the user to determine the minimum sample size required to attain the pre-fixed precision level by minimizing the difference between the sample mean and population mean.
Using this package, one can determine the minimum sample size required so that the mean square error of the sample mean and the population mean of a distribution becomes less than some pre-determined epsilon, i.e. it helps the user to determine the minimum sample size required to attain the pre-fixed precision level by minimizing the difference between the sample mean and population mean.
This is a R implementation of "Minimum SNPs" software as described in "Price E.P., Inman-Bamber, J., Thiruvenkataswamy, V., Huygens, F and Giffard, P.M." (2007) <doi:10.1186/1471-2105-8-278> "Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants."
It can be used to create/encode molecular "license-plates" from sequences and to also decode the "license-plates" back to sequences.  While initially created for transfer RNA-derived small fragments (tRFs), this tool can be used for any genomic sequences including but not limited to: tRFs, microRNAs, etc. The detailed information can reference to Pliatsika V, Loher P, Telonis AG, Rigoutsos I (2016) <doi:10.1093/bioinformatics/btw194>. It can also be used to annotate tRFs. The detailed information can reference to Loher P, Telonis AG, Rigoutsos I (2017) <doi:10.1038/srep41184>.
For a given set of stoichiometric reactions, this package
    evaluates the mass and charge balance, extracts all reactants, products, orphan
    metabolites, metabolite names and compartments. Also are included some options
    to characterize and write models in TSV and SBML formats.
An implementation of the iterative proportional fitting (IPFP), 
    maximum likelihood, minimum chi-square and weighted least squares procedures
    for updating a N-dimensional array with respect to given target marginal 
    distributions (which, in turn can be multidimensional). The package also
    provides an application of the IPFP to simulate multivariate Bernoulli
    distributions.
Generic functions to produce area/bar/box/line plots of data following IAMC (Integrated Assessment Modeling Consortium) submission format.
Simple and lightweight parallelism and concurrent code execution,
    local or distributed across the network, built on 'nanonext' and 'NNG'
    (Nanomsg Next Gen) technology.
Fitting recurrent events survival models for
    left-censored data with multiple imputation of the number of previous episodes.
    See Hernández-Herrera G, Moriña D, Navarro A. (2020) <arXiv:2007.15031>.
Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.
Providing tools for microRNA (miRNA) text mining. 
    miRetrieve summarizes miRNA literature by extracting, counting, and 
    analyzing miRNA names, thus aiming at gaining biological insights into a 
    large amount of text within a short period of time. To do so, miRetrieve 
    uses regular expressions to extract miRNAs and tokenization to 
    identify meaningful miRNA associations. In addition, miRetrieve uses 
    the latest miRTarBase version 8.0 (Hsi-Yuan Huang et al. (2020) 
    "miRTarBase 2020: updates to the experimentally validated microRNA–target 
    interaction database" <doi:10.1093/nar/gkz896>) to display field-specific 
    miRNA-mRNA interactions. The most important functions are available as a 
    Shiny web application under <https://miretrieve.shinyapps.io/miRetrieve/>.
Test for overall association between microbiome composition data 
  and phenotypes via phylogenetic kernels. The phenotype can be univariate 
  continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>), 
  survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>), 
  multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and 
  structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>). 
  The package can also use robust regression (unpublished work) and 
  integrated quantile regression (Wang et al. (2021) <doi:10.1093/bioinformatics/btab668>). 
  In each case, the microbiome community effect is modeled nonparametrically 
  through a kernel function, which can incorporate phylogenetic tree information.
A complete and dedicated analytical toolbox for quality control and diagnosis based on subject-related measurements of micro-RNA (miRNA) expressions. The package consists of a set of functions that allow to train, optimize and use a Bayesian classifier that relies on multiplets of measured miRNA expressions. The package also implements the quality control tools required to preprocess input datasets. In addition, the package provides a function to carry out a statistical analysis of miRNA expressions, which can give insights to improve the classifier's performance. The method implemented in the package was first introduced in L. Ricci, V. Del Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, "Statistical analysis of a Bayesian classifier based on the expression of miRNAs", BMC Bioinformatics 16:287, 2015 <doi:10.1186/s12859-015-0715-9>. The package is thoroughly described in M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti and L. Ricci, "MiRNA-QC-and-Diagnosis: An R package for diagnosis based on MiRNA expression", SoftwareX 12:100569, 2020 <doi:10.1016/j.softx.2020.100569>. Please cite both these works if you use the package for your analysis. DISCLAIMER: The software in this package is for general research purposes only and is thus provided WITHOUT ANY WARRANTY. It is NOT intended to form the basis of clinical decisions. Please refer to the GNU General Public License 3.0 (GPLv3) for further information.
Machine learning method specifically designed for
    pre-miRNA prediction. It takes advantage of unlabeled sequences to improve
    the prediction rates even when there are just a few positive examples, when
    the negative examples are unreliable or are not good representatives of
    its class. Furthermore, the method can automatically search for negative
    examples if the user is unable to provide them. MiRNAss can find a good
    boundary to divide the pre-miRNAs from other groups of sequences; it
    automatically optimizes the threshold that defines the classes boundaries,
    and thus, it is robust to high class imbalance. Each step of the method is
    scalable and can handle large volumes of data.
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Analysis of dichotomous and polytomous response data using
    unidimensional and multidimensional latent trait models under the Item
    Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). 
    Exploratory and confirmatory models can be estimated with quadrature (EM) 
    or stochastic (MHRM) methods. Confirmatory
    bi-factor and two-tier analyses are available for modeling item testlets.
    Multiple group analysis and mixed effects designs also are available for
    detecting differential item and test functioning as well as modeling
    item and person covariates. Finally, latent class models such as the DINA,
    DINO, multidimensional latent class, and several other discrete latent
    variable models, including mixture and zero-inflated response models, 
    are supported.
Provides tools to generate an HTML interface for creating adaptive
    and non-adaptive educational and psychological tests using the shiny
    package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). 
    Suitable for applying unidimensional and multidimensional
    computerized adaptive tests (CAT) using item response theory methodology and for
    creating simple questionnaires forms to collect response data directly in R.
    Additionally, optimal test designs (e.g., "shadow testing") are supported
    for tests which contain a large number of item selection constraints.
    Finally, package contains tools useful for performing Monte Carlo simulations 
    for studying the behavior of computerized adaptive test banks.
Package for combined miRNA- and mRNA-testing.
Provides constrained joint maximum likelihood estimation
    algorithms for item factor analysis (IFA) based on multidimensional item response theory
    models. So far, we provide functions for exploratory and confirmatory IFA based on the 
    multidimensional two parameter logistic (M2PL) model for binary response data. Comparing 
    with traditional estimation methods for IFA, the methods implemented in this package scale
    better to data with large numbers of respondents, items, and latent factors. The computation
    is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to:
    1. Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for 
    High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23. 
    <doi:10.1007/s11336-018-9646-5>;
    2. Chen, Y., Li, X., & Zhang, S. (2019). Structured Latent Factor Analysis for Large-scale Data: 
    Identifiability, Estimability, and Their Implications. Journal of the American Statistical 
    Association, <doi: 10.1080/01621459.2019.1635485>.
Provides singular value decomposition based estimation algorithms for exploratory item factor analysis (IFA) 
    based on multidimensional item response theory models. For more information, please refer to:
    Zhang, H., Chen, Y., & Li, X. (2020). A note on exploratory item factor analysis by 
    singular value decomposition. Psychometrika, 1-15, <DOI:10.1007/s11336-020-09704-7>.
Estimate parameters of linear regression and logistic regression with missing covariates with missing data, perform model selection and prediction, using EM-type algorithms. Jiang W., Josse J., Lavielle M., TraumaBase Group (2020) <doi:10.1016/j.csda.2019.106907>.
A collection of miscellaneous 3d plots, including
    isosurfaces.
Various functions for random number generation, density 
             estimation, classification, curve fitting, and spatial 
             data analysis.
Implementing various things including functions for LaTeX tables,
    the Kalman filter, web scraping, development tools, relative risk and odds
    ratio.
Estimation of the survivor function for interval censored time-to-event data subject to misclassification using nonparametric maximum likelihood estimation, implementing the methods of Titman (2017) <doi:10.1007/s11222-016-9705-7>. Misclassification probabilities can either be specified as fixed or estimated. Models with time dependent misclassification may also be fitted.
Estimates models that extend the standard GLM to take
    misclassification into account. The models require side information from a secondary data set
    on the misclassification process, i.e. some sort of misclassification
    probabilities conditional on some common covariates.
    A detailed description of the algorithm can be found in
    Dlugosz, Mammen and Wilke (2015) <http://www.zew.de/PU70410>.
A collection of miscellaneous methods to simplify various tasks,
  including plotting, data.frame and matrix transformations, environment
  functions, regular expression methods, and string and logical operations, as
  well as numerical and statistical tools. Most of the methods are simple but
  useful wrappers of common base R functions, which extend S3 generics or
  provide default values for important parameters.
Miscellaneous small tools and utilities.
   Many of them facilitate the work with matrices,
   e.g. inserting rows or columns, creating symmetric matrices,
   or checking for semidefiniteness.
   Other tools facilitate the work with regression models,
   e.g. extracting the standard errors,
   obtaining the number of (estimated) parameters,
   or calculating R-squared values.
Clears the workspace. Useful for the beginnings of R scripts, to
    avoid potential problems with accidentally using information from variables
    or functions from previous script evaluations, too many figure windows open
    at the same time, packages that you don't need any more, or a cluttered
    console.  Uses code from various StackOverflow users.  See help(mise) for
    pointers to the relevant StackOverflow pages.
Penalized regression methods, such as lasso and elastic net, are used in
    many biomedical applications when simultaneous regression coefficient
    estimation and variable selection is desired. However, missing data
    complicates the implementation of these methods, particularly when
    missingness is handled using multiple imputation. Applying a variable
    selection algorithm on each imputed dataset will likely lead
    to different sets of selected predictors, making it difficult
    to ascertain a final active set without resorting to ad hoc
    combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet)
    and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes,
    developed by Du et al (2020), currently under review. They, by construction,
    force selection of the same variables across multiply imputed data.
    'miselect' also provides cross validated variants of these methods.
Open-source software for computing Likelihood ratios thresholds and error rates in DNA kinship testing. Marsico FL. et al (2021) <doi:10.1016/j.fsigen.2021.102519>.
Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.
Partial Replacement Imputation Estimation (PRIME) can overcome problems caused by missing covariates in additive partially linear model. PRIME conducts imputation and regression simultaneously with known and unknown model structure. More details can be referred to 
    Zishu Zhan, Xiangjie Li and Jingxiao Zhang. (2022) <arXiv:2205.14994>.
There is an increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. In this package, we present a novel global testing method called aMiSPU, that is highly adaptive and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real data analysis demonstrated that aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates.
Provides a simple and trustworthy methodology for the analysis of misreported continuous time series. See Moriña, D, Fernández-Fontelo, A, Cabaña, A, Puig P. (2021) <arXiv:2003.09202v2>.
Enables investigation of the predictors of misreporting on sensitive survey questions through a multivariate list experiment regression method. The method permits researchers to model whether a survey respondent's answer to the sensitive item in a list experiment is different from his or her answer to an analogous direct question.
Offers a convenient pipeline to test and compare various missing data
  imputation algorithms on simulated and real data. These include simpler methods, such as mean and median
  imputation and random replacement, but also include more sophisticated algorithms already implemented in popular 
  R packages, such as 'mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; 'mice', described by van Buuren 
  and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; 'missForest', described by Stekhoven and Buhlmann (2012) 
  <doi:10.1093/bioinformatics/btr597>; 'missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and 
  'pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. The central assumption behind 
  'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables 
  vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation 
  algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and 
  sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns.
  'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm
  in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you 
  assess imputation performance.
Implements two methods: a nonparametric risk adjustment and a
    data imputation method that use general population mortality tables to allow a
    correct analysis of time to disease recurrence. Also includes a powerful set of
    object oriented survival data simulation functions.
Implements the computation of discrepancy statistics summarizing differences between the density of imputed and observed values and the construction of weights to balance covariates that are part of the missing data mechanism as described in Marbach (2021) <arXiv:2107.05427>.
The function 'missForest' in this package is used to
        impute missing values particularly in the case of mixed-type
        data. It uses a random forest trained on the observed values of
        a data matrix to predict the missing values. It can be used to
        impute continuous and/or categorical data including complex
        interactions and non-linear relations. It yields an out-of-bag
        (OOB) imputation error estimate without the need of a test set
        or elaborate cross-validation. It can be run in parallel to 
        save computation time.
Contains a suite of functions for health economic evaluations with missing outcome data. 
  The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). 
  Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models.
  In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. 
  The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.
A system for Analysis of LSD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3).
A system for Analysis of RBD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3).
Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.
Supply functions for the creation and handling of missing
    data as well as tools to evaluate missing data methods. Nearly all
    possibilities of generating missing data discussed by Santos et al.
    (2019) <doi:10.1109/ACCESS.2019.2891360> and some additional are
    implemented.  Functions are supplied to compare parameter estimates
    and imputed values to true values to evaluate missing data methods.
    Evaluations of these types are done, for example, by Cetin-Berber et
    al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005)
    <doi:10.1093/bioinformatics/bth499>.
Efficient procedures for fitting the conditional graphical
    lasso models linking a set of predictor variables to a set of response
    variables (or tasks), when the response data may contain missing
    values. 'missoNet' simultaneously estimates the predictor coefficients
    for all tasks by leveraging information from one another, in order to
    provide more accurate predictions in comparison to modeling them
    individually. Meanwhile, 'missoNet' is able to estimate the response
    network structure influenced by conditioning predictor variables in a
    L1-regularized conditional Gaussian graphical model. In contrast to
    most penalized multi-task regression (conditional graphical lasso)
    methods, 'missoNet' has the capability of obtaining estimates even
    when the response data is corrupted by missing values. The method
    automatically enjoys the theoretical and computational benefits of
    convexity, and returns solutions that are comparable/close to the
    estimates without any missing values. The package also includes
    auxiliary functions for data simulation, goodness-of-fit evaluation,
    regularization parameter tuning, and visualization of results, as well
    as predictions in new data.
A system for testing differential effects among treatments in case of Randomised Block Design and Latin Square Design when there is one missing observation. Methods for this process are as described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).
Alternative implementation of the beautiful 'MissForest'
    algorithm used to impute mixed-type data sets by chaining random
    forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012)
    <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the
    lightning fast random jungle package 'ranger'. Between the iterative
    model fitting, we offer the option of using predictive mean matching.
    This firstly avoids imputation with values not already present in the
    original data (like a value 0.3334 in 0-1 coded variable).  Secondly,
    predictive mean matching tries to raise the variance in the resulting
    conditional distributions to a realistic level. This would allow e.g.
    to do multiple imputation when repeating the call to missRanger().  A
    formula interface allows to control which variables should be imputed
    by which.
When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 
  due to missing information between node pairs), it is possible to account for the underlying process
  that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>,
  adjusts the popular stochastic block model from network data sampled under various missing data conditions, 
  as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.
The Self-Organizing Maps with Built-in Missing Data Imputation. Missing values are imputed and regularly updated during the online Kohonen algorithm. Our method can be used for data visualisation, clustering or imputation of missing data. It is an extension of the online algorithm of the 'kohonen' package. The method is described
    in the article "Self-Organizing Maps for Exploration of Partially Observed Data and Imputation of Missing Values" by S. Rejeb, C. Duveau, T. Rebafka (2022) <arXiv:2202.07963>.
Provide all the data sets and statistical analysis applications used in "Modern Industrial Statistics: with applications in R, MINITAB and JMP" by R.S. Kenett and S. Zacks with contributions by D. Amberti, John Wiley and Sons, 2021, which is a third revised and expanded revision of "Modern Industrial Statistics: Design and Control of Quality and Reliability", R. Kenett and S. Zacks, Duxbury/Wadsworth Publishing, 1998.
A flexible computational framework for mixture distributions with the focus on the composite models.
Various reliability analysis methods for rare event inference:1) computing failure probability (probability that the output of a numerical model exceeds a threshold), 2) computing quantile of low or high-order, 3) Wilks formula to compute quantile(s) from a sample or the size of the required i.i.d. sample.
Miscellaneous functions for descriptive statistics (e.g., frequency table, cross tabulation, multilevel descriptive statistics, multilevel R-squared measures, within-group and between-group correlation matrix, various effect size measures), data management (e.g., grand-mean and group-mean centering, recode variables and reverse code items, scale and group scores, reading and writing SPSS and Excel files), missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), item analysis (e.g., coefficient alpha and omega, confirmatory factor analysis), and  statistical analysis (e.g., confidence intervals, collinearity diagnostics, analysis of variance, Levene's test, t-test, z-test, sample size determination).
Provides tools for multiple imputation of missing data in multilevel
 modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo',
 and several functions for visualization, data management and the analysis 
 of multiply imputed data sets.
Allows the estimation and downstream statistical analysis of the mitochondrial DNA Heteroplasmy calculated from single-cell datasets <https://github.com/ScialdoneLab/MitoHEAR/tree/master>.
Tools to perform analyses and combine results from
        multiple-imputation datasets.
Extract, transform and load MITRE standards.
    This package gives you an approach to cybersecurity data sets.
    All data sets are build on runtime downloading raw data from MITRE public services.
    MITRE <https://www.mitre.org/> is a government-funded research organization 
    based in Bedford and McLean. Current version includes most used standards as
    data frames. It also provide a list of nodes and edges with all relationships.
Implements the Mittag-Leffler function, distribution,
  random variate generation, and estimation. Based on the Laplace-Inversion
  algorithm by Garrappa, R. (2015) <doi:10.1137/140971191>.
The miWQS package handles the uncertainty due to below the detection limit in a correlated component mixture problem.  Researchers want to determine if a set/mixture of continuous and correlated components/chemicals is associated with an outcome and if so, which components are important in that mixture. These components share a common outcome but are interval-censored between zero and low thresholds, or detection limits, that may be different across the components. This package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>). The imputation models are: bootstrapping imputation (Lubin et.al (2004) <doi:10.1289/ehp.7199>), univariate Bayesian imputation (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>), and multivariate Bayesian regression imputation.
Estimation/multiple imputation programs for mixed categorical
    and continuous data.
Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.
Algorithms and methods for model-based clustering and classification.
 It supports various types of data: continuous, categorical and counting
 and can handle mixed data of these types. It can fit Gaussian (with diagonal
 covariance structure), gamma, categorical and Poisson models. The algorithms
 also support missing values. This package can be used as an independent
 alternative to the (not free) 'mixtcomp' software available at
 <https://massiccc.lille.inria.fr/>.
Model time series using mixture autoregressive (MAR)
             models.  Implemented are frequentist (EM) and Bayesian
             methods for estimation, prediction and model
             evaluation. See Wong and Li (2002)
             <doi:10.1111/1467-9868.00222>, Boshnakov (2009)
             <doi:10.1016/j.spl.2009.04.009>), and the extensive
             references in the documentation.
Mixed effects cumulative and baseline logit link models for the analysis of ordinal or nominal responses, with non-parametric distribution for the random effects.
Deconvolution of thermal decay curves allows you to quantify proportions 
    of biomass components in plant litter. Thermal decay curves derived from 
    thermogravimetric analysis (TGA) are imported, modified, and then modelled in a 
    three- or four- part  mixture model using the Fraser-Suzuki function. The output 
    is estimates for weights of pseudo-components corresponding to hemicellulose, 
    cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, 
    H. (2007) <doi:10.1016/j.jaap.2006.12.008>, Órfão, J. J. M. and Figueiredo, J. L. 
    (2001) <doi:10.1016/S0040-6031(01)00634-7>, and Yang, H. and Yan, R. and 
    Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) <doi:10.1021/ef0580117>.
Implementation of parametric and semiparametric mixture cure models based on existing R packages. See details of the models in Peng and Yu (2020) <ISBN: 9780367145576>.
Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet 
    (Process) mixture  of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used
    to reduce the  dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically
    infer an appropriate number of latent classes or find k classes, as defined by the user.  The model is based on a
    paper by Dunson and Xing (2009) <doi:10.1198/jasa.2009.tm08439>, but implements a scalable variational inference algorithm so that it is
    applicable to large datasets. It is described and tested in the accompanying paper by 
    Ahlmann-Eltze and Yau (2018) <doi:10.1109/DSAA.2018.00068>.
Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.
Semi-parametric approach for sparse canonical correlation analysis 
    which can handle mixed data types: continuous, binary and truncated continuous.
    Bridge functions are provided to connect Kendall's tau to latent correlation
    under the Gaussian copula model. The methods are described in 
    Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and 
    Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.
Implementation of the co-clustering method for mixed type data proposed in M. Selosse, J. Jacques, C. Biernacki (2018) <https://hal.archives-ouvertes.fr/hal-01893457>. It consists in clustering simultaneously the rows (observations) and the columns (features) of a heterogeneous data set.
Functions for testing randomness for a univariate time series with arbitrary distribution  (discrete, continuous, mixture of both types) and for testing  independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in  Genest, Nešlehová, Rémillard & Murphy (2019).
Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when 
  the coefficient matrix is low-rank and sparse. 'mixedLSR' allows subgroup identification by alternating optimization 
  with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically 
  performing parameter selection to identify low-rank substructures in the coefficient matrix.
Fits mixed membership models with discrete multivariate data (with
    or without repeated measures) following the general framework of Erosheva et al
    (2004). This package uses a Variational EM approach by approximating the
    posterior distribution of latent memberships and selecting hyperparameters
    through a pseudo-MLE procedure. Currently supported data types are
    Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with 
    fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al 
    (2014) for other examples of mixed membership models.
The estimation of the parameters in mixed Poisson models.
Tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) 
    and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). 
    Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets.
    For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012).
Inference on stochastic differential models Ornstein-Uhlenbeck or
    Cox-Ingersoll-Ross, with one or two random effects in the drift function.
We provide detailed functions for univariate Mixed Tempered Stable distribution.
Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.
Evaluation and optimization of the Fisher Information Matrix in NonLinear Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete data.
Utilizing model-based clustering (unsupervised)
        for functional magnetic resonance imaging (fMRI) data.
        The developed methods (Chen and Maitra (2021) <arXiv:2102.03639>)
        include 2D and 3D clustering analyses
        (for p-values with voxel locations) and
        segmentation analyses (for p-values alone) for fMRI data where p-values
        indicate significant level of activation responding to stimulate
        of interesting. The analyses are mainly identifying active
        voxel/signal associated with normal brain behaviors.
        Analysis pipelines (R scripts) utilizing this package
        (see examples in 'inst/workflow/') is also implemented with high
        performance techniques.
Multiple imputation using 'XGBoost', bootstrapping and predictive mean 
    matching as described in Deng and Lumley (2021) <arXiv:2106.01574>. It is built 
    under Fully Conditional Specification, where 'XGBoost' imputation models are
    built for each incomplete variable. It supports various types of variables and 
    offers different settings regarding bootstrapping and predictive mean matching. 
    Visual diagnostic functions are also provided for inspecting multiply imputed 
    values for incomplete variables.
Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the  generalized hyperbolic distribution. The first model 'MGHD' (Browne and McNicholas (2015) <doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016) <doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD' is the mixture of  multiple scaled generalized hyperbolic distributions, the 'cMSGHD'  is a 'MSGHD' with convex contour plots and the 'MCGHD', mixture of  coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019)<doi:10.1007/s00357-019-09319-3>. The paper related to the software can be found at <doi:10.18637/jss.v098.i03>.
Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, <https://github.com/RuzhangZhao/mixhvg>.
Developed to deal with multi-locus genotype data, this package is especially designed for those panel which include different type of markers. Basic genetic parameters like allele frequency, genotype frequency, heterozygosity and Hardy-Weinberg test of mixed genetic data can be obtained.     In addition, a new test for mutual independence which is compatible for mixed genetic data is developed in this package.
Kernel-based methods are powerful methods for integrating 
    heterogeneous types of data. mixKernel aims at providing methods to combine
    kernel for unsupervised exploratory analysis. Different solutions are 
    provided to compute a meta-kernel, in a consensus way or in a way that 
    best preserves the original topology of the data. mixKernel also integrates
    kernel PCA to visualize similarities between samples in a non linear space
    and from the multiple source point of view. Functions to assess and display
    important variables are also provided in the package. Ref: Jerome Mariette 
    and Nathalie Villa-Vialaneix (2018) <doi:10.1093/bioinformatics/btx682>.
Specification and estimation of multinomial logit
    models.  Large datasets and complex models are supported, with an
    intuitive syntax.  Multinomial Logit Models, Mixed models, random
    coefficients and Hybrid Choice are all supported.  For more
    information, see Molloy et al. (2019) <doi:10.3929/ethz-b-000334289>.
The main functions perform mixed models analysis by least squares
    or REML by adding the function r() to formulas of lm() and glm(). A collection of
    text-book statistics for higher education is also included, e.g. modifications
    of the functions lm(), glm() and associated summaries from the package 'stats'.
A collection of functions to implement the MixMAP algorithm, which performs gene
    level tests of association using data from a previous GWAS or data from a
    meta-analysis of several GWAS.  Conceptually, genes are detected as
    significant if the collection of p-values within a gene are determined to
    be collectively smaller than would be observed by chance.
Provides sampling and density functions for matrix
    variate normal, t, and inverted t distributions;  ML estimation for matrix
    variate normal and t distributions using the EM algorithm,
    including some restrictions on the parameters; and classification by linear and
    quadratic discriminant analysis for matrix variate normal and t
    distributions described in Thompson et al. (2019) <doi:10.1080/10618600.2019.1696208>.
    Performs clustering with matrix variate normal and t mixture models.
A collection of functions to perform various meta-analytical models
  through a unified mixed-effects framework, including standard univariate
  fixed and random-effects meta-analysis and meta-regression, and non-standard
  extensions such as multivariate, multilevel, longitudinal, and dose-response
  models.
Mixed variable optimization for non-linear functions.
    Can optimize function whose inputs are a combination of continuous,
    ordered, and unordered variables.
Simple tools to perform mixture optimization based on the 'desirability' package by Max Kuhn.
    It also provides a plot routine using 'ggplot2' and 'patchwork'.
Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.
Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.
Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'.
Provides supplemental functions for the 'mixRasch'
    package (Willse, 2014), <https://cran.r-project.org/package=mixRasch/mixRasch.pdf> including a 
    plotting function to compare item parameters for multiple
    class models and a function that provides average theta values for each class in
    a mixture model.
Fits mixtures of (possibly multivariate) regressions
	(which has been described as doing ANCOVA when you don't
	know the levels). Turner (2000) <doi:10.1111/1467-9876.00198>.
It offers random-forest-based functions to impute clustered
    incomplete data. The package is tailored for but not limited to imputing
    multitissue expression data, in which a gene's expression is measured on the
    collected tissues of an individual but missing on the uncollected tissues.
The current version of the 'MixSAL' package allows users to generate data from a multivariate SAL distribution or a mixture of multivariate SAL distributions, evaluate the probability density function of a multivariate SAL distribution or a mixture of multivariate SAL distributions, and fit a mixture of multivariate SAL distributions using the Expectation-Maximization (EM) algorithm (see Franczak et. al, 2014, <doi:10.1109/TPAMI.2013.216>, for details).
Creates and runs Bayesian mixing models to analyze
    biological tracer data (i.e. stable isotopes, fatty acids), which estimate the
    proportions of source (prey) contributions to a mixture (consumer). 'MixSIAR'
    is not one model, but a framework that allows a user to create a mixing model
    based on their data structure and research questions, via options for fixed/
    random effects, source data types, priors, and error terms. 'MixSIAR' incorporates
    several years of advances since 'MixSIR' and 'SIAR'.
The utility of this package is in simulating mixtures of Gaussian
        distributions with different levels of overlap between mixture
        components.  Pairwise overlap, defined as a sum of two
        misclassification probabilities, measures the degree of
        interaction between components and can be readily employed to
        control the clustering complexity of datasets simulated from
        mixtures. These datasets can then be used for systematic
        performance investigation of clustering and finite mixture
        modeling algorithms. Among other capabilities of 'MixSim', there
        are computing the exact overlap for Gaussian mixtures,
        simulating Gaussian and non-Gaussian data, simulating outliers
        and noise variables, calculating various measures of agreement
        between two partitionings, and constructing parallel
        distribution plots for the graphical display of finite mixture
        models.
Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) <doi: 10.18637/jss.v054.i12>, Cabral, Lachos and Prates (2012) <doi:10.1016/j.csda.2011.06.026> and Basso, Lachos, Cabral and Ghosh (2010) <doi:10.1016/j.csda.2009.09.031>.
Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew 
    power exponential and power exponential distributions, respectively. A generalized expectation-maximization 
    framework is used for parameter estimation. Methodology for mixtures of power exponential distributions is 
    from Dang et al. (2015) <doi: 10.1111/biom.12351>.
Provides an optimization method based on sequential
    quadratic programming (SQP) for maximum likelihood estimation of
    the mixture proportions in a finite mixture model where the
    component densities are known. The algorithm is expected to obtain
    solutions that are at least as accurate as the state-of-the-art
    MOSEK interior-point solver (called by function "KWDual" in the
    'REBayes' package), and they are expected to arrive at solutions
    more quickly when the number of samples is large and the number of
    mixture components is not too large. This implements the "mix-SQP"
    algorithm, with some improvements, described in Y. Kim,
    P. Carbonetto, M. Stephens & M. Anitescu (2020)
    <DOI:10.1080/10618600.2019.1689985>.
Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian
              stable distributions developed by Teimouri (2022) <arXiv:2205.14067> and estimating
              parameters of the symmetric stable distribution within the Bayesian framework.
Analyzes finite mixture models for various parametric and semiparametric settings.  This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria).  Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.
Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) 
 dose-response data. Predicting mixture toxicity based on reference 
 models such as 'concentration addition', 'independent action', and 'generalized 
 concentration addition'.
A set of utility functions for analysing and modelling data from 
    continuous report short-term memory experiments using either the 2-component
    mixture model of Zhang and Luck (2008) <doi:10.1038/nature06860> or the 
    3-component mixture model of Bays et al. (2009) <doi:10.1167/9.10.7>. Users 
    are also able to simulate from these models.
An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.
Functions for computing the penalized maximum likelihood estimate (PMLE) or maximum likelihood estimate (MLE), testing the order of a finite mixture model using EM-test, drawing histogram of observations and the fitted density or probability mass function of the mixture model.
Implementation of robust model based cluster analysis with missing data. 
    The models used are: Multivariate Contaminated Normal Mixtures (MCNM),
    Multivariate Student's t  Mixtures (MtM), and Multivariate Normal Mixtures (MNM)
    for data sets with missing values at random. 
    See "Model-Based Clustering and Outlier Detection with Missing Data" by
    Hung Tong and Cristina Tortora (2022) <doi:10.1007/s11634-021-00476-1>.
Fit mixture regression models with nonsusceptibility/cure for left-truncated and interval-censored (LTIC) data (see Chen et al. (2013) <doi:10.1002/sim.5845>). This package also provides the nonparametric maximum likelihood estimator (NPMLE) for the survival/event curves with LTIC data.
Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2021) <doi: 10.1093/bioinformatics/btab162>.
Tool for exploring DNA and amino acid variation and inferring the presence of target lineages from microbial high-throughput genomic DNA samples that potentially contain mixtures of variants/lineages. MixviR was originally created to help analyze environmental SARS-CoV-2/Covid-19 samples from environmental sources such as wastewater or dust, but can be applied to any microbial group. Inputs include reference genome information in commonly-used file formats (fasta, bed) and one or more variant call format (VCF) files, which can be generated with programs such as Illumina's DRAGEN, the Genome Analysis Toolkit, or bcftools. See DePristo et al (2011) <doi:10.1038/ng.806> and Danecek et al (2021) <doi:10.1093/gigascience/giab008> for these tools, respectively. Available outputs include a table of mutations observed in the sample(s), estimates of proportions of target lineages in the sample(s), and an R Shiny dashboard to interactively explore the data.
Optimization algorithms implemented in R, including
    conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the
    limited memory BFGS (L-BFGS) methods. Most internal parameters can be set 
    through the call interface. The solvers hold up quite well for 
    higher-dimensional problems.
A set of classes and methods to set up and run multi-species, trait
    based and community size spectrum ecological models, focused on the marine
    environment.
Performance measures and scores for statistical classification such as accuracy, sensitivity, specificity, recall, similarity coefficients, AUC, GINI index, Brier score and many more. Calculation of optimal cut-offs and decision stumps (Iba and Langley (1991), <doi:10.1016/B978-1-55860-247-2.50035-8>) for all implemented performance measures. Hosmer-Lemeshow goodness of fit tests (Lemeshow and Hosmer (1982), <doi:10.1093/oxfordjournals.aje.a113284>; Hosmer et al (1997), <doi:10.1002/(SICI)1097-0258(19970515)16:9%3C965::AID-SIM509%3E3.0.CO;2-O>). Statistical and epidemiological risk measures such as relative risk, odds ratio, number needed to treat (Porta (2014), <doi:10.1093%2Facref%2F9780199976720.001.0001>).
Computation of standardized interquartile range (IQR), Huber-type skipped mean (Hampel (1985), <doi:10.2307/1268758>), robust coefficient of variation (CV) (Arachchige et al. (2019), <arXiv:1907.01110>), robust signal to noise ratio (SNR), z-score, standardized mean difference (SMD), as well as functions that support graphical visualization such as boxplots based on quartiles (not hinges), negative logarithms and generalized logarithms for 'ggplot2' (Wickham (2016), ISBN:978-3-319-24277-4).
Calculation routines based on the FOCUS Kinetics Report (2006,
  2014).  Includes a function for conveniently defining differential equation
  models, model solution based on eigenvalues if possible or using numerical
  solvers.  If a C compiler (on windows: 'Rtools') is installed, differential
  equation models are solved using automatically generated C functions.
  Heteroscedasticity can be taken into account using variance by variable or
  two-component error models as described by Ranke and Meinecke (2018)
  <doi:10.3390/environments6120124>.  Interfaces to several nonlinear
  mixed-effects model packages are available, some of which are described by
  Ranke et al. (2021) <doi:10.3390/environments8080071>.  Please note that no
  warranty is implied for correctness of results or fitness for a particular
  purpose.
Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), <doi:10.1016/S0167-7152(97)00043-6>), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) and multiple imputation (Barnard and Rubin (1999), <doi:10.1093/biomet/86.4.948>) t-test. Graphical visualization by volcano plots.
Package for fast computation of the maximum kernel likelihood estimator (mkle).
It's a Modern K-Means clustering algorithm allowing data of any number of dimensions, any initial center, and any number of clusters to expect.
Contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals and tests, and generation of similarity matrices.
Package solves multiple knapsack optimisation problem. 
    Given a set of items, each with volume and value, 
    it will allocate them to knapsacks of a given size in a way that
    value of top N knapsacks is as large as possible.
Similarity plots based on correlation and median absolute deviation (MAD); adjusting colors for heatmaps; aggregate technical replicates; calculate pairwise fold-changes and log fold-changes; compute one- and two-way ANOVA; simplified interface to package 'limma' (Ritchie et al. (2015), <doi:10.1093/nar/gkv007> ) for moderated t-test and one-way ANOVA; Hamming and Levenshtein (edit) distance of strings as well as optimal alignment scores for global (Needleman-Wunsch) and local (Smith-Waterman) alignments with constant gap penalties (Merkl and Waack (2009), ISBN:978-3-527-32594-8).
Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), <doi:10.1016/j.jclinepi.2004.12.009>; Dobbin and Simon (2007), <doi:10.1093/biostatistics/kxj036>) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), <doi:10.1016/j.csda.2003.10.002>; Thulin (2014), <doi:10.1214/14-ejs909>) and comparing two negative binomial rates (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>).
Generates efficient balanced non-aliased multi-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has chisquare efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient multi-level k-circulant design through a progress bar. The progress of 100% means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs.
Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) <doi:10.1098/rsif.2018.0512>.
Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required.
Provides 'R6' abstract classes for building machine learning models 
    with 'scikit-learn' like API. <https://scikit-learn.org/> is a popular module 
    for 'Python' programming language which design became de facto a standard 
    in industry for machine learning tasks.
A collection of artificial and real-world machine learning
        benchmark problems, including, e.g., several
        data sets from the UCI repository.
Computational functions for player metrics in major league baseball including batting, pitching, fielding, base-running, and overall player statistics. This package is actively maintained with new metrics being added as they are developed.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters.
Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them.  This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact. Reference: Knoblauch & Maloney (2012) "Modeling Psychophysical Data in R". <doi:10.1007/978-1-4614-4475-6>.
Contains a collection of datasets for working with machine learning tasks.
    It will contain datasets for supervised machine learning Jiang (2020)<doi:10.1016/j.beth.2020.05.002> and will include datasets for classification and regression.
    The aim of this package is to use data generated around health and other domains.
Exploratory data analysis and manipulation functions for multi-
    label data sets along with an interactive Shiny application to ease their use.
Large collection of multilabel datasets along with the functions
    needed to export them to several formats, to make partitions, and to obtain
    bibliographic information.
Difference scaling is a method for scaling perceived 
  supra-threshold differences.  The package contains functions that
  allow the user to design and run a difference scaling experiment, 
  to fit the resulting data by maximum likelihood and test the
  internal validity of the estimated scale.
Calculates the expected/observed Fisher information and the bias-corrected maximum likelihood estimate(s) via Cox-Snell Methodology.
A unified interface is provided to various machine learning
  algorithms like LDA, QDA, k-nearest neighbour, LVQ, random forest, SVM, ... It
  allows to train, test, and apply cross-validation using similar functions and
  function arguments with a minimalist and clean, formula-based interface.
  Missing data are threated the same way as base and stats R functions for all
  algorithms, both in training and testing. Confusion matrices are also provided
  with a rich set of metrics calculated and a few specific plots.
Estimate asymptotic efficient closed-form estimators and provide goodness of fit, estimates, plot and etc.
    Yue, S. (2001) <doi:10.1002/hyp.259>.
    Mosimann, James E. (1962) <doi:10.1093/biomet/49.1-2.65>.
We provide functions to compute the nonparametric 
  maximum likelihood estimator (MLE) for 
  the bivariate distribution of (X,Y), when 
  realizations of (X,Y) cannot be observed directly. 
  To be more precise, we consider the situation 
  where we observe a set of rectangles in R^2 that are known 
  to contain the unobservable realizations of (X,Y). We
  compute the MLE based on such a set of rectangles. 
  The methods can also be used for univariate censored data (see data set
  'cosmesis'), and for 
  censored data with competing risks (see data set 'menopause'). 
  We also provide functions to visualize the observed data and the MLE.
Maximum likelihood Gaussian process modeling for
        univariate and multi-dimensional outputs with diagnostic plots 
        following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>.
        Contact the maintainer for a package version that includes 
        sensitivity analysis.
Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (2006) <DOI: 10.1198/106186006X133069>. The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.
Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals.
Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.
The Iterative Proportional Fitting (IPF) algorithm operates on count data. 
    This package offers implementations for several algorithms that extend this to 
    nested structures: 'parent' and 'child' items for both of which constraints can be provided.
    The fitting algorithms include Iterative Proportional Updating <https://trid.trb.org/view/881554>,
    Hierarchical IPF <doi:10.3929/ethz-a-006620748>, Entropy Optimization <https://trid.trb.org/view/881144>,
    and Generalized Raking <doi:10.2307/2290793>. Additionally, a number of replication methods
    is also provided such as 'Truncate, replicate, sample' <doi:10.1016/j.compenvurbsys.2013.03.004>.
R interface to 'MLflow', open source platform for
    the complete machine learning life cycle, see <https://mlflow.org/>.
    This package supports installing 'MLflow', tracking experiments,
    creating and running projects, and saving and serving models.
Climate-sensitive forest simulator based on the principles of 
  machine learning. It stimulates all key processes in the forest: radial growth, 
  height growth, mortality, crown recession, regeneration and harvesting. The 
  method for predicting tree heights was described by Skudnik and Jevšenak
  (2022) <doi:10.1016/j.foreco.2022.120017>, while the method for predicting
  basal area increments (BAI) was described by Jevšenak and Skudnik (2021) 
  <doi:10.1016/j.foreco.2020.118601>.
Contains the datasets for use with the book Salvan, Sartori and Pace (2020, ISBN:978-88-470-4002-1) "Modelli Lineari Generalizzati".
It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2018) <https://hal.inria.fr/hal-01857242>).
Tools and functions to fit a multilevel index of dissimilarity.
Machine learning algorithms have been used for performing 
    single missing data imputation and most recently, multiple imputations. 
    However, this is the first attempt for using automated machine learning algorithms 
    for performing both 
    single and multiple imputation. Automated machine learning is a procedure for 
    fine-tuning the model automatic, performing a random search for a model that 
    results in less error, without overfitting the data. The main idea is 
    to allow the model to set its own parameters for imputing each variable seperately 
    instead of setting fixed predefined parameters to impute all variables 
    of the dataset.
    Using automated machine learning, the package fine-tunes an Elastic 
    Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting,
    or Stacked Ensemble machine learning model (from one or a combination of other 
    supported algorithms) for imputing the missing 
    observations. This procedure has been implemented for the 
    first time by this package and is expected to outperform other packages for 
    imputing missing data that do not fine-tune their models. The multiple imputation 
    is implemented via bootstrapping without letting the duplicated observations to 
    harm the cross-validation procedure, which is the way imputed variables are evaluated.
    Most notably, the package implements automated procedure for handling imputing imbalanced 
    data (class rarity problem), which happens when a factor variable has a level that is far more 
    prevalent than the other(s). This is known to result in biased predictions, hence, biased 
    imputation of missing data. However, the autobalancing procedure ensures that instead of 
    focusing on maximizing accuracy (classification error) in imputing factor variables, 
    a fairer procedure and imputation method is practiced.
Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072.
An implementation of Multi-level Monte Carlo for R.  This package
    builds on the original 'Matlab' and C++ implementations by Mike Giles to provide
    a full MLMC driver and example level samplers.  Multi-core parallel sampling
    of levels is provided built-in.
A collection of evaluation metrics, including loss, score and
    utility functions, that measure regression, classification and ranking performance.
A collection of miscellaneous helper function for running multilevel/mixed models in 'lme4'. This package aims to provide functions to compute common tasks when estimating multilevel models such as computing the intraclass correlation and design effect, centering variables, estimating the proportion of variance explained at each level, pseudo-R squared, random intercept and slope reliabilities, tests for homogeneity of variance at level-1, and cluster robust and bootstrap standard errors. The tests and statistics reported in the package are from Raudenbush & Bryk (2002, ISBN:9780761919049), Hox et al. (2018, ISBN:9781138121362), and Snijders & Bosker (2012, ISBN:9781849202015).
Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.
Maximum likelihood estimates (MLE) of the proportions
    of 5-mC and 5-hmC in the DNA using information from BS-conversion,
    TAB-conversion, and oxBS-conversion methods. One can use information from all three methods 
    or any combination of two of them. Estimates are based on Binomial model by
    Qu et al. (2013) <doi:10.1093/bioinformatics/btt459> and
    Kiihl et al. (2019) <doi:10.1515/sagmb-2018-0031>.
Pipeline for Genome-Wide Association Study using Multi-Locus Mixed Model from Segura V, Vilhjálmsson BJ et al. (2012) <doi:10.1038/ng.2314>. The pipeline include detection of associated SNPs with MLMM, model selection by lowest eBIC and p-value threshold, estimation of the effects of the SNPs in the selected model and graphical functions.
Computational strategies for multivariate linear
        mixed-effects models with missing values, Schafer and Yucel
        (2002), Journal of Computational and Graphical Statistics, 11,
        421-442.
An efficient Gibbs sampling algorithm is developed for Bayesian multivariate longitudinal data analysis with the focus on selection of important elements in the generalized autoregressive matrix. It provides posterior samples and estimates of parameters. In addition, estimates of several information criteria such as Akaike information criterion (AIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and prediction accuracy such as the marginal predictive likelihood (MPL) and the mean squared prediction error (MSPE) are provided for model selection.
The implemented methods reach out to scientists that seek to estimate multiplicity of infection (MOI) and lineage (allele) frequencies and prevalences at molecular markers using the maximum-likelihood method described in Schneider (2018) <doi:10.1371/journal.pone.0194148>, and Schneider and Escalante (2014) <doi:10.1371/journal.pone.0097899>. Users can import data from Excel files in various formats, and perform maximum-likelihood estimation on the imported data by the package's moimle() function.
Data and examples from a multilevel modelling software review
  as well as other well-known data sets from the multilevel modelling
  literature.
Multilevel models (mixed effects models) are the statistical tool of choice for analyzing multilevel data (Searle et al, 2009). These models account for the correlated nature of observations within higher level units by adding group-level error terms that augment the singular residual error of a standard OLS regression. Multilevel and mixed effects models often require specialized data pre-processing and further post-estimation derivations and graphics to gain insight into model results. The package presented here, 'mlmtools', is a suite of pre- and post-estimation tools for multilevel models in 'R'. Package implements post-estimation tools designed to work with models estimated using 'lme4''s (Bates et al., 2014) lmer() function, which fits linear mixed effects regression models. Searle, S. R., Casella, G., & McCulloch, C. E. (2009, ISBN:978-0470009598). Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014) <doi:10.18637/jss.v067.i01>.
An implementation of several machine learning algorithms for 
    multivariate time series. The package includes functions allowing the
    execution of clustering, classification or outlier detection methods,
    among others. It also incorporates a collection of multivariate time
    series datasets which can be used to analyse the performance of new
    proposed algorithms. Practitioners from a broad variety of fields could
    benefit from the general framework provided by 'mlmts'.
Convenience functions and datasets to be used with Practical Multilevel Modeling using R. The package includes functions for calculating group means, group mean centered variables, and displaying some basic missing data information. A function for computing robust standard errors for linear mixed models based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and 'McCaffrey' (2002) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ> is included as well as a function for checking for level-one homoskedasticity (Raudenbush & Bryk, 2002, ISBN:076191904X).
Maximum likelihood estimation of random utility discrete
             choice models. The software is described in Croissant (2020)
              <doi:10.18637/jss.v095.i11> and the underlying methods in
              Train (2009) <doi:10.1017/CBO9780511805271>.
Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL.
A fast, flexible machine learning library, written in C++, that
             aims to provide fast, extensible implementations of cutting-edge
             machine learning algorithms.  See also Curtin et al. (2018)
             <doi:10.21105/joss.00726>.
Provides functions for obtaining estimates of the parameter of the niche preemption model (also known as the geometric series), in particular a maximum likelihood estimator (Graffelman, 2021) <doi:10.1101/2021.01.27.428381>. The niche preemption model is a widely used model in ecology and biodiversity studies.
An implementation of classifier chains (CC's) for multi-label
    prediction. Users can employ an external package (e.g. 'randomForest',
    'C50'), or supply their own. The package can train a single set of CC's or
    train an ensemble of CC's -- in parallel if running in a multi-core
    environment. New observations are classified using a Gibbs sampler since
    each unobserved label is conditioned on the others. The package includes
    methods for evaluating the predictions for accuracy and aggregating across
    iterations and models to produce binary or probabilistic classifications.
We implement a surrogate modeling algorithm to guide simulation-based
    sample size planning. The method is described in detail in a recent preprint
    (Zimmer & Debelak (2022) <doi:10.31234/osf.io/tnhb2>).
    It supports multiple study design parameters and optimization with respect to a
    cost function. It can find optimal designs that correspond to a desired 
    statistical power or that fulfill a cost constraint.
Quantification is a prominent machine learning task that has received an 
    increasing amount of attention in the last years. The objective is to predict the 
    class distribution of a data sample. This package is a collection of machine learning 
    algorithms for class distribution estimation. This package include algorithms from
    different paradigms of quantification. These methods are described in the paper: 
    A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set 
    size in quantification assessment. In Proceedings of the Twenty-Ninth International 
    Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020.
    <doi:10.24963/ijcai.2020/366>.
Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There
    is also an experimental extension for survival analysis, clustering
    and general, example-specific cost-sensitive learning. Generic
    resampling, including cross-validation, bootstrapping and subsampling.
    Hyperparameter tuning with modern optimization techniques, for single-
    and multi-objective problems.  Filter and wrapper methods for feature
    selection. Extension of basic learners with additional operations
    common in machine learning, also allowing for easy nested resampling.
    Most operations can be parallelized.
Efficient, object-oriented programming on the
    building blocks of machine learning. Provides 'R6' objects for tasks,
    learners, resamplings, and measures. The package is geared towards
    scalability and larger datasets by supporting parallelization and
    out-of-memory data-backends like databases. While 'mlr3' focuses on
    the core computational operations, add-on packages provide additional
    functionality.
Implements methods for post-hoc analysis and
    visualisation of benchmark experiments, for 'mlr3' and beyond.
Extends the 'mlr3' package with cluster analysis.
A small collection of interesting and educational machine
    learning data sets which are used as examples in the 'mlr3' book
    (<https://mlr3book.mlr-org.com>), the use case gallery
    (<https://mlr3gallery.mlr-org.com>), or in other examples. All data
    sets are properly preprocessed and ready to be analyzed by most
    machine learning algorithms.  Data sets are automatically added to the
    dictionary of tasks if 'mlr3' is loaded.
Extends the 'mlr3' package with a backend to
    transparently work with databases such as 'SQLite', 'DuckDB', 'MySQL',
    'MariaDB', or 'PostgreSQL'. The package provides two additional backends:
    'DataBackendDplyr' relies on the abstraction of package 'dbplyr' to
    interact with most DBMS. 'DataBackendDuckDB' operates on 'DuckDB' data bases
    and also on Apache Parquet files.
Integrates fairness auditing and bias mitigation methods for the 'mlr3' ecosystem.
    This includes fairness metrics, reporting tools, visualizations and bias mitigation techniques such as
    "Reweighing" described in 'Kamiran, Calders' (2012) <doi:10.1007/s10115-011-0463-8>  and
    "Equalized Odds" described in 'Hardt et al.' (2016) <https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>.
    Integration with 'mlr3' allows for auditing of ML models as well as convenient joint tuning of
    machine learning algorithms and debiasing methods.
Extends 'mlr3' with filter methods for feature selection.
    Besides standalone filter methods built-in methods of any
    machine-learning algorithm are supported.  Partial scoring of
    multivariate filter methods is supported.
Implements methods for feature selection with
    'mlr3', e.g.  random search and sequential selection. Various
    termination criteria can be set and combined. The class
    'AutoFSelector' provides a convenient way to perform nested resampling
    in combination with 'mlr3'.
Implements hyperband method for hyperparameter
    tuning.  Various termination criteria can be set and combined. The
    class 'AutoTuner' provides a convenient way to perform nested
    resampling in combination with 'mlr3'. The hyperband algorithm was
    proposed by Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin
    Rostamizadeh and Ameet Talwalkar (2018) <arXiv:1603.06560>.
Recommended Learners for 'mlr3'. Extends 'mlr3'
    with interfaces to essential machine learning packages on
    CRAN.  This includes, but is not limited to: (penalized) linear and
    logistic regression, linear and quadratic discriminant analysis,
    k-nearest neighbors, naive Bayes, support vector machines, and
    gradient boosting.
Implements multiple performance measures for
    supervised learning.  Includes over 40 measures for regression and
    classification. Additionally, meta information about the performance
    measures can be queried, e.g. what the best and worst possible
    performances scores are.
Frequently used helper functions and assertions used in
    'mlr3' and its companion packages. Comes with helper functions for
    functional programming, for printing, to work with 'data.table', as
    well as some generally useful 'R6' classes. This package also
    supersedes the package 'BBmisc'.
Provides an interface to 'OpenML.org' to list and download
    machine learning data, tasks and experiments. The 'OpenML' objects can be
    automatically converted to 'mlr3' objects.
    For a more sophisticated interface which also allows uploading to 'OpenML',
    see the 'OpenML' package.
Dataflow programming toolkit that enriches 'mlr3' with a diverse
  set of pipelining operators ('PipeOps') that can be composed into graphs.
  Operations exist for data preprocessing, model fitting, and ensemble
  learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can
  therefore be resampled, benchmarked, and tuned.
A web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the 'mlr3' framework.
Extends the 'mlr3' ML framework with methods for spatial
    objects. Data storage and prediction are supported for packages
    'terra', 'raster' and 'stars'.
Implements methods for hyperparameter tuning with 'mlr3',
    e.g. grid search, random search, generalized simulated annealing and
    iterated racing.  Various termination criteria can be set and
    combined.  The class 'AutoTuner' provides a convenient way to perform
    nested resampling in combination with 'mlr3'.
Collection of search spaces for hyperparameter tuning.
    Includes various search spaces that can be directly applied on an
    `mlr3` learner. Additionally, meta information about the search space
    can be queried.
The 'mlr3' package family is a set of packages for
    machine-learning purposes built in a modular fashion. This wrapper
    package is aimed to simplify the installation and loading of the core
    'mlr3' packages. Get more information about the 'mlr3' project at
    <https://mlr3book.mlr-org.com/>.
Provides visualizations for 'mlr3' objects such as tasks,
    predictions, resample results or benchmark results via the autoplot()
    generic of 'ggplot2'. The returned 'ggplot' objects are intended to
    provide sensible defaults, yet can easily be customized to create
    camera-ready figures. Visualizations include barplots, boxplots,
    histograms, ROC curves, and Precision-Recall curves.
Toolset that enriches 'mlr' with a diverse set of preprocessing
    operators. Composable Preprocessing Operators ("CPO"s) are first-class
    R objects that can be applied to data.frames and 'mlr' "Task"s to modify
    data, can be attached to 'mlr' "Learner"s to add preprocessing to machine
    learning algorithms, and can be composed to form preprocessing pipelines.
The 'mlrMBO' package can ordinarily not be used for optimization within 'mlr3', because of
  incompatibilities of their respective class systems. 'mlrintermbo' offers a compatibility
  interface that provides 'mlrMBO' as an 'mlr3tuning' 'Tuner' object, for tuning of machine
  learning algorithms within 'mlr3', as well as a 'bbotk' 'Optimizer' object for optimization
  of general objective functions using the 'bbotk' black box optimization framework. The
  control parameters of 'mlrMBO' are faithfully reproduced as a 'paradox' 'ParamSet'.
Flexible and comprehensive R toolbox for model-based optimization
    ('MBO'), also known as Bayesian optimization. It implements the Efficient
    Global Optimization Algorithm and is designed for both single- and multi-
    objective optimization with mixed continuous, categorical and conditional
    parameters. The machine learning toolbox 'mlr' provide dozens of regression
    learners to model the performance of the target algorithm with respect to
    the parameter settings. It provides many different infill criteria to guide
    the search process. Additional features include multi-point batch proposal,
    parallel execution as well as visualization and sophisticated logging
    mechanisms, which is especially useful for teaching and understanding of
    algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that
    single components can be easily replaced or adapted by the user for specific
    use cases.
The stepwise regression with assumptions checking and the possible Box-Cox transformation.
Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs).
Generate a stream of pseudo-random numbers generated using the MLS 
    Junk Generator algorithm. Functions exist to generate single pseudo-random 
    numbers as well as a vector, data frame, or matrix of pseudo-random numbers.
Likelihood-based estimation of conditional transformation
  models via the most likely transformation approach described in
  Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020)
  <DOI:10.18637/jss.v092.i01>.
Additional documentation, a package vignette and 
  regression tests for package mlt.
A fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix.
A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.
Estimates the multi-level vector autoregression model on time-series data.
             Three network structures are obtained: temporal networks, contemporaneous
             networks and between-subjects networks.
Simulation, inference and clustering of multilevel networks using a
    Stochastic Block Model framework as described in Chabert-Liddell, Barbillon,
    Donnet and Lazega (2021) <doi:10.1016/j.csda.2021.107179>. 
    A multilevel network is defined as the junction of two interaction networks, 
    the upper level or inter-organizational level and the lower level or 
    inter-individual level. The inter-level represents an affiliation 
    relationship.
Simulation and visualization of complex
    models for longitudinal data. The models are encoded using the model coding
    language 'Mlxtran' and automatically converted into C++ codes. 
    That allows one to implement very easily complex ODE-based models and complex 
    statistical models, including mixed effects models, for continuous, count, 
    categorical, and time-to-event data.
Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.
Various utilities for the Multiplicative Multinomial distribution.
A single-sample classifier that generates Medulloblastoma (MB) subtype predictions for single-samples of human Medulloblastoma (MB) patients and model systems, including cell lines and mouse-models. The MM2S algorithm uses a systems-based methodology that facilitates application of the algorithm on samples irrespective of their platform or source of origin. MM2S demonstrates > 96% accuracy for patients of well-characterized normal cerebellum, Wingless (WNT), or Sonic hedgehog (SHH) subtypes, and the less-characterized Group4 (86%) and Group3 (78.2%). MM2S also enables classification of MB cell lines and mouse models into their human counterparts.This package contains function for implementing the classifier onto human data and mouse data, as well as graphical rendering of the results as PCA (Principal Component Analysis) plots and heatmaps. Deena Gendoo and Benjamin Haibe-Kains (2016) <doi:10.1186/s13029-016-0053-y>.
Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.
The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Laporte, F., Charcosset, A. & Mary-Huard, T. (2022) <doi:10.1371/journal.pcbi.1009659>).
Used for general multiple mediation analysis. 
	The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.
Used for general multiple mediation analysis with big data sets.
Contains the data sets for the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The book will be published by Oxford University Press in 2018 with ISBN-13: 978-019882472.
Provides tools for performing mathematical morphology operations,
    such as erosion and dilation, on data of arbitrary dimensionality. Can also
    be used for finding connected components, resampling, filtering, smoothing
    and other image processing-style operations.
R interface to POSIX mmap and Window's MapViewOfFile.
Uses memory-mapping to enable the random access of elements of
  a text file of characters separated by characters as if it were a simple
  R(cpp) matrix.
Mobile-monitoring or "sensors on a mobile platform", is an increasingly 
    popular approach to measure high-resolution pollution data at the street level. 
    Coupled with location data, spatial visualisation of air-quality parameters 
    helps detect localized areas of high air-pollution, also called hotspots. 
    In this approach, portable sensors are mounted on a vehicle and driven on 
    predetermined routes to collect high frequency data (1 Hz). 
    'mmaqshiny' is for analysing, visualising and spatial mapping of 
    high-resolution air-quality data collected by specific devices installed on 
    a moving platform. 1 Hz data of PM2.5 (mass concentrations of particulate  
    matter with size less than 2.5 microns), Black carbon mass concentrations 
    (BC), ultra-fine particle number concentrations, carbon dioxide along with 
    GPS coordinates and relative humidity (RH) data collected by popular 
    portable instruments (TSI DustTrak-8530, Aethlabs microAeth-AE51, TSI CPC3007, 
    LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively). It 
    incorporates device specific cleaning and correction algorithms. RH correction 
    is applied to DustTrak PM2.5 following the Chakrabarti et al., (2004) 
    <doi:10.1016/j.atmosenv.2004.03.007>. Provision is given to add linear 
    regression coefficients for correcting the PM2.5 data (if required). BC data
    will be cleaned for the vibration generated noise, by adopting the statistical 
    procedure as explained in Apte et al., (2011) <doi:10.1016/j.atmosenv.2011.05.028>, 
    followed by a loading correction as suggested by Ban-Weiss et al., (2009)  
    <doi:10.1021/es8021039>. For the number concentration data, provision is 
    given for dilution correction factor (if a diluter is used with CPC3007; 
    default value is 1). The package joins the raw, cleaned and corrected data 
    from the above said instruments and outputs as a downloadable csv file.
Supports Bayesian models with full and partial (hence
    arbitrary) dependencies between random variables. Discrete and continuous
    variables are supported, and conditional joint probabilities and probability
    densities are estimated using Kernel Density Estimation (KDE). The full
    general form, which implements an extension to Bayes' theorem, as well as
    the simple form, which is just a Bayesian network, both support regression
    through segmentation and KDE and estimation of probability or relative
    likelihood of discrete or continuous target random variables. This package
    also provides true statistical distance measures based on Bayesian models.
    Furthermore, these measures can be facilitated on neighborhood searches,
    and to estimate the similarity and distance between data points.
    Related work is by Bayes (1763) <doi:10.1098/rstl.1763.0053>
    and by Scutari (2010) <doi:10.18637/jss.v035.i03>.
Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.
Fits the mixed cumulative incidence functions model suggested by 
    <doi:10.1093/biostatistics/kxx072> which decomposes within cluster 
    dependence of risk and timing. The estimation method supports computation in 
    parallel using a shared memory C++ implementation. A sandwich estimator of the 
    covariance matrix is available. Natural cubic splines are used to provide a 
    flexible model for the cumulative incidence functions.
An implementation of modified maximum contrast methods (Sato et al. (2009) <doi:10.1038/tpj.2008.17>;
  Nagashima et al. (2011) <doi:10.2202/1544-6115.1560>) and the maximum contrast method (Yoshimura et al. (1997)
  <doi:10.1177/009286159703100213>): Functions mmcm.mvt() and mcm.mvt() give P-value by using randomized quasi-Monte
  Carlo method with pmvt() function of package 'mvtnorm', and mmcm.resamp() gives P-value by using a permutation method.
The aim of the package is two-fold: (i) To implement 
    the MMD method for attribution of individuals to sources using 
    the Hamming distance between multilocus genotypes. (ii) To select 
    informative genetic markers based on information theory concepts 
    (entropy, mutual information and redundancy). The package implements the 
    functions introduced by Perez-Reche, F. J., Rotariu, O., Lopes, B. S., 
    Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence data to 
    efficiently attribute individuals to source populations. 
    Scientific Reports 10, 12124 (2020) <doi:10.1038/s41598-020-68740-6>.
    See more details and examples in the README file.
A method to impute the missingness in categorical data. Details see the paper <doi:10.4310/SII.2020.v13.n1.a2>.
Provides functions for the robust estimation of 
	parametric families of copulas using minimization of 
	the Maximum Mean Discrepancy, following the article
	Alquier, Chérief-Abdellatif, Derumigny and Fermanian (2022)
	<doi:10.1080/01621459.2021.2024836>.
Gene selection based on variance using the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions. Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) <doi:10.1155/2018/6591634>.
Fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one
    random effect in each category of the response variable (Lopez-Vizcaino,E. et al., 2013) <doi:10.1177/1471082X13478873>; Model 2, introducing
    independent time effect; Model 3, introducing correlated time effect.
    mme calculates direct and parametric bootstrap MSE estimators (Lopez-Vizcaino,E et al., 2014) <doi:10.1111/rssa.12085>.
Fit multivariate mixture of normal distribution using
        covariance structure.
Analyzing data under multivariate mixed effects model using multivariate REML and multivariate Henderson3 methods. 
  See Meyer (1985) <doi:10.2307/2530651> and Wesolowska Janczarek (1984) <doi:10.1002/bimj.4710260613>.
Implements a computational framework to predict microbial community-based 
     metabolic profiles with 'O2PLS' model. It provides procedures of model 
     training and prediction. Paired microbiome and metabolome data are needed 
     for modeling, and the trained model can be applied to predict metabolites 
     of analogous environments using new microbial feature abundances.
A set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space.    
    Model is proposed by Alexander Andronov (2012) <arXiv:1901.09600v1> and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition.
    Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix.
    These models have significant differences in terms of the analytical approach.
    Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes).
    Research project No. 1.1.1.2/VIAA/1/16/075.
Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression
	coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package.
Provides functions for measuring
    population divergence from genotypic data.
Perform multivariate modeling of evolved traits, with special attention to
 understanding the interplay of the multi-factorial determinants of their origins
 in complex ecological settings (Stephens, 2007 <doi:10.1016/j.tree.2006.12.003>).
 This software primarily concentrates on phylogenetic regression analysis, enabling
 implementation of tree transformation averaging and visualization functionality.
 Functions additionally support information theoretic approaches
 (Grueber, 2011 <doi:10.1111/j.1420-9101.2010.02210.x>;
 Garamszegi, 2011 <doi:10.1007/s00265-010-1028-7>)
 such as model averaging and selection of phylogenetic models.
 There are other numerous functions for visualizing confounded variables,
 plotting phylogenetic trees, as well as reporting and exporting modeling results.
 Lastly, as challenges to ecology are inherently multifarious, and therefore often
 multi-dataset, this package features several functions to support the identification,
 interpolation, merging, and updating of missing data and outdated nomenclature.
To determine the number of quantitative assays needed for a sample 
    of data using pooled testing methods, which include mini-pooling (MP), MP 
    with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number 
    of assays needed, the package also provides a tool to conduct Monte Carlo (MC) 
    to simulate different orders in which the sample would be collected to form pools. 
    Using MC avoids the dependence of the estimated number of assays on any specific 
    ordering of the samples to form pools.
Marginalizes prediction functions using Monte-Carlo integration and computes permutation importance.
Compute similarities and distances between marked point processes.
Simple helpers for matrix multiplication on data.frames. These allow for more
    concise code during low level mathematical operations, and help ensure code is more
    easily read, understood, and serviced.
Researchers doing a mixed-methods analysis (nested analysis as
    developed by Lieberman (2005) <doi:10.1017/S0003055405051762>) can
    use the package for the classification of cases and case selection using
    results of a linear regression. One can designate cases 
    as typical, deviant, extreme and pathway case and use different case 
    selection strategies for the choice of a case belonging to one of
    these types.
Mixed models for repeated measures (MMRM) are a popular
    choice for analyzing longitudinal continuous outcomes in randomized
    clinical trials and beyond; see Cnaan, Laird and Slasor (1997)
    <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>
    for a tutorial and Mallinckrodt, Lane and Schnell (2008)
    <doi:10.1177/009286150804200402> for a review. This package implements
    MMRM based on the marginal linear model without random effects using
    Template Model Builder ('TMB') which enables fast and robust model
    fitting. Users can specify a variety of covariance matrices, weight
    observations, fit models with restricted or standard maximum
    likelihood inference, perform hypothesis testing with Satterthwaite
    adjusted degrees of freedom, and extract least square means estimates
    by using 'emmeans'.
Subset a control group to match an intervention group on a set of features using multivariate matching and propensity score calipers. Based on methods in Rosenbaum and Rubin (1985).
An implementation of multiple maps t-distributed stochastic
    neighbor embedding (t-SNE). Multiple maps t-SNE is a method for
    projecting high-dimensional data into several low-dimensional maps such that
    non-metric space properties are better preserved than they would be by a single
    map. Multiple maps t-SNE with only one map is equivalent to standard t-SNE.
    When projecting onto more than one map, multiple maps t-SNE estimates a set
    of latent weights that allow each point to contribute to one or more maps
    depending on similarity relationships in the original data. This
    implementation is a port of the original 'Matlab' library by Laurens van der
    Maaten. 
    See Van der Maaten and Hinton (2012) <doi:10.1007/s10994-011-5273-4>.
    This material is based upon work supported by the United States Air Force 
    and Defense Advanced Research Project Agency (DARPA) under Contract No. 
    FA8750-17-C-0020.
    Any opinions, findings and conclusions or recommendations expressed in this
    material are those of the author(s) and do not necessarily reflect the views 
    of the United States Air Force and Defense Advanced Research Projects Agency.
    Distribution Statement A: Approved for Public Release; Distribution Unlimited.
A variable selection tool for multivariate normal variables with missing-at-random values using Bayesian Hierarchical Model.
    Visualization functions show the posterior distribution of gamma (inclusion variables) and beta (coefficients). 
    Users can also visualize the heatmap of the posterior mean of covariance matrix. 
    Kim, T. Nicolae, D. (2019) <https://github.com/tk382/MMVBVS/blob/master/workingpaper.pdf>.
    Guan, Y. Stephens, M. (2011) <doi:10.1214/11-AOAS455>.
The first day of any MMWR week is Sunday.
    MMWR week numbering is sequential beginning with 1
    and incrementing with each week to a maximum of 52
    or 53. MMWR week #1 of an MMWR year is the first week
    of the year that has at least four days in the calendar
    year. This package provides functionality to convert
    Dates to MMWR day, week, and year and the reverse.
Clustering of data under a non-ignorable missingness mechanism. Clustering is achieved by a semi-parametric mixture model and missingness is managed by using the pattern-mixture approach. More details of the approach are available in Du Roy de Chaumaray et al. (2020) <arXiv:2009.07662>.
Diagnostic tools as residual analysis, global, 
    local and total-local influence for the multivariate model 
    from the random intercept Poisson generalized log gamma model 
    are available in this package. Including also, the estimation 
    process by maximum likelihood method, for details see 
    Fabio, L. F; Villegas, C. L.; Carrasco, J.M.F and de Castro, M. (2021) 
    <doi:10.1080/03610926.2021.1939380>.
An API package for the Members' Name Information Service operated 
    by the UK parliament. Documentation for the API itself can be found 
    here: <http://data.parliament.uk/membersdataplatform/default.aspx>.
Density evaluation and random number generation for the Matrix-Normal Inverse-Wishart (MNIW) distribution, as well as the the Matrix-Normal, Matrix-T, Wishart, and Inverse-Wishart distributions.  Core calculations are implemented in a portable (header-only) C++ library, with matrix manipulations using the 'Eigen' library for linear algebra.  Also provided is a Gibbs sampler for Bayesian inference on a random-effects model with multivariate normal observations.
Perform FlexBoost in R. FlexBoost is a newly suggested algorithm based on AdaBoost by adjusting adaptive loss functions. Not only FlexBoost but also other machine learning algorithms (e.g. Support Vector Machines) will be added. For more details on FlexBoost see Jeon, Y. S., Yang, D. H., & Lim, D. J. (2019) <doi:10.1109/access.2019.2938356>.
Conducts moderated nonlinear factor analysis (e.g., Curran et al., 2014,
    <doi:10.1080/00273171.2014.889594>). 
    Regularization methods are implemented for assessing non-invariant items. 
    Currently, the package includes dichotomous items and unidimensional
    item response models. Extensions will be included in future package
    versions.
Functions to easily return simulated predicted probabilities and
    first differences for multinomial logit models. It takes a specified 
    scenario and a multinomial model to predict probabilities with a set of
    coefficients, drawn from a simulated sampling distribution. The simulated 
    predictions allow for meaningful plots with means and confidence intervals.
    The methodological approach is based on the principles laid out by King,
    Tomz, and Wittenberg (2000) <doi:10.2307/2669316> and Hanmer and Ozan Kalkan 
    (2016) <doi:10.1111/j.1540-5907.2012.00602.x>.
An interactive presentation on  the topic of Multinomial Logistic Regression. It is helpful to those who want to learn Multinomial Logistic Regression quickly and get a hands on experience. The presentation has a template for solving problems on Multinomial Logistic Regression. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/MultinomPresentation>.
Multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score are provided. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components. The methodology is described in Oja (2010) <doi:10.1007/978-1-4419-0468-3> and Nordhausen and Oja (2011) <doi:10.18637/jss.v043.i05>.
A data generator of multivariate non-normal data in R. It combines two different methods to generate non-normal data, one with user-specified multivariate skewness and kurtosis (more details can be found in the paper: Qu, Liu, & Zhang, 2019 <doi:10.3758/s13428-019-01291-5>), and the other with the given marginal skewness and kurtosis. The latter one is the widely-used Vale and Maurelli's method. It also contains a function to calculate univariate and multivariate (Mardia's Test) skew and kurtosis.
Calculates and differentiates probabilities and density of (conditional) multivariate normal distribution using methods described in A. Genz (2004) <doi:10.1023/B:STCO.0000035304.20635.31>, A. Genz, F. Bretz (2009) <doi:10.1007/978-3-642-01689-9> and E. Kossova, B. Potanin (2018) <https:ideas.repec.org/a/ris/apltrx/0346.html>.
Functions are provided for computing the density and the
  distribution function of d-dimensional normal and "t" random variables,
  possibly truncated (on one side or two sides),  and for generating random 
  vectors sampled from these distributions, except sampling from the truncated
  "t". Moments of arbitrary order of a multivariate truncated normal are
  computed, and converted to cumulants up to order 4. 
  Probabilities are computed via non-Monte Carlo methods; different routines 
  are used in the case d=1, d=2, d=3, d>3, if d denotes the dimensionality.
Fits the Bayesian multinomial probit model via Markov chain
 Monte Carlo.  The multinomial probit model is often used to analyze 
 the discrete choices made by individuals recorded in survey data. 
 Examples where the multinomial probit model may be useful include the 
 analysis of product choice by consumers in market research and the 
 analysis of candidate or party choice by voters in electoral studies.  
 The MNP package can also fit the model with different choice sets for 
 each individual, and complete or partial individual choice orderings 
 of the available alternatives from the choice set. The estimation is
 based on the efficient marginal data augmentation algorithm that is 
 developed by Imai and van Dyk (2005). "A Bayesian Analysis of the 
 Multinomial Probit Model Using the Data Augmentation." Journal of 
 Econometrics, Vol. 124, No. 2 (February), pp. 311-334. 
 <doi:10.1016/j.jeconom.2004.02.002>  Detailed examples are given in 
 Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial 
 Probit Model."  Journal of Statistical Software, Vol. 14, No. 3 (May), 
 pp. 1-32. <doi:10.18637/jss.v014.i03>.
Allows to analyze the reading data obtained with the MNREAD Acuity 
    Chart, a continuous-text reading acuity chart for normal and low vision. 
    Provides the necessary functions to plot the MNREAD curve and estimate 
    automatically the four MNREAD parameters: Maximum Reading Speed, 
    Critical Print Size, Reading Acuity and Reading Accessibility Index.
    Parameters can be estimated either with the standard method 
    or with a nonlinear mixed-effects (NLME) modeling. 
    See Calabrese et al. 2018 for more details <doi:10.1167/18.1.8>.
An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.
Various affine invariant multivariate normality tests are provided. It is designed to accompany the survey article Ebner, B. and Henze, N. (2020) <arXiv:2004.07332> titled "Tests for multivariate normality -- a critical review with emphasis on weighted L^2-statistics". We implement new and time honoured L^2-type tests of multivariate normality, such as the Baringhaus-Henze-Epps-Pulley (BHEP) test, the Henze-Zirkler test, the test of Henze-Jiménes-Gamero, the test of Henze-Jiménes-Gamero-Meintanis, the test of Henze-Visage, the Dörr-Ebner-Henze test based on harmonic oscillator and the Dörr-Ebner-Henze test based on a double estimation in a PDE. Secondly, we include the measures of multivariate skewness and kurtosis by Mardia, Koziol, Malkovich and Afifi and Móri, Rohatgi and Székely, as well as the associated tests. Thirdly, we include the tests of multivariate normality by Cox and Small, the 'energy' test of Székely and Rizzo, the tests based on spherical harmonics by Manzotti and Quiroz and the test of Pudelko. All the functions and tests need the data to be a n x d matrix where n is the samplesize (number of rows) and d is the dimension (number of columns).
Generate the monotonic binning and
    perform the woe (weight of evidence) transformation for the logistic regression
    used in the consumer credit scorecard development. The woe transformation is a piecewise
    transformation that is linear to the log odds. For a numeric variable, all of its monotonic 
    functional transformations will converge to the same woe transformation.
A framework for interacting with
  R modules such as Reports, Models, and Scripts from a mobile device. 
  The framework allows you to list available modules and select a
  module of interest using a basic e-mail interface. After 
  selecting a specific module, you can either run it as is or 
  provide input via the e-mail interface. After parsing your
  request, R will send the results back to your mobile device.
Measures mobility in a population through transition matrices and mobility indices. Relative, mixed, and absolute transition matrices are supported. The Prais-Bibby, Absolute Movement, Origin Specific, and Weighted Group Mobility indices are supported. Example income and grade data are included.
Models the dependence between two variables in the extremes, identifies most 
    relevant models among six models: the conditional extremes model, the Jt-KDE model 
    and four copulae (Gumbel, Galambos, Normal, FGM). Bivariate return periods for 
    the six models and bivariate level curves can be created.
    Methods used in the package are described in the following reference:
    Tilloy, Malamud, Winter and Joly-Laugel (2020) <doi:10.5194/nhess-20-2091-2020>
    Supporting references for the conditional extremes model,
    Jt-KDE model and for copula modelling are the following: 
    Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>
    Cooley, Thibaud, Castillo and Wehner (2019) <doi:10.1007/s10687-019-00348-0>
    Nelsen (2006) <doi:10.1007/0-387-28678-0>.
Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. The package is also used as the background simulator for our a web-based interface <http:www.mobps.de>. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.
Functions for calculating metrics for the measurement biodiversity
    and its changes across scales, treatments, and gradients. The methods 
    implemented in this package are described in:
    Chase, J.M., et al. (2018) <doi:10.1111/ele.13151>, 
    McGlinn, D.J., et al. (2019) <doi:10.1111/2041-210X.13102>, and
    McGlinn, D.J., et al. (2021) <doi:10.1002/ecy.3233>.
Provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.
The two main functionalities of this package are creating
    mock objects (functions) and selectively intercepting calls to a given
    function that originate in some other function. It can be used with
    any testing framework available for R. Mock objects can be injected
    with either this package's own stub() function or a similar
    with_mock() facility present in the 'testthat' package.
Provides a means to mock a package function, i.e.,
    temporarily substitute it for testing. Designed as a drop-in
    replacement for the now deprecated 'testthat::with_mock()' and
    'testthat::local_mock()'.
With the deprecation of mocking capabilities shipped with
    'testthat' as of 'edition 3' it is left to third-party packages to replace
    this functionality, which in some test-scenarios is essential in order to
    run unit tests in limited environments (such as no Internet connection).
    Mocking in this setting means temporarily substituting a function with a
    stub that acts in some sense like the original function (for example by
    serving a HTTP response that has been cached as a file). The only exported
    function 'with_mock()' is modeled after the eponymous 'testthat' function
    with the intention of providing a drop-in replacement.
Creates modules inline or from a file. Modules can contain any R object and be nested. Each module have their own scope and package "search path" that does not interfere with one another or the user's working environment.
Package for processing downloaded MODIS Surface reflectance
    Product HDF files. Specifically, MOD09 surface reflectance product files, and
    the associated MOD03 geolocation files (for MODIS-TERRA). The package will be
    most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath
    products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and
    adds the directory with the MRTSwath executable to the default R PATH by editing
    ~/.Rprofile.
Multiple moderation analysis for two-instance repeated measures designs, with up to three simultaneous moderators (dichotomous and/or continuous) with additive or multiplicative relationship. Includes analyses of simple slopes and conditional effects at (automatically determined or manually set) values of the moderator(s), as well as an implementation of the Johnson-Neyman procedure for determining regions of significance in single moderator models. Based on Montoya, A. K. (2018) "Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models" <doi:10.3758/s13428-018-1088-6> .
Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors.  These model-based non-parametric clustering techniques can extract  clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.
This is a 'shiny' module that encapsulates various charting options available in 'htmlwidgets', and provides options for each type of chart, a 'crosstalk' like interface for aggregate reports between 'DT' and other chart types.
Provides estimators of the mode of univariate
    data or univariate distributions.
Given independent and identically distributed observations X(1), ..., X(n) from a density f,
 provides five methods to perform a multiscale analysis about f as well as the necessary critical
 values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements
 for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by
 any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of
 I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive
 correction term Gamma in either method offers another two approaches which are more powerful on small scales and
 less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a
 compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically
 equivalent to the first and was introduced in Rufibach and Walther (2010).
Model-based trees for subgroup analyses in clinical trials and
  model-based forests for the estimation and prediction of personalised
  treatment effects (personalised models). Currently partitioning of linear
  models, lm(), generalised linear models, glm(), and Weibull models,
  survreg(), is supported.  Advanced plotting functionality is supported for
  the trees and a test for parameter heterogeneity is provided for the
  personalised models. For details on model-based trees for subgroup analyses
  see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for
  details on model-based forests for estimation of individual treatment effects
  see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.
Implements a general interface for model-based estimations
    for a wide variety of models (see list of supported models using the
    function 'insight::supported_models()'), used in the computation of
    marginal means, contrast analysis and predictions.
This is a cross-platform linear model to 'SQL' compiler. It generates 'SQL' from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a 'SQL' character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) <doi:10.1007/978-1-4419-0118-7> and originating in Nelder & Wedderburn (1972) <doi:10.2307/2344614>. The resultant 'SQL' can be included in a 'SELECT' statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported.
Provides two important functions for producing Gain chart and Lift chart for any classification model.
Data sets used for demonstrating or testing model-related
    packages are contained in this package.
Uses 'dplyr' and 'tidyeval' to fit statistical models inside
    the database. It currently supports KMeans and linear regression
    models.
Website generator with HTML summaries for predictive models.
    This package uses 'DALEX' explainers to describe global model behavior. 
    We can see how well models behave (tabs: Model Performance, Auditor),
    how much each variable contributes to predictions (tabs: Variable Response) 
    and which variables are the most important for a given model (tabs: Variable Importance).
    We can also compare Concept Drift for pairs of models (tabs: Drifter).
    Additionally, data available on the website can be easily recreated in current R session.
    Work on this package was financially supported by the NCN Opus grant 2017/27/B/ST6/01307 
    at Warsaw University of Technology, Faculty of Mathematics and Information Science.
An developer focused, low dependency package in 'tidymodels' that
    provides functions to register how models are to be used. Functions to
    register models are complimented with accessor functions to retrieve
    registered model information to aid in model fitting and error handling.
Defines classes and methods to learn models and use them
  to predict binary outcomes.  These are generic tools, but we also
  include specific examples for many common classifiers.
A minimalistic but flexible framework that facilitates the creation,
    management and training of multiple 'caret' models. A model grid consists of
    two components: (1) a set of settings that is shared by all models by 
    default, and (2) specifications that apply only to the individual models.
    When the model grid is trained, model and training specifications are first 
    consolidated from the shared and the model specific settings into complete
    'caret' model configurations. These models are then trained with the 'train' 
    function from the 'caret' package.
Calculate the financial impact of using a churn model in terms of cost, revenue, profit and return on investment.
An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) <doi:10.1007/s11135-013-9884-7> (see also Harden and Desmarais, 2011 <doi:10.1177/1532440011408929>) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) <doi:10.1093/pan/mpr042>. These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) <doi:10.1177/0010414009341714> and Joshi & Mason (2008) <doi:10.1177/0022343308096155> that are included to facilitate examples from real-world analysis.
The model.matrix() function in R is  convenient for transforming training dataset for modeling. 
    But it does not save any parameter used in transformation, so it is hard to apply the same transformation 
    to test dataset or new dataset. This package is created to solve the problem.
Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.
A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions.  In this way, a user can easily go from linear to non-linear models within the same package.
Plots to assess the quality of predictive models from a business perspective. 
    Using these plots, it can be shown how implementation of the model will impact business 
    targets like response on a campaign or return on investment. Different scopes can be selected: 
    compare models, compare datasets or compare target class values and various plot customization 
    and highlighting options are available.
    targets like response on a campaign. Different scopes can be selected: compare models, compare 
    datasets or compare target class values and various plot customization and highlighting options 
    are available.
Functions for modelling that help you seamlessly integrate
    modelling into a pipeline of data manipulation and visualisation.
The ROC curve method is one of the most important and commonly used 
    methods for model accuracy assessment, which is one of the most important elements 
    of model evaluation.
      The 'modelROC' package is a model-based ROC assessment tool, which directly works 
    for ROC analysis of regression results for logistic regression of binary variables, 
    including the glm() and lrm() commands, and COX regression for survival analysis, 
    including the cph() and coxph() commands.
      The most important feature of 'modelROC' is that both the model and the independent 
    variables can be analysed simultaneously, and for survival analysis 
    multiple time points and area under the curve analysis are supported.
      Still, flexible visualisation is possible with the 'ggplot2' package.
      Reference are Kelly H. Zou (1998) <doi:10.1002/(sici)1097-0258(19971015)16:19%3C2143::aid-sim655%3E3.0.co;2-3> and
    P J Heagerty (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.
Automate the explanatory analysis of machine learning predictive 
    models. Generate advanced interactive model explanations in the form of 
    a serverless HTML site with only one line of code. This tool is 
    model-agnostic, therefore compatible with most of the black-box predictive
    models and frameworks. The main function computes various (instance and 
    model-level) explanations and produces a customisable dashboard, which 
    consists of multiple panels for plots with their short descriptions. It is 
    possible to easily save the dashboard and share it with others. Tools for 
    Explanatory Model Analysis unite with tools for Exploratory Data Analysis 
    to give a broad overview of the model behavior.
Create beautiful and customizable tables to summarize several
    statistical models side-by-side. Draw coefficient plots, multi-level
    cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and
    correlation matrices. This package supports dozens of statistical models, and
    it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel,
    RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 'knitr'
    dynamic documents. Details can be found in Arel-Bundock (2022)
    <doi:10.18637/jss.v103.i01>.
Provides a number of testthat tests that can be
    used to verify that tidy(), glance() and augment() methods meet
    consistent specifications. This allows methods for the same generic to
    be spread across multiple packages, since all of those packages can
    make the same guarantees to users about returned objects.
The time series forecasting framework for use with the 'tidymodels' ecosystem. 
    Models include ARIMA, Exponential Smoothing, and additional time series models
    from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" 
    (<https://otexts.com/fpp2/>).
    Refer to "Prophet: forecasting at scale" 
    (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).
A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, 
    weighted averaging, and stacking. These techniques are popular methods 
    to improve forecast accuracy and stability. Refer to papers such as 
    "Machine-Learning Models for Sales Time Series Forecasting" Pavlyshenko, B.M. (2019) <doi:10.3390>.
Use the 'GluonTS' deep learning library inside of 'modeltime'.
    Available models include 'DeepAR', 'N-BEATS', and 'N-BEATS' Ensemble. 
    Refer to "GluonTS - Probabilistic Time Series Modeling" 
    (<https://ts.gluon.ai/index.html>).
Use the 'H2O' machine learning library inside of 'modeltime'.
    Available models include 'AutoML' for Automatic Machine Learning. 
    Please see H2O.ai for more information <https://github.com/h2oai/h2o-3>.
A 'modeltime' extension that implements forecast resampling tools
    that assess time-based model performance and stability for a single time series, 
    panel data, and cross-sectional time series analysis.
A collection of tools to deal with statistical models. 
  The functionality is experimental and the user interface is likely to
  change in the future. The documentation is rather terse, but packages `coin'
  and `party' have some working examples. However, if you find the
  implemented ideas interesting we would be very interested in a discussion
  of this proposal. Contributions are more than welcome!
Makes a word cloud of text, sized by the frequency of the word, and colored either by user-specified colors or colored by the strength of the coefficient of that text derived from a regression model.
Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at <https://moderndive.com/>.
Provides functions that fit two modern education-based value-added models.  
                 One of these models is the quantile value-added model.  This model 
                 permits estimating a school's value-added based on specific quantiles of 
                 the post-test distribution.  Estimating value-added based on quantiles 
                 of the post-test distribution provides a more complete picture of an 
                 education institution's contribution to learning for students of all 
                 abilities. See Page, G.L.; San Martín, E.; Orellana, J.; Gonzalez, J. (2017)
                 <doi:10.1111/rssa.12195> for more details.  The second model is a temporally 
                 dependent value-added model. This model takes into account the temporal 
                 dependence that may exist in school performance  between two cohorts in one 
                 of two ways.  The first is by modeling school  random effects with a 
                 non-stationary AR(1) process.  The second is by  modeling school effects 
                 based on previous cohort's post-test performance. In addition to more 
                 efficiently estimating value-added, this model permits making statements 
                 about the persistence of a schools effectiveness. The standard  value-added 
                 model is also an option.
User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian decision procedure based on logistic regression.
Function ModEstM() is the only one of this package, it estimates the modes of an empirical univariate distribution. It relies on the stats::density() function, even for input control. Due to very good performance of the density estimation, computation time is not an issue. The multiple modes are handled using dplyr::group_by(). For conditions and rates of convergences, see Eddy (1980) <doi:10.1214/aos/1176345080>.
Compute important quantities when we consider stochastic systems that are observed continuously. 
             Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. 
             The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press.
Analyses species distribution models and evaluates their performance. It includes functions for performing variation partitioning, calculating several measures of model discrimination and calibration, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. (2013) <doi:10.1111/ddi.12100>.
This is a shiny module that presents a file picker user interface to get an Excel file name, and reads the Excel sheets using 'readxl' package and returns the resulting sheet(s) as a vector and data in dataframe(s).
Algorithms for multivariate outlier detection when missing values occur.
    Algorithms are based on Mahalanobis distance or data depth. Imputation is based
    on the multivariate normal model or uses nearest neighbour donors. The algorithms 
    take sample designs, in particular weighting, into account. The methods are 
    described in Bill and Hulliger (2016) <doi:10.17713/ajs.v45i1.86>.
Power of non-parametric Mann-Kendall test and Spearman’s Rho test is highly influenced by serially correlated data. To address this issue, trend tests may be applied on the modified versions of the time series data by  Block Bootstrapping (BBS), Prewhitening (PW) , Trend Free Prewhitening (TFPW), Bias Corrected Prewhitening and Variance Correction Approach by calculating effective sample size.
    Mann, H. B. (1945).<doi:10.1017/CBO9781107415324.004>.
    Kendall, M. (1975). Multivariate analysis. Charles Griffin&Company Ltd,. 
    sen, P. K. (1968).<doi:10.2307/2285891>.
    Önöz, B., & Bayazit, M. (2012) <doi:10.1002/hyp.8438>.
    Hamed, K. H. (2009).<doi:10.1016/j.jhydrol.2009.01.040>.
    Yue, S., & Wang, C. Y. (2002) <doi:10.1029/2001WR000861>.
    Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002) <doi:10.1002/hyp.1095>.
    Hamed, K. H., & Ramachandra Rao, A. (1998) <doi:10.1016/S0022-1694(97)00125-X>.
    Yue, S., & Wang, C. Y. (2004) <doi:10.1023/B:WARM.0000043140.61082.60>.
Download and processing functionality for the Moderate Resolution
    Imaging Spectroradiometer (MODIS). The package provides automated access to the
    global online data archives LP DAAC (<https://lpdaac.usgs.gov/>), LAADS 
    (<https://ladsweb.modaps.eosdis.nasa.gov/>) and NSIDC (<https://nsidc.org/home>) 
    as well as processing capabilities such as file conversion, mosaicking, 
    subsetting and time series filtering.
Programmatic interface to the Oak Ridge National Laboratories
    'MODIS Land Products Subsets' web services 
    (<https://modis.ornl.gov/data/modis_webservice.html>). Allows for easy
    downloads of 'MODIS' time series directly to your R workspace or
    your computer.
Allows automating the creation of time series of rasters derived
    from MODIS satellite land products data. It performs several typical
    preprocessing steps such as download, mosaicking, reprojecting and resizing
    data acquired on a specified time period. All processing parameters
    can be set using a user-friendly GUI. Users can select which layers of
    the original MODIS HDF files they want to process, which additional
    quality indicators should be extracted from aggregated MODIS quality
    assurance layers and, in the case of surface reflectance products,
    which spectral indexes should be computed from the original reflectance
    bands. For each output layer, outputs are saved as single-band raster
    files corresponding to each available acquisition date. Virtual files
    allowing access to the entire time series as a single file are also created.
    Command-line execution exploiting a previously saved processing options
    file is also possible, allowing users to automatically update time series
    related to a MODIS product whenever a new image is available.
    For additional documentation refer to the following article: 
    Busetto and Ranghetti (2016) <doi:10.1016/j.cageo.2016.08.020>.
Calculate predicted levels and marginal effects,
    using the delta method to calculate standard errors. This is an R-based
    version of the 'margins' command from Stata.
The algorithms implemented here are used to detect the community structure of a network. 
            These algorithms follow different approaches, but are all based on the concept of modularity maximization.
Methods for modeling moderator variables in cross-sectional, temporal, and multi-level networks. Includes model selection techniques and a variety of plotting functions. Implements the methods described by Swanson (2020) <https://www.proquest.com/openview/d151ab6b93ad47e3f0d5e59d7b6fd3d3>.
'MatLab'-Style Modeling of Optimization Problems with 'R'. This package provides a set of convenience functions to transform a 'MatLab'-style optimization modeling structure to its 'ROI' equivalent.
Contains basic tools for performing 
  multiple-output quantile regression and computing
  regression quantile contours by means of directional
  regression quantiles. In the location case, one can thus
  obtain halfspace depth contours in two to six dimensions.
  Hallin, M., Paindaveine, D. and Šiman, M. (2010)
  Multivariate quantiles and multiple-output regression quantiles:
  from L1 optimization to halfspace depth. Annals of Statistics 38, 635-669
  For more references about the method, see Help pages.
Datasets and functions for the book "Modélisation statistique par la pratique avec R", F. Bertrand, E. Claeys and M. Maumy-Bertrand (2019, ISBN:9782100793525, Dunod, Paris). The first chapter of the book is dedicated to an introduction to the R statistical software. The second chapter deals with correlation analysis: Pearson, Spearman and Kendall simple, multiple and partial correlation coefficients. New wrapper functions for permutation tests or bootstrap of matrices of correlation are provided with the package. The third chapter is dedicated to data exploration with factorial analyses (PCA, CA, MCA, MDA) and clustering. The fourth chapter is dedicated to regression analysis: fitting and model diagnostics are detailed. The exercises focus on covariance analysis, logistic regression, Poisson regression, two-way analysis of variance for fixed or random factors. Various example datasets are shipped with the package: for instance on pokemon, world of warcraft, house tasks or food nutrition analyses.
Collection of tools for regression and classification tasks. The package implements a consistent user interface to the most popular regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, and complements it with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.
Turning point method is a method proposed by Choi (1990) <doi:10.2307/2531453> to 
    estimate 50 percent effective dose (ED50) in the study of drug sensitivity. The method has 
    its own advantages for that it can provide robust ED50 estimation. This package contains 
    the modified function of Choi's turning point method.
Methods for color labeling, calculation of eigengenes, merging of closely related modules.
Provides modules as an organizational unit for source code. Modules
    enforce to be more rigorous when defining dependencies and have
    a local search path. They can be used as a sub unit within packages
    or in scripts.
RMSD and Internal Energy analysis of NAMD and YASARA Molecular Dynamics output files. Allows to comparison of different dynamics per different complexes. Input files have to be ASCII files tab separated.
Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.
This is a thin wrapper around the 'MOEX' 'ISS' REST interface, see
    <https://iss.moex.com>.  It allows to quickly fetch price candles for a 
    particular security, obtain its profile information and so on.
Identifying important factors from a large number of potentially important factors of a highly nonlinear and computationally expensive black box model is a difficult problem. Xiao, Joseph, and Ray (2022) <doi:10.1080/00401706.2022.2141897> proposed Maximum One-Factor-at-a-Time (MOFAT) designs for doing this. A MOFAT design can be viewed as an improvement to the random one-factor-at-a-time (OFAT) design proposed by Morris (1991) <doi:10.1080/00401706.1991.10484804>. The improvement is achieved by exploiting the connection between Morris screening designs and Monte Carlo-based Sobol' designs, and optimizing the design using a space-filling criterion. This work is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921646>.
Functions for exploring the best subsets in regression with a genetic algorithm. The package is much faster than methods relying on complete enumeration, and is suitable for data sets with large number of variables. For more information, see Sinha, Malo & Kuosmanen (2015) <doi:10.1080/10618600.2014.899236>.
Support JSON flattening in a long data frame way, where the nesting keys will be stored in the absolute path. It also
   provides an easy way to summarize the basic description of a JSON list. The idea of 'mojson' is to transform a JSON object in an 
   absolute serialization way, which means the early key-value pairs will appear in the heading rows of the resultant data frame. 
   'mojson' also provides an alternative way of comparing two different JSON lists, returning the left/inner/right-join style results.
Contains functions for performing Mokken
   scale analysis on test and questionnaire data.
   It includes an automated item selection algorithm, and various checks of model assumptions.
Multi-Objective optimization based on the Kriging metamodel.
  Important functions: mkm() (builder for the multiobjective models), MVPF() (sequential minimizator using variance reduction), 
  MEGO() (generalization of ParEgo) and HEGO() (minimizator using the expected hypervolume improvement).
  References are Passos and Luersen (2018) <doi:10.1590/1679-78254324>.
Surface topography calculations of Dirichlet's normal energy,
    relief index, surface slope, and orientation patch count for teeth using scans of
    enamel caps.
    Importantly, for the relief index and orientation patch count calculations to
    work, the scanned tooth files must be oriented with the occlusal plane parallel
    to the x and y axes, and perpendicular to the z axis. The files should also be
    simplified, and smoothed in some other software prior to uploading into R.
Model for simulating language evolution in terms of cultural evolution (Smith & Kirby (2008) <DOI:10.1098/rstb.2008.0145>; Deacon 1997). The focus is on the emergence of argument-marking systems (Dowty (1991) <DOI:10.1353/lan.1991.0021>, Van Valin 1999, Dryer 2002, Lestrade 2015a), i.e. noun marking (Aristar (1997) <DOI:10.1075/sl.21.2.04ari>, Lestrade (2010) <DOI:10.7282/T3ZG6R4S>), person indexing (Ariel 1999, Dahl (2000) <DOI:10.1075/fol.7.1.03dah>, Bhat 2004), and word order (Dryer 2013), but extensions are foreseen. Agents start out with a protolanguage (a language without grammar; Bickerton (1981) <DOI:10.17169/langsci.b91.109>, Jackendoff 2002, Arbib (2015) <DOI:10.1002/9781118346136.ch27>) and interact through language games (Steels 1997). Over time, grammatical constructions emerge that may or may not become obligatory (for which the tolerance principle is assumed; Yang 2016). Throughout the simulation, uniformitarianism of principles is assumed (Hopper (1987) <DOI:10.3765/bls.v13i0.1834>, Givon (1995) <DOI:10.1075/z.74>, Croft (2000), Saffran (2001) <DOI:10.1111/1467-8721.01243>, Heine & Kuteva 2007), in which maximal psychological validity is aimed at (Grice (1975) <DOI:10.1057/9780230005853_5>, Levelt 1989, Gaerdenfors 2000) and language representation is usage based (Tomasello 2003, Bybee 2010). In Lestrade (2015b) <DOI:10.15496/publikation-8640>, Lestrade (2015c) <DOI:10.1075/avt.32.08les>, and Lestrade (2016) <DOI:10.17617/2.2248195>), which reported on the results of preliminary versions, this package was announced as WDWTW (for who does what to whom), but for reasons of pronunciation and generalization the title was changed.
A set of functions to be able to manage data shared on a
  'MOLGENIS Armadillo' storage server ('MinIO').
Discover 'OpenID Connect' endpoints and authenticate
    using device flow. Used by 'MOLGENIS' packages.
Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) <doi:10.1016/0378-3758(94)00035-T>, Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) <doi:10.1198/Tech.2011.10087>, Joseph, V. R., Gul, E., and Ba, S. (2015) <doi:10.1093/biomet/asv002>.
Outlier detection in, possibly high-dimensional, categorical data following
	     Mads Lindskou et al. (2019) <doi:10.1111/sjos.12407>.
Networks provide a means to incorporate molecular interactions into
    reasoning, but on the omics-level, they are currently mainly used to combine
    genomic and proteomic information. We here present a novel network analysis
    pipeline that enables integrative analysis of multi-omics data including
    metabolomics. It allows for comparative conclusions between two different
    conditions, such as tumor subgroups, healthy vs. disease, or generally control
    vs. perturbed.
    Our approach focuses on interactions and their strength instead of on node
    properties and includes molecules with low abundance and unknown function. We
    use correlation-induced networks that are reduced and combined to form 
    heterogeneous, multi-omics molecular networks. Prior information such as
    metabolite-protein interactions are incorporated. A semi-local, path-based
    integration step denoises the network and ensures integrative conclusions. As
    case studies, we investigate differential drug response in breast cancer tumor
    datasets providing proteomics, transcriptomics, phospho-proteomics and
    metabolomics data and contrasting patients with different estrogen receptor
    status.
    Our proposed pipeline leverages multi-omics data for differential predictions,
    e.g. on drug response, and includes prior information on interactions.
    The case study presented in the vignette uses data published by
    Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only
    to the software and explicitly not to the included data.
Bayesian model selection and averaging for regression and mixtures for non-local and selected local priors.
A collection of moment-matching methods for computing the cumulative distribution function of a positively-weighted sum of chi-squared random variables. Methods include the Satterthwaite-Welch method, Hall-Buckley-Eagleson method, Wood's F method, and the Lindsay-Pilla-Basak method.
Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), for single equations and systems of equation, and  the Generalized Empirical Likelihood (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>).
Functions to calculate: moments, Pearson's kurtosis,
        Geary's kurtosis and skewness; tests related to them
        (Anscombe-Glynn, D'Agostino, Bonett-Seier).
Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.
The goal of 'Momocs' is to provide a complete, convenient, 
       reproducible and open-source toolkit for 2D morphometrics.
       It includes most common 2D morphometrics approaches on outlines, open outlines, 
       configurations of landmarks, traditional morphometrics, and facilities for data preparation, 
       manipulation and visualization with a consistent grammar throughout.
       It allows reproducible, complex morphometrics analyses and other morphometrics approaches 
       should be easy to plug in, or develop from, on top of this canvas.
Cluster any count data matrix with a fixed number of variables, such as document/term matrices. It integrates the dimension reduction aspect of topic models in the mixture models framework. Inference is done by means of a greedy Classification Variational Expectation Maximisation (C-VEM) algorithm. An Integrated Classication Likelihood (ICL) model selection is designed for selecting the latent dimension (number of topics) and the number of clusters. For more details, see the article of Jouvin et. al. (2020) <arxiv:1909.00721>.
'MetaOMineR' suite is a set of R packages that offers many functions and modules needed for the analyses 
    of quantitative metagenomics data. 'momr' is the core package and contains routines for biomarker identification and exploration.
    Developed since the beginning of field, 'momr' has evolved and is structured around the different modules 
    such as preprocessing, analysis, vizualisation, etc. See package help for more information.
It computes arbitrary products moments (mean vector and variance-covariance matrix), for some double truncated (and folded) multivariate distributions. These distributions belong to the family of selection elliptical distributions, which includes well known skewed distributions as the unified skew-t distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t (ST) and the symmetric student-t (T) distribution. Analogous normal cases unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and symmetric normal (N) are also included. Density, probabilities and random deviates are also offered for these members.
A HTML widget rendering the 'Monaco' editor. The 'Monaco' editor is the code editor which powers 'VS Code'. It is particularly well developed for 'JavaScript'. In addition to the built-in features of the 'Monaco' editor, the widget allows to prettify multiple languages, to view the 'HTML' rendering of 'Markdown' code, and to view and resize 'SVG' images.
An API wrapper for the 'Monash University Probabilistic Footy 
    Tipping Competition' <https://probabilistic-footy.monash.edu/~footy/index.shtml>. 
    Allows users to submit tips directly to the competition from R.
Keep track of dates in terms of fractional calendar months 
  per Damien Laker "Time Calculations for Annualizing Returns: the Need for Standardization", 
  The Journal of Performance Measurement, 2008.
  Model dates as of close of business.
  Perform date arithmetic in units of "months" and "years".
  Allow "infinite" dates to model "ultimate" time.
The unique function of this package allows representing in a single graph the relative occurrence and co-occurrence of events measured in a sample. 
  As examples, the package was applied to describe the occurrence and co-occurrence of different species of bacterial or viral symbionts infecting arthropods at the individual level. The graphics allows determining the prevalence of each symbiont and the patterns of multiple infections (i.e. how different symbionts share or not the same individual hosts). 
  We named the package after the famous painter as the graphical output recalls Mondrian’s paintings.
Allows to pull data from MonetDB into R.
High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'.
    Includes support for aggregation, indexing, map-reduce, streaming, encryption,
    enterprise authentication, and GridFS. The online user manual provides an overview 
    of the available methods in the package: <https://jeroen.github.io/mongolite/>.
Translate R code into MongoDB aggregation pipelines.
Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.
Train and make predictions from a multi-layer perceptron neural
        network with optional partial monotonicity constraints.
Performs monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) 
	development. All functions handle both binary and continuous target variable. 
	Functions that use isotonic regression in the first stage of binning process have an additional 
	feature for correction of minimum percentage of observations and minimum target rate per bin. 	
	Additionally, monotonic trend can be identified based on raw data or, if known in advance,
	forced by functions' argument. Missing values and other possible special values are treated 
	separately from so-called complete cases.
This is an add-on package to the 'monobin' package that simplifies its use. It provides shiny-based user interface (UI) 
	     that is especially handy for less experienced 'R' users as well as for those who intend to perform quick scanning 
	     of numeric risk factors when building credit rating models. The additional functions implemented in 
	     'monobinShiny' that do no exist in 'monobin' package are: descriptive statistics, special case and outliers imputation. 
	     The function descriptive statistics is exported and can be used in 'R' sessions independently from the user interface, 
	     while special case and outlier imputation functions are written to be used with shiny UI.
Generate a monochrome palette from a starting colour 
    for a specified number of colours. The package can also be used to display 
    colour palettes in the plot window, with or without hex colour code labels.
Implementation of the Monothetic Clustering
    algorithm (Chavent, 1998 <doi:10.1016/S0167-8655(98)00087-7>) on
    continuous data sets. A lot of extensions are included in the package,
    including applying Monothetic clustering on data sets with circular
    variables, visualizations with the results, and permutation and
    cross-validation based tests to support the decision on the number of
    clusters.
Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology charts and distributional and diversity maps are also available.
Various imputation methods are utilized in this package, where one can flag and impute non-monotonic data that is outside of a prespecified range.
Estimation of multivariate normal (MVN) and student-t data of 
 arbitrary dimension where the pattern of missing data is monotone.
 See Pantaleo and Gramacy (2010) <arXiv:0907.2135>.
 Through the use of parsimonious/shrinkage regressions 
 (plsr, pcr, lasso, ridge,  etc.), where standard regressions fail, 
 the package can handle a nearly arbitrary amount of missing data. 
 The current version supports maximum likelihood inference and 
 a full Bayesian approach employing scale-mixtures for Gibbs sampling.
 Monotone data augmentation extends this Bayesian approach to arbitrary 
 missingness patterns.  A fully functional standalone interface to the 
 Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown),
 Horseshoe (from Carvalho, Polson, & Scott), and ridge regression 
 with model selection via Reversible Jump, and student-t errors 
 (from Geweke) is also provided.
Requires rooted phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways. 
 "MonoPhy: a simple R package to find and visualize monophyly issues." Schwery, O. & O'Meara, B.C. (2016) <doi:10.7717/peerj-cs.56>.
Functions for fitting monotone polynomials to data.
             Detailed discussion of the methodologies used can be
             found in Murray, Mueller and Turlach (2013)
             <doi:10.1007/s00180-012-0390-5> and Murray, Mueller and
             Turlach (2016) <doi:10.1080/00949655.2016.1139582>.
An extended version of the nonparametric Bayesian monotonic regression procedure described in Saarela & Arjas (2011) <DOI:10.1111/j.1467-9469.2010.00716.x>, allowing for multiple additive monotonic components in the linear predictor, and time-to-event outcomes through case-base sampling. The extension and its applications, including estimation of absolute risks, are described in Saarela & Arjas (2015) <DOI:10.1111/sjos.12125>. The package also implements the nonparametric ordinal regression model described in Saarela, Rohrbeck & Arjas <arXiv:2007.01390>.
The monotone package contains a fast up-and-down-blocks implementation for the pool-adjacent-violators algorithm 
  for simple linear ordered monotone regression, including two spin-off functions
  for unimodal and bivariate monotone regression (see <doi:10.18637/jss.v102.c01>).
Test for monotonicity in financial variables sorted by portfolios. It is conventional practice in empirical research to form portfolios of assets ranked by a certain sort variable. A t-test is then used to consider the mean return spread between the portfolios with the highest and lowest values of the sort variable. Yet comparing only the average returns on the top and bottom portfolios does not provide a sufficient way to test for a monotonic relation between expected returns and the sort variable. This package provides nonparametric tests for the full set of monotonic patterns by Patton, A. and Timmermann, A. (2010) <doi:10.1016/j.jfineco.2010.06.006> and compares the proposed results with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.
Estimates monotone regression and variance functions in a nonparametric model, based on Dette, Holger, Neumeyer, and Pilz (2006) <doi:10.3150/bj/1151525131>.
Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) <doi:10.1111/insr.12087>.
Simplifies Monte Carlo simulation studies by automatically 
             setting up loops to run over parameter grids and parallelising
             the Monte Carlo repetitions. It also generates LaTeX tables.
Monte Carlo simulation allows to test different conditions given to the correct structural equation models. This package runs Monte Carlo simulations under different conditions (such as sample size or normality of data). Within the package data sets can be simulated and run based on the given model.
  First, continuous and normal data sets are generated based on the given model. Later Fleishman's power method (1978) <DOI:10.1007/BF02293811> is used to add non-normality if exists. 
  When data generation is completed (or when generated data sets are given) model test can also be run.
  Please cite as "Orçan, F. (2021). MonteCarloSEM: An R Package to Simulate Data for SEM. International Journal of Assessment Tools in Education, 8 (3), 704-713."
A collection of functions to connect to a 'Moodle' database, cache relevant tables locally and generate learning analytics. 
    'Moodle' is an open source Learning Management System (LMS) developed by MoodleHQ. For more information about Moodle, visit <https://moodle.org>.
Several analysis-related functions for the book entitled "R
    statistics and graph for medical articles" (written in Korean), version 1,
    by Keon-Woong Moon with Korean demographic data with several plot
    functions.
Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) <doi:10.48550/arXiv.2209.01493>.  It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.
Provides real & simulated datasets containing time-series traffic observations
    and additional information pertaining to Loop 1 "Mopac" located in Austin, Texas.
With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The 'MOQA' package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.
Supports morphological analysis for Japanese by using 'MeCab'. 
    Can input data.frame and obtain all results of 'MeCab' and row number of 
    original data.frame as a text id.
Additions to 'party' and 'partykit' packages : tools for the interpretation of forests (surrogate trees, prototypes, etc.), feature selection (see Gregorutti et al (2017) <arXiv:1310.5726>, Hapfelmeier and Ulm (2013) <doi:10.1016/j.csda.2012.09.020>, Altmann et al (2010) <doi:10.1093/bioinformatics/btq134>) and parallelized versions of conditional forest and variable importance functions. Also modules and a shiny app for conditional inference trees.
Automatically segments a 3D array of voxels into mutually exclusive morphological elements. This package extends existing work for segmenting 2D binary raster data. A paper documenting this approach has been accepted for publication in the journal Landscape Ecology. Detailed references will be updated here once those are known.
Tokenize text into morphemes. The morphemepiece algorithm uses a 
  lookup table to determine the morpheme breakdown of words, and falls back on a 
  modified wordpiece tokenization algorithm for words not found in the lookup 
  table.
Provides data about morphemes, the smallest units of meaning in a 
    language.
A toolset for Geometric Morphometrics and mesh processing. This
    includes (among other stuff) mesh deformations based on reference points,
    permutation tests, detection of outliers, processing of sliding
    semi-landmarks and semi-automated surface landmark placement.
Extract cross sections from long bone meshes at specified intervals along the diaphysis. Calculate two and three-dimensional morphometric maps, cross-sectional geometric parameters, and semilandmarks on the periosteal and endosteal contours of each cross section.
Implements adaptive landscape methods first described by Polly et al. (2016) <doi:10.1080/02724634.2016.1111225> for the integration, analysis and visualization of biological trait data on a phenotypic morphospace - typically defined by shape metrics.
Tools for multivariate analyses of morphological data, wrapped in one package, to make the workflow convenient and fast. Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package will be described in Šlenker et al. (in prep).
Advanced methods for a valuable quantitative environmental risk 
   assessment using Bayesian inference of survival and reproduction Data. Among
   others, it facilitates Bayesian inference of the general unified
   threshold model of survival (GUTS). See our companion paper 
   Baudrot and Charles (2021) <doi:10.21105/joss.03200>,
   as well as complementary details in Baudrot et al. (2018)
   <doi:10.1021/acs.est.7b05464> and Delignette-Muller et al.
   (2017) <doi:10.1021/acs.est.6b05326>.
A collection of functions for the analysis of archaeological mortality
    data (on the topic see e.g. Chamberlain 2006 
    <https://books.google.de/books?id=nG5FoO_becAC&lpg=PA27&ots=LG0b_xrx6O&dq=life%20table%20archaeology&pg=PA27#v=onepage&q&f=false>). 
    It takes demographic data in different formats and displays the result in a standard life table
    as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, life expectancy, percentage of population).
Life expectancy is highly correlated over time among countries and 
  between males and females. These associations can be used to improve forecasts. 
  Here we have implemented a method for forecasting female life expectancy based on 
  analysis of the gap between female life expectancy in a country compared with
  the record level of female life expectancy in the world. Second, to forecast 
  male life expectancy, the gap between male life expectancy and female life 
  expectancy in a country is analysed. We named this method the Double-Gap model.
  For a detailed description of the method see Pascariu et al. (2017). 
  <doi:10.1016/j.insmatheco.2017.09.011>.
Fit the most popular human mortality 'laws', and construct 
  full and abridge life tables given various input indices. A mortality
  law is a parametric function that describes the dying-out process of 
  individuals in a population during a significant portion of their 
  life spans. For a comprehensive review of the most important mortality 
  laws see Tabeau (2001) <doi:10.1007/0-306-47562-6_1>. 
  Practical functions for downloading data from various human mortality 
  databases are provided as well.
Classes to implement and plot cohort life tables
    for actuarial calculations. In particular, birth-year dependent mortality
    tables using a yearly trend to extrapolate from a base year are implemented,
    as well as period life table, cohort life tables using an age shift, and
    merged life tables.
Age-specific mortality rates are estimated and projected using 
    the Kannisto, Lee-Carter and related methods as described in 
    Sevcikova et al. (2016) <doi:10.1007/978-3-319-26603-9_15>.
Returns information about characters, locations, and episodes from
    'The Rick and Morty' API: <https://rickandmortyapi.com>.
Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used
    to teach mathematics, statistics, computation and modeling.  Funded by the
    NSF, Project MOSAIC is a community of educators working to tie together
    aspects of quantitative work that students in science, technology,
    engineering and mathematics will need in their professional lives, but
    which are usually taught in isolation, if at all.
Software to support the introductory *MOSAIC Calculus* 
    textbook <https://www.mosaic-web.org/MOSAIC-Calculus/>),
    one of many data- and modeling-oriented educational resources developed by 
    Project MOSAIC (<https://www.mosaic-web.org/>). Provides symbolic and
    numerical differentiation and integration, as well as support for 
    applied linear algebra (for data science), and differential equations/dynamics.
    Includes grammar-of-graphics-based functions for drawing vector fields, trajectories, etc.
    The software is suitable for general use, but intended mainly for teaching calculus.
Common utilities used in other MOSAIC-family packages are 
    collected here.
Data sets from Project MOSAIC (<http://www.mosaic-web.org>) used
    to teach mathematics, statistics, computation and modeling.  Funded by the
    NSF, Project MOSAIC is a community of educators working to tie together
    aspects of quantitative work that students in science, technology,
    engineering and mathematics will need in their professional lives, but
    which are usually taught in isolation, if at all.
Provides functions for evaluating, displaying, and interpreting statistical models. The goal is to abstract the operations on models from the particular architecture of the model. For instance, calculating effect sizes rather than looking at coefficients. The package includes interfaces to both regression and classification architectures, including lm(), glm(), rlm() in 'MASS', random forests and recursive partitioning, k-nearest neighbors, linear and quadratic discriminant analysis, and models produced by the 'caret' package's train(). It's straightforward to add in other other model architectures.
Performs simultaneous hyperparameter tuning and
  feature selection through both single-objective and multi-objective
  optimization as described in Binder, Moosbauer et al. (2019) <arXiv:1912.12912>.
  Uses the 'ecr'-package as basis but adds mixed integer
  evolutionary strategies and multi-fidelity functionality as well as operators
  specific for the problem of feature selection.
This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling time period, and the optimal time to apply a treatment or multiple treatments.
High dimensionality, noise and heterogeneity among
    samples and features challenge the omic integration task. Here we
    present an omic integration method based on sparse singular value
    decomposition (SVD) to deal with these limitations, by: a. obtaining
    the main axes of variation of the combined omics, b. imposing sparsity
    constraints at both subjects (rows) and features (columns) levels
    using Elastic Net type of shrinkage, and c. allowing both linear and
    non-linear projections (via t-Stochastic Neighbor Embedding) of the
    omic data to detect clusters in very convoluted data
    (Gonzalez-Reymundez et. al, 2022) <doi:10.1093/bioinformatics/btac179>.
Provides functions similar to the 'SAS' macros previously provided
              to accompany Collins, Dziak, and Li (2009) <DOI:10.1037/a0015826>
              and Dziak, Nahum-Shani, and Collins (2012) <DOI:10.1037/a0026972>, papers
              which outline practical benefits and challenges of factorial
              and fractional factorial experiments for scientists interested
              in developing biological and/or behavioral interventions, especially
              in the context of the multiphase optimization strategy
              (see Collins, Kugler & Gwadz 2016) <DOI:10.1007/s10461-015-1145-4>.  The package
              currently contains three functions. First, RelativeCosts1() draws a graph
              of the relative cost of complete and reduced factorial designs versus
              other alternatives. Second, RandomAssignmentGenerator() returns a dataframe
              which contains a list of random numbers that can be used to conveniently
              assign participants to conditions in an experiment with
              many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect
              size, or required sample size of a factorial or fractional factorial
              experiment, for main effects or interactions, given several possible choices
              of effect size metric, and allowing pretests and clustering.
Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) <doi:10.3150/16-BEJ887> and the multiscale algorithmic extension from Cho and Kirch (2022) <doi:10.1007/s10463-021-00811-5>, as well as the bootstrap procedure for generating confidence intervals about the locations of change points as proposed in Cho and Kirch (2022) <doi:10.1016/j.csda.2022.107552>. See also Meier, Kirch and Cho (2021) <doi:10.18637/jss.v097.i08> which accompanies the R package.
Learning, manipulation and  evaluation of mixtures of  truncated basis  functions 
  (MoTBFs),  which include mixtures of  polynomials (MOPs) and  mixtures of truncated 
  exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian
  networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). The  package provides  functionality for learning  univariate, multivariate and
  conditional  densities, with the  possibility of incorporating prior  knowledge. Structural
  learning of hybrid Bayesian  networks is also provided. A set of useful tools is provided,
  including  plotting, printing  and likelihood  evaluation. This  package  makes use of  S3 
  objects, with two new classes called 'motbf' and 'jointmotbf'.
Measure of the Effect ('MOTE') is an effect size calculator, including a 
    wide variety of effect sizes in the mean differences family (all versions of d) and 
    the variance overlap family (eta, omega, epsilon, r). 'MOTE' provides non-central 
    confidence intervals for each effect size, relevant test statistics, and output 
    for reporting in APA Style (American Psychological Association, 2010, 
    <ISBN:1433805618>) with 'LaTeX'. In research, an over-reliance on p-values 
    may conceal the fact that a study is under-powered (Halsey, Curran-Everett, 
    Vowler, & Drummond, 2015 <doi:10.1038/nmeth.3288>). A test may be statistically 
    significant, yet practically inconsequential (Fritz, Scherndl, & Kühberger, 2012 
    <doi:10.1177/0959354312436870>). Although the American Psychological Association 
    has long advocated for the inclusion of effect sizes (Wilkinson & American 
    Psychological Association Task Force on Statistical Inference, 1999 
    <doi:10.1037/0003-066X.54.8.594>), the vast majority of peer-reviewed, 
    published academic studies stop short of reporting effect sizes and confidence 
    intervals (Cumming, 2013, <doi:10.1177/0956797613504966>). 'MOTE' simplifies 
    the use and interpretation of effect sizes and confidence intervals. For more 
    information, visit <https://www.aggieerin.com/shiny-server>.
Describes spatial patterns of categorical raster data for 
    any defined regular and irregular areas. 
    Patterns are described quantitatively using built-in signatures 
    based on co-occurrence matrices but also allows for 
    any user-defined functions. 
    It enables spatial analysis such as search, change detection,
    and clustering to be performed on spatial patterns (Nowosad (2021) <doi:10.1007/s10980-020-01135-0>).
Tools for spectral clustering of weighted directed networks using motif
    adjacency matrices. Methods perform well on large and sparse networks, and
    random sampling methods for generating weighted directed networks are also
    provided. Based on methodology detailed in Underwood, Elliott and Cucuringu
    (2020) <arXiv:2004.01293>.
Tools for motif analysis in multi-level networks.
    Multi-level networks combine multiple networks in one, e.g.
    social-ecological networks. Motifs are small configurations of nodes
    and edges (subgraphs) occurring in networks. 'motifr' can visualize
    multi-level networks, count multi-level network motifs and compare
    motif occurrences to baseline models. It also identifies contributions
    of existing or potential edges to motifs to find critical or missing
    edges. The package is in many parts an R wrapper for the excellent
    'SESMotifAnalyser' 'Python' package written by Tim Seppelt.
Functions for fitting models of trait evolution
        on phylogenies for continuous traits. The majority of functions
        described in Thomas and Freckleton (2012) <doi:10.1111/j.2041-210X.2011.00132.x> 	and include functions that allow for tests of variation in the rates of trait evolution.
The temporal relationship between motor neurons can offer 
    explanations for neural strategies. We combined functions to reduce neuron 
    action potential discharge data and analyze it for short-term, time-domain 
    synchronization. Even more so, motoRneuron combines most available methods 
    for the determining cross correlation histogram peaks and most available 
    indices for calculating synchronization into simple functions. See 
    Nordstrom, Fuglevand, and Enoka (1992) <doi:10.1113/jphysiol.1992.sp019244> 
    for a more thorough introduction.
Functions to estimate start and duration of moult from moult 
             data, based on models developed in Underhill 
             and Zucchini (1988, 1990).
Lattice functions for drawing folded empirical cumulative
    distribution plots, or mountain plots. A mountain plot is similar
    to an empirical CDF plot, except that the curve increases from
    0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at
    the right side. See Monti (1995) <doi:10.1080/00031305.1995.10476179>.
Mouse-tracking, the analysis of mouse movements in computerized
    experiments, is a method that is becoming increasingly popular in the
    cognitive sciences. The mousetrap package offers functions for importing,
    preprocessing, analyzing, aggregating, and visualizing mouse-tracking data.
    An introduction into mouse-tracking analyses using mousetrap can be found
    in Wulff, Kieslich, Henninger, Haslbeck, & Schulte-Mecklenbeck (2021)
    <doi:10.31234/osf.io/v685r> (preprint: <https://psyarxiv.com/v685r>).
Contains functions to access movement data stored in 'movebank.org'
    as well as tools to visualize and statistically analyze animal movement data,
    among others functions to calculate dynamic Brownian Bridge Movement Models.
    Move helps addressing movement ecology questions.
Provides the facility to calculate non-isotropic accumulated cost surface, least-cost paths, least-cost corridors, least-cost networks using a number of human-movement-related cost functions that can be selected by the user. It just requires a Digital Terrain Model, a start location and (optionally) destination locations. See Alberti (2019) <doi:10.1016/j.softx.2019.100331>.
Provides tools for animal movement modelling using hidden Markov
    models. These include processing of tracking data, fitting hidden Markov models
    to movement data, visualization of data and fitted model, decoding of the state
    process...
Tools to visualize movement data (e.g. from GPS tracking) and temporal changes of environmental data (e.g. from remote sensing) by creating video animations.
Estimating wind speed from trajectories of individually tracked birds using a maximum likelihood approach.
Fit and simulate mixtures of von Mises-Fisher distributions.
Multidimensional projection techniques are used to create two
    dimensional representations of multidimensional data sets.
Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>,
      Wang et al. (2015) <doi:10.1002/bimj.201400143>,
      Wang et al. (2016) <doi:10.1177/0962280214530608>,
      Wang (2021) <doi:10.1007/s11749-021-00770-2>,
      Wang (2020) <arXiv:2010.02848>.
Overcomes one of the major challenges in mobile (passive) sensing, namely 
    being able to pre-process the raw data that comes from a mobile sensing app, 
    specifically "m-Path Sense" <https://m-path.io>. The main task of 'mpathsenser' is 
    therefore to read "m-Path Sense" JSON files into a database and provide several
    convenience functions to aid in data processing.
Performs treatment allocation in two-arm clinical trials by the maximal procedure described by Berger et al. (2003) <doi:10.1002/sim.1538>. To that end, the algorithm provided by Salama et al. (2008) <doi:10.1002/sim.3014> is implemented.
It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.
Data sets and scripts for Modeling Psychophysical Data in R (Springer).
Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1101/2020.07.06.190256>.
Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold.  The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator.  Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) <doi:10.35648/20.500.12413/11781/ii039> for The Alkire-Foster methodology.
Computing modified profile likelihood estimates for Weibull Shape and Regression Parameters. Modified likelihood estimates are provided.
Model stability and variable inclusion plots [Mueller and Welsh
    (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller
    (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al.
    (2008, <doi:10.1214/07-AOS517>); Jiang et al. 
    (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.
Leverages the R language to automate latent variable model estimation
	and interpretation using 'Mplus', a powerful latent variable modeling program
	developed by Muthen and Muthen (<https://www.statmodel.com>). Specifically, this package
    provides routines for creating related groups of models, running batches of
    models, and extracting and tabulating model parameters and fit statistics.
Uses recursive partitioning to create homogeneous subgroups based on structural equation models 
             fit in 'Mplus', a stand-alone program developed by Muthen and Muthen.
Exploratory graphical analysis of multivariate data,
    specifically gene expression data with different projection
    methods: principal component analysis, correspondence analysis,
    spectral map analysis.
Functions to compute and plot multivariate (partial) Mantel correlograms.
Uses a kernel smoothing approach to calculate Mutual Information
    for comparisons between all types of variables including continuous vs
    continuous, continuous vs discrete and discrete vs discrete. Uses a
    nonparametric bias correction giving Bias Corrected Mutual Information
    (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to
    large genomic datasets.
Calculates the Most Probable Number (MPN) to quantify the
    concentration (density) of microbes in serial dilutions of a laboratory
    sample (described in Jarvis, 2010 <doi:10.1111/j.1365-2672.2010.04792.x>).
    Also calculates the Aerobic Plate Count (APC) for similar microbial
    enumeration experiments.
Symbolic computing with multivariate polynomials in R.
A flexible framework for power analysis using Monte
    Carlo simulation for settings in which considerations of the correlations
    between predictors are important. Users can set up a data generative model
    that preserves dependence structures among predictors given existing data
    (continuous, binary, or ordinal). Users can also generate power curves to
    assess the trade-offs between sample size, effect size, and power of a design.
    This package includes several statistical models common in environmental
    mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <arXiv:2209.08036>.
Analysis of experimental multi-parent populations to detect
             regions of the genome (called quantitative trait loci, QTLs)
             influencing phenotypic traits. The population must be composed of crosses
             between a set of at least three parents (e.g. factorial design,
             'diallel', or nested association mapping). The functions cover data
             processing, QTL detection, and results visualization. The implemented
             methodology is described by Garin, Wimmer, Mezmouk, Malosetti and
             van Eeuwijk (2017) <doi:10.1007/s00122-017-2923-3>.
Fitting Multi-Parameter Regression (MPR) models to right-censored survival data. These are flexible parametric regression models which extend standard models, for example, proportional hazards. See Burke & MacKenzie (2016) <doi:10.1111/biom.12625> and Burke et al (2020) <doi:10.1111/rssc.12398>.
Infer parental genotypes based on low-coverage population sequencing data and 
		thus can genotype mapping populations and construct ultra-high density linkage map 
		in a parent-independent manner. Weibo Xie et al. (2010) <doi:10.1073/pnas.1005931107>.
Developed for computing the probability density function, computing the cumulative distribution function, computing the quantile function, random generation, drawing q-q plot, and estimating the parameters of 24 G-family of statistical distributions via the maximum product spacing approach introduced in <https://www.jstor.org/stable/2345411>. The set of families contains: beta G distribution, beta exponential G distribution, beta extended G distribution, exponentiated G distribution, exponentiated exponential Poisson G distribution, exponentiated generalized G distribution, exponentiated Kumaraswamy G distribution, gamma type I G distribution, gamma type II G distribution, gamma uniform G distribution, gamma-X generated of log-logistic family of G distribution, gamma-X family of modified beta exponential G distribution, geometric exponential Poisson G distribution, generalized beta G distribution, generalized transmuted G distribution, Kumaraswamy G distribution, log gamma type I G distribution, log gamma type II G distribution, Marshall Olkin G distribution, Marshall Olkin Kumaraswamy G distribution, modified beta G distribution, odd log-logistic G distribution, truncated-exponential skew-symmetric G distribution, and Weibull G distribution.
Computational tools to represent phylogenetic signals using adapted eigenvector maps.
A low-dependency implementation of GSIF::mpspline() <https://r-forge.r-project.org/scm/viewvc.php/pkg/R/mpspline.R?view=markup&revision=240&root=gsif>,
  which applies a mass-preserving spline to soil attributes. Splining soil data 
  is a safe way to make continuous down-profile estimates of attributes measured
  over discrete, often discontinuous depth intervals.
Supplementary materials and datasets for the book "Modern Psychometrics With R" (Mair, 2018, Springer useR! series).
Fitting and testing multinomial processing tree (MPT) models, a
  class of nonlinear models for categorical data.  The parameters are the
  link probabilities of a tree-like graph and represent the latent cognitive
  processing steps executed to arrive at observable response categories
  (Batchelder & Riefer, 1999 <doi:10.3758/bf03210812>; Erdfelder et al., 2009
  <doi:10.1027/0044-3409.217.3.108>; Riefer & Batchelder, 1988
  <doi:10.1037/0033-295x.95.3.318>).
Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g.,  Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.
Statistical or cognitive modeling usually requires a number of more or less 
    arbitrary choices creating one specific path through a 'garden of forking paths'. 
    The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, 
    <doi:10.1177/1745691616658637>) offers a principled alternative in which results 
    for all possible combinations of reasonable modeling choices are reported. 
    MPTmultiverse performs a multiverse analysis for multinomial processing tree 
    (MPT, Riefer & Batchelder, 1988, <doi:10.1037/0033-295X.95.3.318>) models combining 
    maximum-likelihood/frequentist and Bayesian estimation approaches with 
    different levels of pooling (i.e., data aggregation). For the 
    frequentist approaches, no pooling (with and without parametric or nonparametric 
    bootstrap) and complete pooling are implemented using 
    MPTinR <https://cran.r-project.org/package=MPTinR>. 
    For the Bayesian approaches, no pooling, complete pooling, and three different 
    variants of partial pooling are implemented using 
    TreeBUGS <https://cran.r-project.org/package=TreeBUGS>. The main function is 
    fit_mpt() who performs the multiverse analysis in one call.
Most of this package consists of data sets from the 
             textbook Introduction
             to Linear Regression Analysis (3rd ed), by Montgomery, Peck
             and Vining.
             Some additional data sets and functions are also included.
Useful functions to analyze proteomic workflows including
    number of identifications, data completeness, missed cleavages,
    quantitative and retention time precision etc. Various software
    outputs are supported such as 'ProteomeDiscoverer', 'Spectronaut', 'DIA-NN'
    and 'MaxQuant'.
Complements the book "Using R for Modelling and  
    Quantitative Methods in Fisheries" ISBN: 9780367469894, being 
    published in September 2020 by Chapman & Hall in their "Using R series". 
    There are numerous functions and data-sets that are used in the 
    book's many practical examples.
Parametric modeling of M-quantile regression coefficient functions.
The penalized inverse-variance weighted (pIVW) estimator is a Mendelian randomization method for estimating the causal effect of an exposure variable on an outcome of interest based on summary-level GWAS data. The pIVW estimator accounts for weak instruments and balanced horizontal pleiotropy simultaneously. See Xu S., Wang P., Fung W.K. and Liu Z. (2022) <doi:10.1111/biom.13732>.
Mendelian randomization is a method of identifying and estimating a confounded causal effect using genetic instrumental variables. This packages implements methods for two-sample Mendelian randomization with summary statistics by using Robust Adjusted Profile Score (RAPS). References: Qingyuan Zhao, Jingshu Wang, Jack Bowden, Dylan S. Small. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. <arXiv:1801.09652>.
Accomplishes mark-recapture analysis with covariates. Models available include the Cormack-Jolly-Seber open population (Cormack (1972) <doi:10.2307/2556151>; Jolly (1965) <doi:10.2307/2333826>; Seber (1965) <doi:10.2307/2333827>) and Huggin's (1989) <doi:10.2307/2336377> closed population. 
	Link functions include logit, sine, and hazard.  Model selection, model averaging, plot, and simulation routines included. Open population size by the Horvitz-Thompson (1959) <doi:10.2307/2280784> estimator.
Performs (simultaneous) inferences for ratios of linear combinations of coefficients in the general linear model, linear mixed model, and for quantiles in a one-way layout. Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) <https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn (2008) <doi:10.1002/bimj.200710466>. Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances.
Bayesian estimation of inverse variance weighted (IVW), Burgess 
    et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden et 
    al. (2015) <doi:10.1093/ije/dyv080>, summary data models for Mendelian 
    randomization analyses.
A collection of functions for processing and analyzing metabolite data. 
    The namesake function mrbin() converts 1D 
    or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and
    performs basic processing steps in a reproducible way. Negative values, a
    common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used
    parameters are stored in a readable text file and can be restored from that
    file to enable exact reproduction of the data at a later time. The function fia() ranks features according
    to their impact on classifier models, especially artificial neural network models.
A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features.
    mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales.
    To infer which of the captured features are credible, Bayesian analysis is used.
    The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction.
    (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found.
    (3) Posterior credibility analysis of the differences of smooths created.
    The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011>.
    Matlab code is available under <http://cc.oulu.fi/~lpasanen/MRBSiZer/>.
Compute and select tuning parameters for the MRCE estimator proposed by Rothman, Levina, and Zhu (2010) <doi:10.1198/jcgs.2010.09188>.  This estimator fits the multiple output linear regression model with a sparse estimator of the error precision matrix and a sparse estimator of the regression coefficient matrix.
Animal abundance estimation via conventional, multiple covariate
    and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function
    fitting is performed via maximum likelihood. Also included are diagnostics
    and plotting for fitted detection functions. Abundance estimation is via a
    Horvitz-Thompson-like estimator.
Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics.
Tools to get marine regions data from
    <https://www.marineregions.org/>. Includes tools to get region metadata,
    as well as data in 'GeoJSON' format, as well as Shape files. Use cases
    include using data downstream to visualize 'geospatial' data by marine
    region, mapping variation among different regions, and more.
Forecasting of univariate time series using feature extraction with variable prediction methods is provided. Feature extraction is done with a redundant Haar wavelet transform with filter h = (0.5, 0.5). The advantage of the approach compared to typical Fourier based methods is an dynamic adaptation to varying seasonalities. Currently implemented prediction methods based on the selected wavelets levels and scales are a regression and a multi-layer perceptron. Forecasts can be computed for horizon 1 or higher. Model selection is performed with an evolutionary optimization. Selection criteria are currently the AIC criterion, the Mean Absolute Error or the Mean Root Error. The data is split into three parts for model selection: Training, test, and evaluation dataset. The training data is for computing the weights of a parameter set. The test data is for choosing the best parameter set. The evaluation data is for assessing the forecast performance of the best parameter set on new data unknown to the model. This work is published in Stier, Q.; Gehlert, T.; Thrun, M.C. Multiresolution Forecasting for Industrial Applications. Processes 2021, 9, 1697. <doi:10.3390/pr9101697>.
Model fitting, sampling and visualization
    for the (Hidden) Markov Random Field model with pairwise interactions and 
    general interaction structure from 
    Freguglia, Garcia & Bicas (2020) <doi:10.1002/env.2613>,
    which has many popular models used in 2-dimensional lattices
    as particular cases, like the Ising Model and Potts Model.
    A complete manuscript describing the package is available in
    Freguglia & Garcia (2022) <doi:10.18637/jss.v101.i08>.
Performs the MRFA approach proposed by Sung et al. (2019+) <arXiv:1709.07064> to fit
    and predict nonlinear regression problems, particularly for large-scale and
    high-dimensional problems. The application includes deterministic or stochastic
    computer experiments, spatial datasets, and so on.
Approximate node interaction parameters of Markov Random Fields 
    graphical networks. Models can incorporate additional covariates, allowing users to estimate
    how interactions between nodes in the graph are predicted to change across
    covariate gradients. The general methods implemented in this package are described 
    in Clark et al. (2018) <doi:10.1002/ecy.2221>.
Tools to compute depth measures and implementations of related 
             tasks such as outlier detection, data exploration and 
            classification of multivariate, regression and functional data.
Simulation from an 'mrgsolve' 
    <https://cran.r-project.org/package=mrgsolve> model using a parallel backend.  
    Input data sets are split (chunked) and simulated in parallel using 
    mclapply() or future_lapply() 
    <https://cran.r-project.org/package=future.apply>.
Perform sensitivity analysis on ordinary differential equation 
    based models, including ad-hoc graphical analyses based on structured 
    sequences of parameters as well as local sensitivity analysis. Functions 
    are provided for creating inputs, simulating scenarios and plotting outputs.
Fast simulation from ordinary differential equation
    (ODE) based models typically employed in quantitative pharmacology and
    systems biology.
Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) 
    self-exciting process, with given immigrant hazard rate functions and 
    offspring density function. Calculate the likelihood of a MRHawkes process 
    with given hazard rate functions and offspring density function for an 
    (increasing) sequence of event times. Calculate the Rosenblatt residuals of the 
    event times. Predict future event times based on observed event times up to a 
    given time. For details see Stindl and Chen (2018) <doi:10.1016/j.csda.2018.01.021>.
It provides functions to compute the values of different modifications of the Rand and Wallace indices. The indices are used to measure the stability or similarity of two partitions obtained on two different sets of units with a non-empty intercept. Splitting and merging of clusters can (depends on the selected index) have a different effect on the value of the indices.  The indices are proposed in Cugmas and Ferligoj (2018) <http://ibmi.mf.uni-lj.si/mz/2018/no-1/Cugmas2018.pdf>.
A causal mediation approach under the counterfactual framework 
    to test the significance of total, direct and indirect effects. In this approach, 
    a group of methylated sites from a predefined region are utilized as the mediator, 
    and the functional transformation is used to reduce the possible high dimension 
    in the region-based methylated sites and account for their location information.
Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes, and all the nonzero effects were further identified by likelihood ratio test for significant QTL. The program may run on a desktop or laptop computers. If marker genotypes in association mapping population are almost homozygous, these methods in this software are very effective. If there are many heterozygous marker genotypes, the IIIVmrMLM software is recommended. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018, <doi:10.1093/bib/bbw145>), and Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM (2022, <doi:10.1016/j.molp.2022.02.012>).
Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) <doi:10.1093/bib/bbw145>.
Computes mutual information matrices from continuous, categorical 
  and survival variables, as well as feature selection with minimum redundancy, 
  maximum relevance (mRMR) and a new ensemble mRMR technique. Published in
  De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>.
Computes multiple correlation coefficient when the data matrix is given and tests its significance.
A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC 
            (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also 
            contains functions to simulate data under a certain topology, to visualize a graph in different 
            ways, and to compare graphs and quantify the differences. 
            See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>,Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>.
An easy-to-use IMAP client that provides tools for message searching,
    selective fetching of message attributes, mailbox management, attachment extraction, 
    and several other IMAP features, paving the way for e-mail data analysis in R.
We can calculate directly used this package the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method.
We provide the framework to analyze multiresolution partitions (e.g. country, provinces, subdistrict) where each individual data point belongs to only one partition in each layer (e.g. i belongs to subdistrict A, province P, and country Q).   We assume that a partition in a higher layer subsumes lower-layer partitions (e.g. a nation is at the 1st layer subsumes all provinces at the 2nd layer). Given N individuals that have a pair of real values (x,y) that generated from  independent variable X and dependent variable Y. Each individual i belongs to one partition per layer. Our goal is to find which partitions at which highest level that all individuals  in the these partitions share the same linear model Y=f(X) where f is a linear function. The framework deploys the Minimum Description Length principle (MDL) to infer solutions. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2021) <doi:10.1145/3424670>.
Frequentist and Bayesian linear regression for large data sets. Useful 
    when the data does not fit into memory (for both frequentist and Bayesian regression), 
    to make running time manageable (mainly for Bayesian regression), and to reduce 
    the total running time because of reduced or less severe memory-spillover into 
    the virtual memory. This is an implementation of Merge & Reduce for linear regression
    as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020).
    'Streaming statistical models via Merge & Reduce'. International Journal of
    Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.
An implementation of the MRS algorithm for comparison across distributions,
  as described in Jacopo Soriano, Li Ma (2016) <doi:10.1111/rssb.12180>. 
  The model is based on a nonparametric process taking the form of a Markov model 
  that transitions between a "null" and an "alternative" state 
  on a multi-resolution partition tree of the sample space. 
  MRS effectively detects and characterizes a variety of underlying differences. 
  These differences can be visualized using several plotting functions.
An R implementation of 'GUIDE' style algorithm focusing on subgroup identification problem 
            under multiple responses of Loh et al. (2019) <doi:10.1002/widm.1326>. This package is intended for use 
            for randomized trials and observational studies.
Provide a sample size calculator for micro-randomized trials (MRTs) based on methodology developed in Sample Size Calculations for Micro-randomized Trials in mHealth by Liao et al. (2016) <DOI:10.1002/sim.6847>.
Provides a sample size calculator for micro-randomized trials (MRTs) with binary outcomes based on methodology developed in Qian et al. (2020) <doi:10.1093/biomet/asaa070>. Also provides a power calculator when the sample size is input by the user.
Calculates ARMSS (age related multiple sclerosis severity), and both local and global MSSS (multiple sclerosis severity score).
Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.
Implements Benchmarking Method for Multivariate Small Area Estimation under Fay Herriot Model. Multivariate Small Area Estimation (MSAE) is a development of Univariate Small Area Estimation that considering the correlation among response variables and borrowing the strength from related areas and auxiliary variables to increase the effectiveness of sample size, the multivariate model in this package is based on multivariate model 1 proposed by Roberto Benavent and Domingo Morales (2016) <doi:10.1016/j.csda.2015.07.013>. Benchmarking in Small Area Estimation is a modification of Small Area Estimation model to guarantee that the aggregate weighted mean of the county predictors equals the corresponding weighted mean of survey estimates. Difference Benchmarking is the simplest benchmarking method but widely used by multiplying empirical best linear unbiased prediction (EBLUP) estimator by the common adjustment factors (J.N.K Rao and Isabel Molina, 2015).
Implements area level of multivariate small area estimation using Hierarchical Bayesian method under Normal and T distribution. The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.
Multi-step adaptive elastic-net (MSAENet) algorithm for
    feature selection in high-dimensional regressions proposed in
    Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>,
    with support for multi-step adaptive MCP-net (MSAMNet) and
    multi-step adaptive SCAD-net (MSASNet) methods.
Implements multivariate optimum benchmarking small area estimation. This package provides optimum benchmarking estimation for univariate and multivariate small area estimation and its MSE. In fact, MSE estimators for optimum benchmark are not readily available, so resampling method that called parametric bootstrap is applied. The optimum benchmark model and parametric bootstrap in this package are based on the model proposed in small area estimation. J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7).
Implements multivariate ratio benchmarking small area estimation. This package provides ratio benchmarking estimation for univariate and multivariate small area estimation and its MSE. In fact, MSE estimators for ratio benchmark are not readily available, so resampling method that called parametric bootstrap is applied. The ratio benchmark model and parametric bootstrap in this package are based on the model proposed in small area estimation. J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7).
The new methodology "moving subset analysis" provides functions to investigate the effect of environmental conditions on the CO2 fertilization effect within longterm free air carbon enrichment (FACE)  experiments. In general, the functionality is applicable to derive the influence of a third variable (forcing experiment-support variable) on the relation between a dependent and an independent variable.
Estimates the sample size needed to detect microbial contamination 
 in a lot with a user-specified detection probability and user-specified analytical sensitivity.
 Various patterns of microbial contamination are accounted for: homogeneous (Poisson), 
 heterogeneous (Poisson-Gamma) or localized(Zero-inflated Poisson).
  Ida Jongenburger et al. (2010) <doi:10.1016/j.foodcont.2012.02.004> 
  "Impact of microbial distributions on food safety".
  Leroy Simon (1963) <doi:10.1017/S0515036100001975>
  "Casualty Actuarial Society - The Negative Binomial and Poisson Distributions Compared".
Visualizes multiple sequence alignments dynamically within the
    Shiny web application framework.
Common mass spectrometry tools described in John Roboz (2013) <doi:10.1201/b15436>. It allows checking element
 isotopes, calculating (isotope labelled) exact monoisitopic mass, m/z values and mass accuracy, and inspecting possible contaminant mass peaks,
 examining possible adducts in electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI)
 ion sources.
Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2016).
Functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters. Numerical solutions are provided for its extensions.
Create native charts for 'Microsoft PowerPoint' and 'Microsoft Word' documents. 
 These can then be edited and annotated. Functions are provided to let users create charts, modify 
 and format their content. The chart's underlying data is automatically saved within the 
 'Word' document or 'PowerPoint' presentation. It extends package 'officer' that does 
 not contain any feature for 'Microsoft' native charts production.
Functions to classify mass spectra in known categories, and to determine discriminant mass-over-charge values. It includes easy-to-use functions for pre-processing mass spectra, functions to determine discriminant mass-over-charge values (m/z) from a library of mass spectra corresponding to different categories, and functions to predict the category (species, phenotypes, etc.) associated to a mass spectrum from a list of selected mass-over-charge values. Three vignettes illustrating how to use the functions of this package from real data sets are also available online to help users: <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_Ecrobiav3.html>, <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_Klebsiellav3.html> and <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_DAv3.html>.
Model based clustering using 
    the multivariate multiple Scaled t (MST) and multivariate multiple 
    scaled contaminated normal (MSCN) distributions. The MST is an 
    extension of the multivariate Student-t distribution to include 
    flexible tail behaviors, Forbes, F. & Wraith, D. (2014) <doi:10.1007/s11222-013-9414-4>. The MSCN represents a  heavy-tailed
    generalization of the multivariate normal (MN) distribution to
    model elliptical contoured scatters in the presence of mild outliers
    (also referred to as "bad" points) and automatically detect bad points, Punzo, A. & Tortora, C. (2021) <doi:10.1177/1471082X19890935>.
Three generalizations of the synthetic control method (which has 
    already an implementation in package 'Synth') are implemented: first, 
    'MSCMT' allows for using multiple outcome variables, second, time series 
    can be supplied as economic predictors, and third, a well-defined 
    cross-validation approach can be used.
    Much effort has been taken to make the implementation as stable as possible 
    (including edge cases) without losing computational efficiency.
    A detailed description of the main algorithms is given in 
    Becker and Klößner (2018) <doi:10.1016/j.ecosta.2017.08.002>.
Find common entities detected in both positive and negative
    ionization mode, delete this entity in the less sensible mode and combine both
    matrices.
Multiscale moving sum procedure for the detection of changes in expectation in univariate sequences. References - Multiscale change point detection via gradual bandwidth adjustment in moving sum processes (2021+), Tijana Levajkovic and Michael Messer.
Methods for analyzing and using quartets displayed on a collection of gene trees, 
      primarily to make inferences about the species tree or network under the multi-species 
      coalescent model. These include quartet hypothesis tests for the model, as developed by 
      Mitchell et al. (2019) <doi:10.1214/19-EJS1576>, simplex plots of quartet concordance factors
      as presented by Allman et al. (2020) <doi:10.1101/2020.02.13.948083>, species tree inference methods 
      based on quartet distances of Rhodes (2019) <doi:10.1109/TCBB.2019.2917204> and 
      Yourdkhani and Rhodes (2019) <doi:10.1007/s11538-020-00773-4>, and the NANUQ algorithm for inference of level-1 
      species networks of Allman et al. (2019) <doi:10.1186/s13015-019-0159-2>. Software announcement by 
      Rhodes et al. (2020) <doi:10.1093/bioinformatics/btaa868>.
Statistical tests for validating multispecies coalescent gene tree simulators, using pairwise distances and rooted triple counts. Background is given by Allman, Banos, and Rhodes (2019) <arXiv:1908.01424>.
R Client for the Microsoft Cognitive Services Text Analytics
    REST API, including Sentiment Analysis, Topic Detection, Language Detection,
    and Key Phrase Extraction. An account MUST be registered at the Microsoft
    Cognitive Services website <https://www.microsoft.com/cognitive-services/>
    in order to obtain a (free) API key. Without an API key, this package will
    not work properly.
R Client for the Microsoft Cognitive Services 
  'Text-to-Speech' REST API, including voice synthesis. A valid account 
  must be registered at the Microsoft Cognitive Services website 
  <https://azure.microsoft.com/services/cognitive-services/> in order to 
  obtain a (free) API key. Without an API key, this package will not 
  work properly.
R Client for the Microsoft Cognitive Services Web Language Model
    REST API, including Break Into Words, Calculate Conditional
    Probability, Calculate Joint Probability, Generate Next Words, and List
    Available Models. A valid account MUST be registered at the Microsoft
    Cognitive Services website <https://www.microsoft.com/cognitive-services/>
    in order to obtain a (free) API key. Without an API key, this package will
    not work properly.
Implements the method of successive dichotomizations by
    Bradley and Massof (2018) <doi:10.1371/journal.pone.0206106>, 
    which estimates item measures, person measures and ordered rating
    category thresholds given ordinal rating scale data.
Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously. It is an implementation of the work proposed by Mai, Q., Yang, Y., and Zou, H. (2019) <doi:10.5705/ss.202016.0117>.
Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components.  The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'.
Development, simulation testing, and implementation of management procedures for fisheries 
    (see Carruthers & Hordyk (2018) <doi:10.1111/2041-210X.13081>).
A companion to the Chinese book ``Modern Statistical Graphics''.
Fit (by Maximum Likelihood or MCMC/Bayesian), simulate, and forecast various Markov-Switching GARCH models as described in Ardia et al. (2019) <doi:10.18637/jss.v091.i04>.
Implements the three parallel forecast combinations of Markov Switching GARCH and extreme learning machine model along with the selection of appropriate model for volatility forecasting. For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>, Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>.
Multinomial logistic regression with sparse group lasso
        penalty. Simultaneous feature selection and parameter
        estimation for classification. Suitable for high dimensional
        multiclass classification with many classes. The algorithm
        computes the sparse group lasso penalized maximum likelihood
        estimate. Use of parallel computing for cross validation and
        subsampling is supported through the 'foreach' and 'doParallel'
        packages. Development version is on GitHub, please report
        package issues on GitHub.
For fitting multivariate response and multiple predictor linear regressions with an arbitrary group structure assigned on the regression coefficient matrix, using the multivariate sparse group lasso and the mixed coordinate descent algorithm.
This is the library that can serialize or unserialize MessagePack format data.
Computes the degrees of freedom of the lasso,
        elastic net, generalized elastic net and adaptive lasso based
        on the generalized path seeking algorithm.  The optimal model
        can be selected by model selection criteria including Mallows'
        Cp, bias-corrected AIC (AICc), generalized cross validation
        (GCV) and BIC.
Provides new functions info(), warn() and error(), similar to message(),
    warning() and stop() respectively. However, the new functions can have a 'level'
    associated with them, so that when executed the global level option determines whether
    they are shown or not. This allows debug modes, outputting more information. The can also
    output all messages to a log file.
Allows for the computation of mSHAP values on
    two-part models as proposed  by Matthews, S. and Hartman, B. 
    (2021) <arXiv:2106.08990>.
    Also contains functions for simple plotting of
    the results (or any SHAP values). For information about the
    TreeSHAP algorithm that mSHAP builds on, see Lundberg, S.M.,
    Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz,
    R., Himmelfarb, J., Bansal, N., Lee, S.I. (2020)
    <doi:10.1038/s42256-019-0138-9>.
The Molecular Signatures Database ('MSigDB') is one of the most widely 
    used and comprehensive databases of gene sets for performing gene set 
    enrichment analysis <doi:10.1016/j.cels.2015.12.004>. The 'msig' package provides 
    you with powerful, easy-to-use and flexible query functions for the 'MsigDB' 
    database.
        There are 2 query modes in the 'msig' package: online query and local query. 
    Both queries contain 2 steps: gene set name and gene.
        The online search is divided into 2 modes: registered search and 
    non-registered browse. For registered search, email that you registered should 
    be provided.
        Local queries can be made from local database, which can be updated by msig_update() function.
Provides the 'Molecular Signatures Database' (MSigDB) gene
    sets typically used with the 'Gene Set Enrichment Analysis' (GSEA)
    software (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>,
    Liberzon et al. 2015 <doi:10.1016/j.cels.2015.12.004>) in a standard R
    data frame with key-value pairs. The package includes the human genes
    as listed in MSigDB as well as the corresponding symbols and IDs for
    frequently studied model organisms such as mouse, rat, pig, fly, and
    yeast.
Utility functions for mutational signature analysis.
             This package provides two groups of functions. One is for
             dealing with mutational signature "exposures" (i.e. the
             counts of mutations in a sample that are
             due to each mutational signature). The other group of
             functions is for matching two sets of mutational
             signatures. The match minimizes the total distance
             between paired signatures by using the "Hungarian 
             algorithm" described in : 
             Kuhn, H. W. (1955) <doi:10.1002/nav.3800020109>.
             'mSigTools' stands for mutational Signature analysis Tools.
Micro simulation model to reproduce natural history of cervical cancer and cost-effectiveness evaluation of prevention strategies. See Georgalis L, de Sanjose S, Esnaola M, Bosch F X, Diaz M (2016) <doi:10.1097/CEJ.0000000000000202> for more details.
The 'MSiP' is a computational approach to predict protein-protein interactions from large-scale affinity purification mass 'spectrometry' (AP-MS) data. This approach includes both spoke and matrix models for interpreting AP-MS data in a network context. The "spoke" model considers only bait-prey interactions, whereas the "matrix" model assumes that each of the identified proteins (baits and prey) in a given AP-MS experiment interacts with each of the others. The spoke model has a high false-negative rate, whereas the matrix model has a high false-positive rate. Although, both statistical models have merits, a combination of both models has shown to increase the performance of machine learning classifiers in terms of their capabilities in discrimination between true and false positive interactions.
An R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.
Programmatic access to 'OncoTree' API
    <http://oncotree.mskcc.org/>. Get access to tumor main types, identifiers
    and utility routines to map across to other tumor classification systems.
Analysis of annual average ocean water level time series
    from long (minimum length 80 years) individual records, providing improved
    estimates of trend (mean sea level) and associated real-time velocities and
    accelerations. Improved trend estimates are based on Singular Spectrum Analysis
    methods. Various gap-filling options are included to accommodate incomplete time
    series records. The package also contains a forecasting module to consider the
    implication of user defined quantum of sea level rise between the end of the
    available historical record and the year 2100. A wide range of screen and pdf
    plotting options are available in the package.
Functions for fitting continuous-time Markov and hidden
    Markov multi-state models to longitudinal data.  Designed for
    processes observed at arbitrary times in continuous time (panel data)
    but some other observation schemes are supported. Both Markov
    transition rates and the hidden Markov output process can be modelled
    in terms of covariates, which may be constant or piecewise-constant
    in time.
Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.
Functions and datasets from Hilbe, J.M., and Robinson, A.P. 2013. Methods of Statistical Model Estimation. Chapman & Hall / CRC.
A fast and general method for restructuring classical longitudinal data into
    augmented ones. The reason for this is to facilitate the modeling of longitudinal data under
    a multi-state framework using the 'msm' package.
Data sets in the book entitled "Multivariate Statistical Methods with R Applications", H.Bulut (2018). 
             The book was published in Turkish and the original name of this book will be "R Uygulamalari ile Cok Degiskenli Istatistiksel Yontemler".
Multivariate Analysis methods and data sets used
    in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>.
    This also serves as a companion package for the 
    STAT 571: Multivariate Analysis course offered by the Department of Statistics
    at the University of Illinois at Urbana-Champaign ('UIUC').
Given the maximum available sample size (N) for an experiment, and the target levels of Type I 
             and II error probabilities, this package designs a modified SPRT (MSPRT). For any designed MSPRT 
             the package can also obtain its operating characteristics and implement the test for a given 
             sequentially observed data. The MSPRT is defined in a manner very similar to Wald's initial proposal. 
             The proposed test has shown evidence of reducing the average sample size required to perform 
             statistical hypothesis tests at specified levels of significance and power. Currently, the package
             implements one-sample proportion tests, one and two-sample z tests, and one and two-sample 
             t tests. A brief user guidance for this package is provided below. One can also refer to the 
             supplemental information for the same.
This is a toolkit for multivariate process monitoring. It computes several multivariate control charts e.g. Hotelling, Chi-squared, MEWMA, MCUSUM and Generalized Variance. Ten didactic datasets are included. It also contains some tools for assessing multivariate normality e.g. Mardia's, Royston's and Henze-Zirkler's tests. Please, see the NEWS file for the latest changes in the package.
This is a implementation of design methods for multi-state reliability demonstration tests (MSRDT) with failure count data, 
    which is associated with the work from the published paper "Multi-state Reliability Demonstration Tests" by Suiyao Chen et al. (2017) <doi:10.1080/08982112.2017.1314493>.
    It implements two types of MSRDT, multiple periods (MP) and multiple failure modes (MFM). 
    For MP, two different scenarios with criteria on cumulative periods (Cum) or separate periods (Sep) are implemented respectively.
    It also provides the implementation of conventional design method, namely binomial tests for failure count data.
Provides methods to perform parameter estimation and 
  make analysis of multivariate observed outcomes through time which depends 
  on a latent state variable. All methods scale well in the dimension 
  of the observed outcomes at each time point. The package contains an 
  implementation of a Laplace approximation, particle filters like 
  suggested by Lin, Zhang, Cheng, & Chen (2005)
  <doi:10.1198/016214505000000349>, and the gradient and observed information
  matrix approximation suggested by Poyiadjis, Doucet, & Singh (2011) 
  <doi:10.1093/biomet/asq062>.
A collection of helper functions for analyzing Second Primary Cancer data, 
    including functions to reshape data, to calculate patient states and analyze cancer incidence.
Tools that extend the functionality of the 'RODBC' package to work
  with Microsoft SQL Server databases. Makes it easier to browse the database
  and examine individual tables and views.
Constructs trees for multivariate survival data using marginal and frailty models.
    Grows, prunes, and selects the best-sized tree.
Contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models, see Putter, Fiocco, Geskus (2007) <doi:10.1002/sim.2712>.
This is a tool for epidemiologist, medical data analyst, 
    medical or public health professionals. It contains three domains of functions:
    1) data management, 2) statistical analysis and 3) calculating 
    epidemiological measures.
Implements a minimum-spanning-tree-based heuristic for k-means clustering using a union-find disjoint set and the algorithm in Kruskal (1956) <doi:10.1090/S0002-9939-1956-0078686-7>.
A collection of statistical tests for the detection of differential
    item functioning (DIF) in multistage tests. Methods entail logistic regression,
    an adaptation of the simultaneous item bias test (SIBTEST), and various score-based tests.
    The presented tests provide itemwise test for DIF along categorical, ordinal or metric covariates. Methods for uniform and non-uniform 
    DIF effects are available depending on which method is used.
A new approach to detect change points based on smoothing and multiple testing, which is for long data sequence modeled as piecewise constant functions plus stationary Gaussian noise, see Dan Cheng and Armin Schwartzman (2015) <arXiv:1504.06384>.
Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2022) <https://grodriguezrondon.com/research/> that can be used to identify the number of regimes in Markov switching models.
Software to aid in modeling and analyzing mass-spectrometry-based
    proteome melting data. Quantitative data is imported and normalized and
    thermal behavior is modeled at the protein level. Methods exist for
    normalization, modeling, visualization, and export of results. For a
    general introduction to MS-based thermal profiling, see Savitski et al.
    (2014) <doi:10.1126/science.1255784>.
Implements the MST-kNN clustering algorithm which was proposed by Inostroza-Ponta, M. (2008) <https://trove.nla.gov.au/work/28729389?selectedversion=NBD44634158>.
Generation of response patterns under dichotomous and polytomous computerized multistage testing (MST) framework. It holds various item response theory (IRT) and score-based methods to select the next module and estimate ability levels (Magis, Yan and von Davier (2017, ISBN:978-3-319-69218-0)).
Interface for creating data sets and extracting data through the
    'MicroStrategy' REST API. Access the demo API at
    <https://demo.microstrategy.com/MicroStrategyLibrary/api-docs/index.html>.
Estimators for multivariate symmetrical uncertainty based
    on the work of Gustavo Sosa et al. (2016) <arXiv:1709.08730>,
    total correlation, information gain and symmetrical uncertainty of
    categorical variables.
Estimation, inference and diagnostics for Univariate Autoregressive Markov Switching Models for Linear and Generalized Models. Distributions for the series include gaussian, Poisson, binomial and gamma cases. The EM algorithm is used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).
Functions for metabolomics data analysis: data preprocessing, 
  orthogonal signal correction, PCA analysis, PCA-DA analysis, 
	PLS-DA analysis, classification, feature selection, correlation 
	analysis, data visualisation and re-sampling strategies.
Build multiscalar territorial analysis based on various contexts.
Perform multi-trait rare-variant association tests using the summary statistics and adjust for possible sample overlap. Package is based on "Multi-Trait Analysis of Rare-Variant Association Summary Statistics using MTAR" by Luo, L., Shen, J., Zhang, H., Chhibber, A. Mehrotra, D.V., Tang, Z., 2019 (submitted).
The purpose of this package is to share a collection of functions the author wrote during weekends for managing
    kitchen and garden tasks, e.g. making plant growth charts or Thanksgiving kitchen schedule charts, etc. 
    Functions might include but not limited to:
    (1) aiding summarizing time related data; 
    (2) generating axis transformation from data; and
    (3) aiding Markdown (with html output) and Shiny file editing.
Implements Mander & Thompson's (2010) 
    <doi:10.1016/j.cct.2010.07.008> methods for two-stage designs
    optimal under the alternative hypothesis for phase II [cancer] trials.  Also
    provides an implementation of Simon's (1989) 
    <doi:10.1016/0197-2456(89)90015-9> original methodology and 
    allows exploration of the operating characteristics of sub-optimal designs.
The Mass Transportation Distance rank histogram was developed to assess the reliability of scenarios with equal or different probabilities of occurrence <doi:10.1002/we.1872>.
Several robust estimators for linear regression and variable selection are provided. 
              Included are Maximum tangent likelihood estimator (Qin, et al., 2017), 
              least absolute deviance estimator and Huber regression. The penalized version of each of these 
              estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to 
              produce consistent estimates for both fixed and high-dimensional settings.
Calculate Sample Size and Power for
    Association Studies Involving Mitochondrial DNA Haplogroups.
    Based on formulae by Samuels et al. AJHG, 2006. 78(4):713-720. <DOI:10.1086/502682>.
Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the Expectation-Maximization (EM) algorithm that not only can effectively utilize unknown similarity between related tasks but is also robust against a fraction of outlier tasks from arbitrary sources. The proposed procedure is shown to achieve minimax optimal rate of convergence for both parameter estimation error and the excess mis-clustering error, in a wide range of regimes. Moreover, we generalize our approach to tackle the problem of transfer learning for GMMs, where similar theoretical results are derived. Finally, we demonstrate the effectiveness of our methods through simulations and a real data analysis. To the best of our knowledge, this is the first work studying multi-task and transfer learning on GMMs with theoretical guarantees. This package implements the algorithms proposed in Tian, Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>.
An implementation of Multi-Task Logistic Regression (MTLR) for R. 
  This package is based on the method proposed by Yu et al. (2011) which utilized MTLR for generating individual survival curves
  by learning feature weights which vary across time. This model was further extended to account for left and interval censored data.
Simultaneous multiple outcomes prediction based on revised stacking algorithms, which enables the integration of information from predictions of individual models. An implementation of methodologies proposed in our paper: Li Xing, Mary L Lesperance, Xuekui Zhang. (2019)  Bioinformatics, "Simultaneous prediction of multiple outcomes using revised stacking algorithms" <doi:10.1093/bioinformatics/btz531>.
Use morphological image processing and edge detection algorithms to automatically measure tree ring widths on digital images. Users can also manually mark tree rings on species with complex anatomical structures. The arcs of inner-rings and angles of successive inclined ring boundaries are used to correct ring-width series. The package provides a Shiny-based application, allowing R beginners to easily analyze tree ring images and export ring-width series in standard file formats.
Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis.  (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models.  (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics.  (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.
This is an EM algorithm based method for imputation of missing values in multivariate normal time series. The imputation algorithm accounts for both spatial and temporal correlation structures. Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.
Mahalanobis-Taguchi (MT) system is a collection of multivariate
    analysis methods developed for the field of quality engineering. MT system
    consists of two families depending on their purpose. One is a family of
    Mahalanobis-Taguchi (MT) methods (in the broad sense) for diagnosis (see
    Woodall, W. H., Koudelik, R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and
    Carvounis, C. P. (2003) <doi:10.1198/004017002188618626>) and the other is a
    family of Taguchi (T) methods for forecasting (see Kawada, H., and Nagata, Y.
    (2015) <doi:10.17929/tqs.1.12>). The MT package contains three basic methods
    for the family of MT methods and one basic method for the family of T
    methods. The MT method (in the narrow sense), the Mahalanobis-Taguchi
    Adjoint (MTA) methods, and the Recognition-Taguchi (RT) method are for the
    MT method and the two-sided Taguchi (T1) method is for the family of T
    methods. In addition, the Ta and Tb methods, which are the improved versions
    of the T1 method, are included.
Generates mid upper arm circumference (MUAC) and body mass index (BMI) 
            for age z-scores and percentiles based on LMS method for children and 
            adolescents up to 19 years that can be used to assess nutritional and health status and
            define risk of adverse health events.
Nonparametric approach to estimate the location of block boundaries (change-points) of 
    non-overlapping blocks in a random symmetric matrix which consists of random variables whose 
    distribution changes from block to block.
    BRAULT Vincent, OUADAH Sarah, SANSONNET Laure and LEVY-LEDUC Celine (2017) <doi:10.1016/j.jmva.2017.12.005>.
A data package containing public domain information on requests made by the
             'MuckRock' (https://www.muckrock.com/) project under the United States
             Freedom of Information Act.
Formatting and structuring multi-parameter spatiotemporal data
  is often a time-consuming task. This package offers functions and data structures 
  designed to easily organize and visualize these data for applications in geology, 
  paleolimnology, dendrochronology, and paleoclimate. See Dunnington and Spooner (2018)
  <doi:10.1139/facets-2017-0026>.
Compute a density estimate from a vector of right-censored survival time using kernel functions.
Implements an estimator for relative risk based on the median unbiased estimator. The relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. The details of the method are available in Carter et al (2010) <doi:10.1111/j.1467-9876.2010.00711.x>.
Produces a smooth estimate of the hazard
  function for censored data.
Constructing hierarchical voronoi tessellations for a given data set and overlay heatmap for variables at various levels of the tessellations for in-depth data analysis. See <https://en.wikipedia.org/wiki/Voronoi_diagram> for more information. Credits to Mu Sigma for their continuous support throughout the development of the package.
A simple tool allowing users to easily and dynamically explore or document a data set using a tree structure.
Generates Muller plot from parental/genealogy/phylogeny information and population/abundance/frequency dynamics data.
    Muller plots are plots which combine information about succession of different OTUs (genotypes, phenotypes, species, ...) and information about dynamics of their abundances (populations or frequencies) over time. They are powerful and fascinating tools to visualize evolutionary dynamics. They may be employed also in study of diversity and its dynamics, i.e. how diversity emerges and how changes over time. They are called Muller plots in honor of Hermann Joseph Muller which used them to explain his idea of Muller's ratchet (Muller, 1932, American Naturalist).
    A big difference between Muller plots and normal box plots of abundances is that a Muller plot depicts not only the relative abundances but also succession of OTUs based on their genealogy/phylogeny/parental relation. In a Muller plot, horizontal axis is time/generations and vertical axis represents relative abundances of OTUs at the corresponding times/generations. Different OTUs are usually shown with polygons with different colors and each OTU originates somewhere in the middle of its parent area in order to illustrate their succession in evolutionary process.
    To generate a Muller plot one needs the genealogy/phylogeny/parental relation of OTUs and their abundances over time.
    MullerPlot package has the tools to generate Muller plots which clearly depict the origin of successors of OTUs.
Allows the user to create graph with multiple layers. The user can also modify the layers, the nodes, and the edges. The graph can also be visualized.
    Zaynab Hammoud and Frank Kramer (2018) <doi:10.3390/genes9110519>.
    More about multilayered graphs and their usage can be found in our review paper:
    Zaynab Hammoud and Frank Kramer (2020) <doi:10.1186/s41044-020-00046-0>.
Computes efficient data distributions from highly inconsistent datasets with many missing values using multi-set intersections. Based upon hash functions, 'mulset' can quickly identify intersections from very large matrices of input vectors across columns and rows and thus provides scalable solution for dealing with missing values. Tomic et al. (2019) <doi:10.1101/545186>.
Integer Programming Formulations Applied to Univariate and Multivariate Allocation Problems.
Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in 'Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.
Contains auxiliary routines for influx software. This packages is not intended to be used directly. Influx was published here: Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.
Simultaneous tests and confidence intervals
  for general linear hypotheses in parametric models, including 
  linear, generalized linear, linear mixed effects, and survival models.
  The package includes demos reproducing analyzes presented
  in the book "Multiple Comparisons Using R" (Bretz, Hothorn, 
  Westfall, 2010, CRC Press).
Convert a logical vector or a vector of
    p-values or a correlation, difference, or distance
    matrix into a display identifying the pairs for
    which the differences were not significantly
    different.  Designed for use in conjunction with
    the output of functions like TukeyHSD, dist{stats},
    simint, simtest, csimint, csimtest{multcomp},
    friedmanmc, kruskalmc{pgirmess}.
Allows to perform the multivariate version of the Diebold-Mariano test for equal predictive ability of multiple forecast comparison. Main reference: Mariano, R.S., Preve, D. (2012) <doi:10.1016/j.jeconom.2012.01.014>.
Equivalence tests and related confidence intervals for the
 comparison of two treatments, simultaneously for one or many normally
 distributed, primary response variables (endpoints). The step-up
 procedure of Quan et al. (2001) is both applied for differences and
 extended to ratios of means. A related single-step procedure is also
 available.
Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints.
    The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative
    hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate
    permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated 
    that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal
    significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved
    by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple
    testing procedures.
GEE solver for correlated nominal or ordinal multinomial responses
    using a local odds ratios parameterization.
Provides a multi action button for usage in 'shiny'
    applications.
The base apply function and its variants, as well as the related
    functions in the 'plyr' package, typically apply user-defined functions to a
    single argument (or a list of vectorized arguments in the case of mapply). The
    'multiApply' package extends this paradigm with its only function, Apply, which
    efficiently applies functions taking one or a list of multiple unidimensional
    or multidimensional arrays (or combinations thereof) as input. The input
    arrays can have different numbers of dimensions as well as different dimension
    lengths, and the applied function can return one or a list of unidimensional or
    multidimensional arrays as output. This saves development time by preventing the
    R user from writing often error-prone and memory-inefficient loops dealing with
    multiple complex arrays. Also, a remarkable feature of Apply is the transparent
    use of multi-core through its parameter 'ncores'. In contrast to the base apply
    function, this package suggests the use of 'target dimensions' as opposite
    to the 'margins' for specifying the dimensions relevant to the function to be
    applied.
Efficient finite difference method for valuing European and American multi-asset options.
Estimation routines for several classes of affine term structure of interest rates models. All the models are based on the single-country unspanned macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014) <doi:10.1111/jofi.12131>. Multicountry extensions such as the ones of Jotikasthira, Le, and Lundblad (2015) <doi:10.1016/j.jfineco.2014.09.004> and Candelon and Moura (2021) <http://hdl.handle.net/2078.1/249985> are also available.
Computationally efficient functions to provide direct likelihood-based
    inference for partially-observed multivariate birth-death processes.  Such processes
    range from a simple Yule model to the complex susceptible-infectious-removed model
    in disease dynamics.  Efficient likelihood evaluation facilitates maximum likelihood
    estimation and Bayesian inference.
Provides a GUI with which users can construct and interact
        with Multibiplot Analysis.
Functions and datasets to support Smilde, Næs and Liland (2021, ISBN: 978-1-119-60096-1) 
   "Multiblock Data Fusion in Statistics and Machine Learning - Applications in the Natural and Life Sciences". 
   This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting 
   functions, several real data sets and six vignettes covering a range different applications.
Evaluate hypotheses concerning the distribution of multinomial
  proportions using bridge sampling. The bridge sampling routine is able to
  compute Bayes factors for hypotheses that entail inequality constraints,
  equality constraints, free parameters, and mixtures of all three. These
  hypotheses are tested against the encompassing hypothesis, that all parameters
  vary freely or against the null hypothesis that all category proportions are equal.
  For more information see Sarafoglou et al. (2020) <doi:10.31234/osf.io/bux7p>.
Implements a generalization of the Cochran-Armitage trend test to
    multinomial data. In addition to an overall test, multiple testing adjusted
    p-values for trend in individual outcomes and power calculation is
    available.
Provides a basic interface for accessing annotation data from
   the Multi-CAST collection, a database of spoken natural language texts
   edited by Geoffrey Haig and Stefan Schnell. The collection draws from a
   diverse set of languages and has been annotated across multiple levels.
   Annotation data is downloaded on request from the servers of the
   University of Bamberg. See the Multi-CAST website
   <https://multicast.aspra.uni-bamberg.de/> for more information and a list
   of related publications.
Given a set of models for which a measure of model (mis)fit and model complexity is provided, CHull(), developed by Ceulemans and Kiers (2006) <doi:10.1348/000711005X64817>, determines the models that are located on the boundary of the convex hull and selects an optimal model by means of the scree test values.
A toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on 'switchBox' package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret.  For complex problems RF performs better.  Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...).  Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model.  We provide a detailed description with each function in this package to explain the filtration and training methodology in each line. Reference: Marzouka & Eriksson (2021) <doi:10.1093/bioinformatics/btab088>.
A toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) <doi:10.1016/j.jmva.2016.04.007>. Future development will extend the package to higher-dimensional data.
The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed.
Add multiple colors to text that is printed to the console.
A set of tools to permute multisets without loops or hash tables and to generate integer partitions. The permutation functions are based on C code from Aaron Williams. Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. Loopless Generation of Multiset Permutations by Prefix Shifts. SODA 2009, Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from <http://tamivox.org/dave/multinomial/index.html> and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here <https://jeromekelleher.net/category/combinatorics.html>. The C++ code and Python code are distributed without conditions.
Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Lönnqvist & Ilmarinen (2021) <doi:10.1007/s11109-021-09681-2> and Ilmarinen et al. (2022) <doi:10.1177/08902070221088155>.
    Includes tools that help in understanding difference score reliability, predictions of difference score variables, conditional intra-class correlations, and heterogeneity of variance estimates. Package development was supported by the Academy of Finland research grant 338891.
Code to support a systems biology research program from inception through publication.  The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations.  The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.  As a result, we encourage suggested additions to the package, both methodological and graphical.
Partition a data frame across multiple worker processes to
    provide simple multicore parallelism.
An implementation for multivariate functional additive mixed
    models (multiFAMM), see Volkmann et al. (2021, <arXiv:2103.06606>). It builds on developed methods for univariate sparse 
    functional regression models and multivariate functional principal component
    analysis. This package contains the function to run a multiFAMM and some
    convenience functions useful when working with large models. An additional 
    package on GitHub contains more convenience functions to reproduce the 
    analyses of the corresponding paper 
    (<https://github.com/alexvolkmann/multifammPaper>).
A suite of functions for performing analyses, based on a multiverse approach, for conditioning data. Specifically, given the appropriate data, the functions are able to perform t-tests, analyses of variance, and mixed models for the provided data and return summary statistics and plots. The function is also able to return for all those tests p-values, confidence intervals, and Bayes factors. The methods are described in Lonsdorf, Gerlicher, Klingelhofer-Jens, & Krypotos <doi:10.31234/osf.io/2z6pd>.
Test for independence of two random vectors, learn and report the dependency structure. For more information, see Gorsky, Shai and Li Ma, Multiscale Fisher's Independence Test for Multivariate Dependence, Biometrika, accepted, January 2022.
Methods for the analysis of how ecological drivers affect the
    multifunctionality of an ecosystem based on methods of Byrnes et al. 
    2016 <doi:10.1111/2041-210X.12143> and Byrnes et al. 
    2022 <doi:10.1101/2022.03.17.484802>. Most standard
    methods in the literature are implemented (see vignettes) in a tidy
    format.
Read a table of fixed width formatted data of different types into
    a data.frame for each type.
Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2022), <arXiv:2208.14721>.
Functions to plot and manipulate multigraphs, signed and valued graphs, bipartite graphs, multilevel graphs, and Cayley graphs with various layout options.
Methods and models for analysing multigraphs as introduced by Shafie (2015) <doi:10.21307/joss-2019-011>, including methods to study local and global properties <doi:10.1080/0022250X.2016.1219732> and goodness of fit tests.
Multivariate analysis methods including principal component analysis,
    partial least square regression, and multiblock analysis to describe,
    summarize, and visualize data with a group structure.
Two method new of multigroup and simulation of data.
  The first technique called multigroup PCA (mgPCA) this multivariate exploration approach
  that has the idea of considering the structure of groups and / or different types
  of variables. On the other hand, the second multivariate technique called Multigroup Dimensionality
  Reduction (MDR) it is another multivariate exploration method that is based on
  projections. In addition, a method called Single Dimension Exploration (SDE) was
  incorporated for to analyze the exploration of the data. It could help us in a better
  way to observe the behavior of the multigroup data with certain variables of interest.
Wrapper around the Unix join facility which is more efficient than the built-in R routine merge().
 The package enables the joining of multiple files on disk at once. 
 The files can be compressed and various filters can be deployed before joining.
 Compiles only under Unix.
Estimation and inference for multiple kink quantile regression. A bootstrap restarting iterative segmented quantile algorithm is proposed to estimate the multiple kink quantile regression model conditional on a given number of change points. The number of kinks is also allowed to be unknown. In such case, the backward elimination algorithm and the bootstrap restarting iterative segmented quantile algorithm are combined to select the number of change points based on a quantile BIC. A score-type based test statistic is also developed for testing the existence of kink effect. The package is based on the paper, "Wei Zhong, Chuang Wan and Wenyang Zhang (2020). Estimation and inference for multi-kink quantile regression, submitted".
A flexible, efficient implementation of multilateral price index calculations.
             Includes common methods focused on time product dummy regression and GEKS variations.
             Allows for extension of the methods through automatic window splicing.
             See Krsinich (2016) <doi: 10.1515/jos-2016-0021>.
Computing transitive (and non-transitive) index numbers (Coelli et al., 2005 <doi:10.1007/b136381>) for cross-sections and panel data. For the calculation of transitive indexes, the EKS (Coelli et al., 2005 <doi:10.1007/b136381>; Rao et al., 2002 <doi:10.1007/978-1-4615-0851-9_4>) and Minimum spanning tree (Hill, 2004 <doi:10.1257/0002828043052178>) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).
Tools used by organizational researchers for the analysis of multilevel data. Includes four broad sets of tools. First, functions for estimating within-group agreement and reliability indices. Second, functions for manipulating multilevel and longitudinal (panel) data. Third, simulations for estimating power and generating multilevel data. Fourth, miscellaneous functions for estimating reliability and performing simple calculations and data transformations.
Bindings for hierarchical regression models for use with the
    'parsnip' package. Models include longitudinal generalized linear
    models (Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and
    mixed-effect models (Pinheiro and Bates)
    <doi:10.1007/978-1-4419-0318-1_1>.
Conducts and visualizes propensity score analysis for multilevel, 
  or clustered data. Bryer & Pruzek (2011) <doi:10.1080/00273171.2011.636693>.
Effect sizes, diagnostics and performance metrics for
  multilevel and mixed effects models.
  Includes marginal and conditional 'R2' estimates for linear mixed effects models
  based on Johnson (2014) <doi:10.1111/2041-210X.12225>.
Provides install functions of other languages 
             such as 'java', 'python'.
Traditional and spatial capture-mark-recapture analysis with
    multiple non-invasive marks. The models implemented in 'multimark' combine
    encounter history data arising from two different non-invasive "marks",
    such as images of left-sided and right-sided pelage patterns of bilaterally
    asymmetrical species, to estimate abundance and related demographic
    parameters while accounting for imperfect detection. Bayesian models are
    specified using simple formulae and fitted using Markov chain Monte Carlo.
    Addressing deficiencies in currently available software, 'multimark' also
    provides a user-friendly interface for performing Bayesian multimodel
    inference using non-spatial or spatial capture-recapture data consisting of a single
    conventional mark or multiple non-invasive marks. See McClintock (2015) <doi:10.1002/ece3.1676> and Maronde et al. (2020) <doi:10.1002/ece3.6990>.
A latent variable model based on factor analytic and mixture of experts models, designed to infer food intake from multiple biomarkers data. The model is framed within a Bayesian hierarchical framework, which provides flexibility to adapt to different biomarker distributions and facilitates inference on food intake from biomarker data alone, along with the associated uncertainty. Details are in D'Angelo, et al. (2020) <arXiv:2006.02995>.
Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques <DOI:10.18637/jss.v097.i09>.
Identifying comorbidities, frailty, and multimorbidity in claims 
    and administrative data is often a duplicative process.
    The functions contained in this package are meant to first prepare the data to a format
    acceptable by all other packages, then provide a uniform and simple approach to
    generate comorbidity and multimorbidity metrics based on these claims data. The package
    is ever evolving to include new metrics, and is always looking for new measures to include.
    The citations used in this package include the following publications: 
    Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M. Coffey (1998) <doi:10.1097/00005650-199801000-00004>,
    Brian J Moore, Susan White, Raynard Washington, et al. (2017) <doi:10.1097/MLR.0000000000000735>,
    Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald MacKenzie (1987) <doi:10.1016/0021-9681(87)90171-8>,
    Richard A. Deyo, Daniel C. Cherkin, Marcia A. Ciol (1992) <doi:10.1016/0895-4356(92)90133-8>,
    Hude Quan, Vijaya Sundararajan, Patricia Halfon, et al. (2005) <doi:10.1097/01.mlr.0000182534.19832.83>,
    Dae Hyun Kim, Sebastian Schneeweiss, Robert J Glynn, et al. (2018) <doi:10.1093/gerona/glx229>,
    Melissa Y Wei, David Ratz, Kenneth J Mukamal (2020) <doi:10.1111/jgs.16310>,
    Kathryn Nicholson, Amanda L. Terry, Martin Fortin, et al. (2015) <doi:10.15256/joc.2015.5.61>,
    Martin Fortin, José Almirall, and Kathryn Nicholson (2017)<doi:10.15256/joc.2017.7.122>.
Functions for the creation/generation and analysis of multilayer social networks <doi:10.18637/jss.v098.i08>.
Analyze multilevel networks as described in Lazega et al (2008)
    <doi:10.1016/j.socnet.2008.02.001> and in Lazega and Snijders 
    (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an 
    extension to 'igraph'.
Network meta-analysis and network meta-regression models for 
    aggregate data, individual patient data, and mixtures of both individual 
    and aggregate data using multilevel network meta-regression as described by
    Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a
    Bayesian framework using 'Stan'.
An implementation of a method for building simultaneous confidence intervals for the probabilities of a multinomial distribution given a set of observations, proposed by Sison and Glaz in their paper:
        Sison, C.P and J. Glaz. Simultaneous confidence intervals and sample size determination for multinomial proportions. Journal of the American Statistical Association, 90:366-369 (1995). 
        The method is an R translation of the SAS code implemented by May and Johnson in their paper:
        May, W.L. and W.D. Johnson. Constructing two-sided simultaneous confidence intervals for multinomial proportions for small counts in a large number of cells. Journal of Statistical Software 5(6) (2000). 
        Paper and code available at <DOI:10.18637/jss.v005.i06>.
Methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2022) <DOI:10.48550/arXiv.2207.13984>. These models are estimated under  a frequentist as well as a Bayesian setup using the Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC), respectively. The (unknown) number of clusters is selected according to the Integrated Completed Likelihood criterion (for the frequentist model), and estimating the number of non-empty components using overfitting mixture models after imposing suitable sparse prior assumptions on the mixing proportions (in the Bayesian case), see Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>. In the latter case, various MCMC chains run in parallel and are allowed to switch states. The final MCMC output is suitably post-processed in order to undo label switching using the Equivalence Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>.
Implements Gibbs sampling and Bayes factors for multinomial models with
    linear inequality constraints on the vector of probability parameters. As
    special cases, the model class includes models that predict a linear order 
    of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models 
    assuming that the parameter vector p must be inside the convex hull of a 
    finite number of predicted patterns (i.e., vertices). A formal definition of 
    inequality-constrained multinomial models and the implemented computational
    methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). 
    Multinomial models with linear inequality constraints: Overview and improvements 
    of computational methods for Bayesian inference. Journal of Mathematical 
    Psychology, 91, 70-87. <doi:10.1016/j.jmp.2019.03.004>.
    Inequality-constrained multinomial models have applications in the area of 
    judgment and decision making to fit and test random utility models  
    (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of 
    preferences. Psychological Review, 118, 42–56, <doi:10.1037/a0021150>) or to 
    perform outcome-based strategy classification to select the decision strategy 
    that provides the best account for a vector of observed choice frequencies 
    (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information 
    processing to decisions: Formalizing and comparing probabilistic choice models. 
    Cognitive Psychology, 96, 26–40. <doi:10.1016/j.cogpsych.2017.05.003>).
Provides implementations of functions that can be used to test multivariate integration routines. The package covers five different integration domains (unit hypercube, unit ball, unit sphere, standard simplex and R^n). For each domain several functions with different properties (smooth, non-differentiable, ...) are available. The functions are available in all dimensions n >= 1. For each function the exact value of the integral is known and implemented to allow testing the accuracy of multivariate integration routines. Details on the available test functions can be found at on the development website.
Matching algorithm based on network-flow structure. 
  Users are able to modify the emphasis on three different 
  optimization goals: two different distance measures and 
  the number of treated units left unmatched. The method is proposed by Pimentel
  and Kelz (2019) <doi:10.1080/01621459.2020.1720693>.
  The 'rrelaxiv' package, which provides an alternative solver for
	the underlying network flow problems, carries an
	academic license and is not available on CRAN, but
	may be downloaded from Github at 
	<https://github.com/josherrickson/rrelaxiv/>.
Spatio-temporal multivariate occupancy models can handle multiple species in occupancy models.  This method for fitting such models is described in Hepler and Erhardt (2021) "A spatiotemporal model for multivariate occupancy data" <https://onlinelibrary.wiley.com/doi/abs/10.1002/env.2657>.
A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006) <DOI:10.1080/10629360600569246>.
Tools to create a layout for figures made of multiple panels, and
    to fill the panels with base, 'lattice', 'ggplot2' and 'ComplexHeatmap'
    plots, grobs, as well as content from all image formats supported by
    'ImageMagick' (accessed through 'magick').
Performs genetic association tests between SNPs
        (one-at-a-time) and multiple phenotypes (separately or in joint
        model).
The iterative procedure estimates structural changes in the success probability of Bernoulli variables. It estimates the number and location of the breakpoints as well as the success probability of the different sequences between the breakpoints. In addition, it provides a graphical illustration of the result.
Provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) <doi:10.1111/iere.12131>. Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.
Fit Cox proportional hazard models with a weighted 
  partial likelihood. It handles one or multiple endpoints, additional matching 
  and makes it possible to reuse controls for other endpoints.
Tools to analysis of experiments having two or more quantitative explanatory variables and one quantitative dependent variable. Experiments can be without repetitions or with a statistical design (Hair JF, 2016) <ISBN: 13: 978-0138132637>. Pacote para uma analise de experimentos havendo duas ou mais variaveis explicativas quantitativas e uma variavel dependente quantitativa. Os experimentos podem ser sem repeticoes ou com delineamento estatistico (Hair JF, 2016) <ISBN: 13: 978-0138132637>.
An implementation of the additive (Gurevitch et al., 2000 <doi:10.1086/303337>) 
    and multiplicative (Lajeunesse, 2011 <doi:10.1890/11-0423.1>) factorial null models for 
    multiple stressor data (Burgess et al., 2021 <doi:10.1101/2021.07.21.453207>). Effect sizes 
    are able to be calculated for either null model, and subsequently classified into one of 
    four different interaction classifications (e.g., antagonistic or synergistic interactions). 
    Analyses can be conducted on data for single experiments through to large meta-analytical datasets. 
    Minimal input (or statistical knowledge) is required, with any output easily understood. 
    Summary figures are also able to be easily generated.
Algebraic procedures for the analysis of multiple social networks are delivered with this 
	    package as described in Ostoic (2020) <DOI:10.18637/jss.v092.i11>. Among other things, it 
	    makes it possible to create and manipulate multiplex, multimode, and multilevel network data 
	    with different formats. There are effective ways available to treat multiple networks with 
	    routines that combine algebraic systems like the partially ordered semigroup or the semiring 
	    structure with the relational bundles occurring in different types of multivariate network 
	    data sets. It also provides an algebraic approach for affiliation networks through Galois 
	    derivations between families of the pairs of subsets in the two domains.
Functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.
Various utilities to manipulate multivariate polynomials.
Fits the Multiple Random Dot Product Graph Model and performs a test
	 for whether two networks come from the same distribution. Both methods are 
	 proposed in Nielsen, A.M., Witten, D., (2018) "The Multiple Random Dot Product Graph
	 Model", arXiv preprint <arXiv:1811.12172> (Submitted to Journal of Computational
	 and Graphical Statistics).
Functions to calculate Unique Trait Combinations (UTC) and scaled
    Unique Trait Combinations (sUTC) as measures of multivariate richness. The
    package can also calculate beta-diversity for trait richness and can
    partition this into nestedness-related and turnover components. The code
    will also calculate several measures of overlap. See Keyel and Wiegand
    (2016) <doi:10.1111/2041-210X.12558> for more details.
Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization. Multiple high-dimensional data types that require penalization are allowed, as well as unpenalized variables. Paired and preferential data types can be specified. See Van de Wiel et al. (2021), <arXiv:2005.09301>.
Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.
Multiply robust estimation for population mean (Han and Wang 2013) <doi:10.1093/biomet/ass087>, regression analysis (Han 2014) <doi:10.1080/01621459.2014.880058> (Han 2016) <doi:10.1111/sjos.12177> and quantile regression (Han et al. 2019) <doi:10.1111/rssb.12309>.
Tools to solve real-world problems with multiple classes classifications by computing the areas under ROC and PR curve via micro-averaging and macro-averaging. The vignettes of this package can be found via <https://github.com/WandeRum/multiROC>. The methodology is described in V. Van Asch (2013) <https://www.clips.uantwerpen.be/~vincent/pdf/microaverage.pdf> and Pedregosa et al. (2011) <http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html>.
Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.
Tools necessary to reconstruct clonal affiliations from
    temporally and/or spatially separated measurements of viral
    integration sites. For this means it utilizes correlations present
    in the relative readouts of the integration sites. Furthermore,
    facilities for filtering of the data and visualization of different
    steps in the pipeline are provided with the package.
Calculates multi-scale geomorphometric terrain attributes from regularly gridded digital terrain models using a variable focal windows size (Misiuk et al. (2021) <doi:10.1080/01490419.2021.1925789>; Wilson et al. (2007) <doi:10.1080/01490410701295962>; Wood (1996) <https://hdl.handle.net/2381/34503>).
Uses multiple AUCs to select a combination of predictors when the outcome has multiple (ordered) levels and the focus is discriminating one particular level from the others. This method is most naturally applied to settings where the outcome has three levels. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://biostats.bepress.com/uwbiostat/paper423/>.)
Functions to perform sensitivity analysis on a model with multivariate output.
The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the 'training set' and applying the model in the 'test set'. This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly, as described in (Solanes et al., Psychiatry Research: Neuroimaging 2021, 314:111313). It currently supports the estimation of sensitivity, specificity, balanced accuracy (for binary or multinomial variables), the area under the curve, correlation, mean squarer error, and hazard ratio for binomial, multinomial, gaussian, and survival (time-to-event) outcomes.
Multisite causal mediation analysis using the methods proposed by Qin and Hong (2017) <doi:10.3102/1076998617694879>, Qin, Hong, Deutsch, and Bein (2019) <doi:10.1111/rssa.12446>, and Qin, Deutsch, and Hong (2021) <doi:10.1002/pam.22268>. It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects transmitted through one single mediator or two concurrent (conditionally independent) mediators. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. This package also provides a function that can further incorporate a sample weight and a nonresponse weight for multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse, to enhance both the internal validity and external validity. The package also provides a weighting-based balance checking function for assessing the remaining overt bias.
Computes the third multivariate cumulant of either the raw, centered or standardized data. Computes the main measures of multivariate skewness, together with their bootstrap distributions. Finally, computes the least skewed linear projections of the data.
Implements two versions of the algorithm namely: stochastic and batch. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.
The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.
Algorithms to build set partitions and commutator matrices and their use in the 
    construction of multivariate d-Hermite polynomials;  estimation and derivation of theoretical  vector moments and vector
    cumulants  of multivariate distributions; conversion formulae for multivariate moments and cumulants. Applications to
    estimation and derivation of multivariate measures of skewness and kurtosis;  estimation and derivation of asymptotic 
    covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis.
    The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods".
Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates. This package is an implementation of the method described in D.J. Thomson (1982) "Spectrum estimation and harmonic analysis" <doi:10.1109/PROC.1982.12433>.
Provides utility functions for multivariate analysis (factor analysis, discriminant analysis, and others). The package is primary written for the course Multivariate analysis and for the course Computer intensive methods at the masters program of Applied Statistics at University of Ljubljana.
Functions for simulating, estimating and forecasting stationary Vector Autoregressive (VAR) models for multiple subject data using the penalized multi-VAR framework in Fisher, Kim and Pipiras (2020) <arXiv:2007.05052>.
Distance multivariance is a measure of dependence which can be used to detect 
    and quantify dependence of arbitrarily many random vectors. The necessary functions are
    implemented in this packages and examples are given. It includes: distance multivariance, 
    distance multicorrelation, dependence structure detection, tests of independence and
    copula versions of distance multivariance based on the Monte Carlo empirical transform.
    Detailed references are given in the package description, as starting point for the 
    theoretic background we refer to:
    B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using 
    the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020), 
    <doi:10.1515/stat-2020-0001>.
Package with multivariate analysis methodologies for experiment evaluation.
	 The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA,
	 principal components, canonical variables, etc. (Pacote com metodologias de analise
	 multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade,
	 construi de dendogramas, obtem a MANOVA,  componentes principais, variáveis canônicas, etc.)
Models and predicts multiple output features in single random forest considering the 
    linear relation among the output features, see details in Rahman et al (2017)<doi:10.1093/bioinformatics/btw765>.
Fully parametric Bayesian multiple imputation framework for massive multivariate data of different variable types as seen in Demirtas, H. (2017) <doi:10.1007/978-981-10-3307-0_8>.
It performs variable selection in a multivariate linear model by estimating the covariance matrix 
 of the residuals then use it to remove the dependence that may exist among the responses and eventually performs variable selection by using the Lasso criterion.
 The method is described in the paper Perrot-Dockès et al. (2017) <arXiv:1704.00076>.
A multivariate generalization of the emulator package.
Implement 'multiverse' style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016)
    <doi:10.1177/1745691616658637>, (Dragicevic P., Jansen Y., Sarma A., Kay M., Chevalier F., 2019) <doi:10.1145/3290605.3300295> 
    to show the robustness of statistical inference. 'Multiverse analysis' is a philosophy of 
    statistical reporting where paper authors report the outcomes of many different statistical 
    analyses in order to show how fragile or robust their findings are. 
    The 'multiverse' package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm>
    allows users to concisely and flexibly implement 'multiverse-style' 
    analysis, which involve declaring alternate ways of performing an analysis step, in R and R Notebooks.
Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).
Computation of an estimation of the long-memory parameters and
     the long-run covariance matrix using a multivariate model
	 (Lobato (1999) <doi:10.1016/S0304-4076(98)00038-4>; Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>). Two semi-parametric methods are
	 implemented: a Fourier based approach (Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>) and a wavelet based
	 approach (Achard and Gannaz (2016) <doi:10.1111/jtsa.12170>).
Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) <doi:10.1080/10618600.2017.1401544>].  Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model.
Exports two functions implementing
    multi-way clustering using the method suggested by Cameron, Gelbach, &
    Miller (2011) and cluster (or block)
    bootstrapping for estimating variance-covariance matrices. Normal one and
    two-way clustering matches the results of other common statistical
    packages.  Missing values are handled transparently and rudimentary
    parallelization support is provided.
A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics.  Supported by grant NSF DMS 1712839.  See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.
In the case of multivariate ordinal responses, parameter estimates can be severely biased if personal response styles are ignored. This packages provides methods to account for personal response styles and to explain the effects of covariates on the response style, as proposed by Schauberger and Tutz 2021 <doi:10.1177/1471082X20978034>. The method is implemented both for the multivariate cumulative model and the multivariate adjacent categories model.
Multivariate version of the two-sample Gehan and logrank tests, as described in L.J Wei & J.M Lachin (1984) and Persson et al. (2019).
Implementation of commonly used p-value-based and
        parametric multiple testing procedures (computation of adjusted
        p-values and simultaneous confidence intervals) and parallel
        gatekeeping procedures based on the methodology presented in
        the book "Multiple Testing Problems in Pharmaceutical
        Statistics" (edited by Alex Dmitrienko, Ajit C. Tamhane and
        Frank Bretz) published by Chapman and Hall/CRC Press 2009.
Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.
Tools for performing model selection and model averaging. Automated
             model selection through subsetting the maximum model, with optional
             constraints for model inclusion. Model parameter and prediction
             averaging based on model weights derived from information criteria
             (AICc and alike) or custom model weighting schemes.
Fit multiplicative mixed models using maximum likelihood estimation via the Template
  Model Builder (TMB), Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM (2016) <doi:10.18637/jss.v070.i05>.
  One version of the multiplicative mixed model is applied in Piepho (1999) <doi:10.1111/j.0006-341X.1999.01120.x>.
  The package provides functions for calculating confidence intervals for the model parameters
  and for performing likelihood ratio tests.
Multidimensional unfolding using Schoenemann's algorithm for metric
   and Procrustes rotation of unfolding results.
Provides easy access to, and manipulation of, the Munsell 
    colours. Provides a mapping between Munsell's 
    original notation (e.g. "5R 5/10") and hexadecimal strings suitable 
    for use directly in R graphics. Also provides utilities 
    to explore slices through the Munsell colour tree, to transform 
    Munsell colours and display colour palettes.
Methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, CIE Lab, CIE Luv, or RGB.  Includes ISCC-NBS color block lookup.  Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".
Provides mailmerge methods for reading spreadsheets of addresses and other relevant information to create standardized but customizable letters.  Provides a method for mapping US ZIP codes, including those of letter recipients.  Provides a method for parsing and processing html code from online job postings of the American Political Science Association.
Data and code for the paper by Ehm, Gneiting, Jordan and
    Krueger ('Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet
    Representations, and Forecast Rankings', JRSS-B, 2016 <DOI:10.1111/rssb.12154>).
Calculates k-best solutions and costs for an assignment problem following the method outlined in Murty (1968) <doi:10.1287/opre.16.3.682>.
Sampling and evaluation methods to apply Monetary Unit Sampling (or in older literature Dollar Unit Sampling) during an audit of financial statements.
Provides a framework to factorise electromyography (EMG) data.
    Tools are provided for raw data pre-processing, non negative matrix factorisation,
    classification of factorised data and plotting of obtained outcomes.
    In particular, reading from ASCII files is supported, along with wide-used
    filtering approaches to process EMG data. All steps include one or more sensible
    defaults that aim at simplifying the workflow. Yet, all functions are largely
    tunable at need. Example data sets are included.
An aid for learning and using music theory. You can build chords, scales, and chord progressions using 12-note equal temperament tuning (12-ET) or user-defined tuning. Includes functions to visualize notes on a piano using ASCII plots in the console and to plot waveforms using base graphics. It allows simple playback of notes and chords using the 'audio' package.
Provides functions allowing for (1) easy aggregation of multivariate time series into custom time scales, (2) comparison of statistical summaries between different data sets at multiple time scales (e.g. observed and bias-corrected data), (3) comparison of relations between variables and/or different data sets at multiple time scales (e.g. correlation of precipitation and temperature in control and scenario simulation) and (4) transformation of time series at custom time scales.
A simple system for saving and loading objects in
    R. Long running computations can be stashed after the first run and
    then reloaded the next time. Dependencies can be added to ensure that
    a computation is re-run if any of its dependencies or inputs have
    changed.
Designed to ease the application and comparison of multiple
    hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are 
    standardized and usable by the accompanying 'mutossGUI'.
The mutossGUI package provides a graphical user interface for the MuToss Project.
Cancer cells accumulate DNA mutations as result of DNA damage and DNA repair processes. This computational framework is aimed at deciphering DNA mutational signatures operating in cancer. The framework includes modules that support raw data import and processing, mutational signature extraction, and results interpretation and visualization. The framework accepts widely used file formats storing information about DNA variants, such as Variant Call Format files. The framework performs Non-Negative Matrix Factorization to extract mutational signatures explaining the observed set of DNA mutations. Bootstrapping is performed as part of the analysis. The framework supports parallelization and is optimized for use on multi-core systems. The software was described by Fantini D et al (2020) <doi:10.1038/s41598-020-75062-0> and is based on a custom R-based implementation of the original MATLAB WTSI framework by Alexandrov LB et al (2013) <doi:10.1016/j.celrep.2012.12.008>.
The Mutual Information Index (M) introduced to social science literature by
    Theil and Finizza (1971) <doi:10.1080/0022250X.1971.9989795> is a multigroup
    segregation measure that is highly decomposable and that according to Frankel
    and Volij (2011) <doi:10.1016/j.jet.2010.10.008> and Mora and Ruiz-Castillo
    (2011) <doi:10.1111/j.1467-9531.2011.01237.x> satisfies the Strong Unit
    Decomposability and Strong Group Decomposability properties. This package allows
    computing and decomposing the total index value into its "between" and
    "within" terms. These last terms can also be decomposed into their
    contributions, either by group or unit characteristics. The factors that produce
    each "within" term can also be displayed at the user's request. The results can
    be computed considering a variable or sets of variables that define separate
    clusters.
An ensemble classifier for multiclass classification. This is a novel classifier that natively works as an ensemble. It projects data on a large number of matrices, and uses very simple classifiers on each of these projections. The results are then combined, ideally via Dempster-Shafer Calculus.
Functions, data sets, analyses and examples from the book 
  `An Introduction to Applied Multivariate Analysis with R' 
  (Brian S. Everitt and Torsten Hothorn, Springer, 2011).
A set of tools for displaying, modeling and analysing
        multivariate abundance data in community ecology. See
        'mvabund-package.Rd' for details of overall package organization.
        The package is implemented with the Gnu Scientific Library
        (<http://www.gnu.org/software/gsl/>) and 'Rcpp'
        (<http://dirk.eddelbuettel.com/code/rcpp.html>) 'R' / 'C++' classes.
Package for multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis (QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, biplot, scatter plot, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.
Pacote para analise multivariada, tendo funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA), analise de cluster hierarquico e nao hierarquico, regressao linear simples e multipla, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos, biplot, scatter plot, projection pursuit (PP), grant tour e outras funcoes uteis para a analise multivariada.
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.
Hierarchical workspace tree, code editing and backup, easy package prep, editing of packages while loaded, per-object lazy-loading, easy documentation, macro functions, and miscellaneous utilities. Needed by debug package.
Permutation tests for variance components for 2-level, 3-level and 4-level data with univariate or multivariate responses.
An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users).   Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.
Extends 'multiverse' package
    (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021)
    <doi:10.31219/osf.io/yfbwm>, which allows users perform to create explorable
    multiverse analysis in R. This extension provides an additional level of
    abstraction to the 'multiverse' package with the aim of creating
    user friendly syntax to researchers, educators, and students in statistics.
    The 'mverse' syntax is designed to allow piping and takes hints from
    the 'tidyverse' grammar. The package allows users to define and inspect
    multiverse analysis using familiar syntax in R.
Generates multivariate subgaussian stable probabilities using the QRSVN
    algorithm as detailed in Genz and Bretz (2002) <DOI:10.1198/106186002394>
    but by sampling positive stable variates not chi/sqrt(nu).
Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.
Methods for estimating and utilizing the multivariate generalized
    propensity score (mvGPS) for multiple continuous exposures described in
    Williams, J.R, and Crespi, C.M. (2020) <arxiv:2008.13767>. The methods allow
    estimation of a dose-response surface relating the joint distribution of multiple
    continuous exposure variables to an outcome. Weights are constructed assuming a
    multivariate normal density for the marginal and conditional distribution of
    exposures given a set of confounders. Confounders can be different for different
    exposure variables. The weights are designed to achieve balance across all
    exposure dimensions and can be used to estimate dose-response surfaces.
Computes regression deletion diagnostics for multivariate linear models and provides some associated
	diagnostic plots.  The diagnostic measures include hat-values (leverages), generalized Cook's distance, and
	generalized squared 'studentized' residuals.  Several types of plots to detect influential observations are
	provided.
Pearson and Spearman correlation coefficients are commonly used to quantify the strength
	of bivariate associations of genomic variables.  For example, correlations of gene-level 
	DNA copy number and gene expression measurements may be used to assess the impact of 
	DNA copy number changes on gene expression in tumor tissue.  'MVisAGe' enables users to 
	quickly compute and visualize the correlations in order to assess the effect of regional 
	genomic events such as changes in DNA copy number or DNA methylation level.  Please see
	Walter V, Du Y, Danilova L, Hayward MC, Hayes DN, 2018. Cancer Research 
	<doi:10.1158/0008-5472.CAN-17-3464>.
Allows a user to conduct multivariate multiple regression using analytic p-values rather than classic approximate F-tests.
Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) <doi:10.1109/TSP.2014.2343937> for details.
Implementation of imputation techniques based on locally stationary wavelet time series forecasting methods from Wilson, R. E. et al. (2021) <doi:10.1007/s11222-021-09998-2>.
Define, manipulate and plot meshes on simplices, spheres, balls, rectangles and tubes.
 Directional and other multivariate histograms are provided.
Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.
Offers a general framework of multivariate mixed-effects
        models for the joint analysis of multiple correlated outcomes with clustered 
        data structures and potential missingness proposed by Wang et al. (2018) <doi:10.1093/biostatistics/kxy022>. The missingness of outcome values may 
        depend on the values themselves (missing not at random and non-ignorable), 
        or may depend on only the covariates (missing at random and ignorable), or both.
        This package provides functions for two models: 1) mvMISE_b() 
        allows correlated outcome-specific random intercepts with a factor-analytic 
        structure, and 2) mvMISE_e() allows the correlated outcome-specific 
        error terms with a graphical lasso penalty on the error precision matrix. Both functions 
        are motivated by the multivariate data analysis on data with clustered structures 
        from labelling-based quantitative proteomic studies. These models and functions 
        can also be applied to univariate and multivariate analyses of clustered data 
        with balanced or unbalanced design and no missingness.
Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to
    data generated from a continuous-time multivariate industrial or natural
    process. Employ PCA-based dimension reduction to extract linear combinations
    of relevant features, reducing computational burdens. For a description of 
    ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al.
    The multi-state application of ADPCA is from a manuscript under current
    revision entitled "Multi-State Multivariate Statistical Process Control" by
    Odom, Newhart, Cath, and Hering, and is  expected to appear in Q1 of 2018.
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood.  See
    Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.
Performs multivariate normality tests and graphical approaches and
    implements multivariate outlier detection and univariate normality of marginal
    distributions through plots and tests, and performs multivariate Box-Cox transformation 
    (Korkmaz et al, (2014), <https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>).
Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models <ISBN:978-0-387-68560-1>.
Tools of Bayesian analysis framework using the method
  suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for
  multivariate normal (MVN) distribution and multivariate normal
  mixture (MixMVN) distribution:
  a) calculating Bayesian posteriori of (Mix)MVN distribution;
  b) generating random vectors of (Mix)MVN distribution;
  c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.
Provides computationally efficient tools related to the
    multivariate normal and Student's t distributions. The main functionalities
    are: simulating multivariate random vectors, evaluating multivariate normal or
    Student's t densities and Mahalanobis distances. These tools are very efficient
    thanks to the use of C++ code and of the OpenMP API.
Package for  moving grid adjustment 
	      in plant breeding field trials.
Implementing a multiple imputation algorithm for multivariate data with missing and censored values under a coarsening at random assumption (Heitjan and Rubin, 1991<doi:10.1214/aos/1176348396>). The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987)<doi:10.1080/01621459.1987.10478458>. The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data.
A simple informative powerful test (mvnTest()) for multivariate normality proposed by 
    Zhou and Shao (2014) <doi:10.1080/02664763.2013.839637>, which combines kurtosis 
    with Shapiro-Wilk test that is easy for biomedical researchers to understand and 
    easy to implement in all dimensions. This package also contains some other multivariate 
    normality tests including Fattorini's FA test (faTest()), Mardia's skewness and kurtosis 
    test (mardia()), Henze-Zirkler's test (mhz()), Bowman and Shenton's test (msk()), 
    Royston’s H test (msw()), and Villasenor-Alva and Gonzalez-Estrada's test (msw()). Empirical 
    power calculation functions for these tests are also provided. In addition, this package 
    includes some functions to generate several types of multivariate distributions mentioned in 
    Zhou and Shao (2014).
Generalization of shapiro-wilk test for multivariate
        variables.
Given a vector of multivariate normal data, a matrix of
    covariates and the data covariance matrix, generate new multivariate normal
    samples that have the same covariance matrix based on permutations of
    the transformed data residuals.
Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test,  Royston test and Henze-Zirkler test.
Provides a test of multivariate normality of an unknown sample 
    that does not require estimation of the nuisance parameters, the mean and covariance 
    matrix.  Rather, a sequence of transformations removes these nuisance parameters and
    results in a set of sample matrices that are positive definite.  These matrices are 
    uniformly distributed on the space of positive definite matrices in the unit 
    hyper-rectangle if and only if the original data is multivariate normal (Fairweather,
    1973, Doctoral dissertation, University of Washington). The package performs a 
    goodness of fit test of this hypothesis. In addition to the test, functions in the 
    package give visualizations of the support region of positive definite matrices for 
    bivariate samples.
A flexible framework for fitting multivariate
    ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.
Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files.
Fast manipulation of symbolic multivariate polynomials
  using the 'Map' class of the Standard Template Library.  The package
  uses print and coercion methods from the 'mpoly' package (Kahle 2013,
  "Multivariate polynomials in R".  The R Journal, 5(1):162), but offers
  speed improvements.  It is comparable in speed to the 'spray' package
  for sparse arrays, but retains the symbolic benefits of 'mpoly'.
Estimates multivariate subgaussian stable densities 
    and probabilities as well as generates random variates using product 
    distribution theory.  A function for estimating the parameters from 
    data to fit a distribution to data is also provided, using the 
    method from Nolan (2013) <DOI:10.1007/s00180-013-0396-7>.
Tools for high-dimensional peaks-over-threshold inference and simulation
  of spatial extremal processes. Key references include de Fondeville and Davison (2018) <doi:10.1093/biomet/asy026>, Thibaud and Opitz (2015) <doi:10.1093/biomet/asv045>, Wadsworth and Tawn <doi:10.1093/biomet/ast042>.
Tools for estimating multivariate probit models,
   calculating conditional and unconditional expectations,
   and calculating marginal effects on conditional and unconditional
   expectations.
Provides methods to construct multivariate grids, which can be used
    for multivariate quadrature. This grids can be based on different quadrature
    rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss
    quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the
    multidimensional grid the product-rule or the combination- technique can be
    applied.
Functions used for graphing in multivariate contexts. These 
    functions are designed to support produce reasonable graphs with minimal 
    input of graphing parameters. The motivation for these functions was to 
    support students learning multivariate concepts and R - there may be 
    other functions and packages better-suited to practical data analysis. For
    details about the ellipse methods see Johnson and Wichern (2007, 
    ISBN:9780131877153).
This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include:
            (i) Normalization and/or variance stabilization of the data,
            (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
            (iii) Generation of diverse diagnostic plots,
            (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.
Compute the coefficient of determination for outcomes in n-dimensions. 
  May be useful for multidimensional predictions (such as a multinomial model) or
  calculating goodness of fit from latent variable models such as probabilistic
  topic models like latent Dirichlet allocation or deterministic topic models 
  like latent semantic analysis. Based on Jones (2019) <arXiv:1911.11061>.
This package implements the generalization of the Shapiro-Wilk test
        for multivariate normality proposed by Villasenor-Alva and
        Gonzalez-Estrada (2009).
Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history. See K. Bartoszek, J, Pienaar, P. Mostad, S. Andersson, T. F. Hansen (2012) <doi:10.1016/j.jtbi.2012.08.005>. The suggested PCMBaseCpp package (which significantly speeds up the likelihood calculations) can be obtained from <https://github.com/venelin/PCMBaseCpp/>.
Multivariate hypothesis tests and the confidence intervals. It can be used to test the hypothesizes about mean vector or vectors (one-sample, two independent samples, paired samples), covariance matrix (one or more matrices), and the correlation matrix. Moreover, it can be used for robust Hotelling T^2 test at one sample case in high dimensional data. For this package, we have benefited from the studies Rencher (2003), Nel and Merwe (1986) <DOI: 10.1080/03610928608829342>, Tatlidil (1996), Tsagris (2014), Villasenor Alva and Estrada (2009) <DOI: 10.1080/03610920802474465>.
Functions to run fixed effects or random effects multivariate meta-analysis.
Computes multivariate normal and t probabilities, quantiles,
  random deviates and densities.
A function for plotting multivariate time series data.
Implementation of Matched Wake Analysis (mwa) for studying causal relationships in spatiotemporal event data, introduced by Schutte and Donnay (2014) <doi:10.1016/j.polgeo.2014.03.001>.
Algorithms for solving various Maximum
    Weight Connected Subgraph Problems, including variants with
    budget constraints, cardinality constraints, weighted edges and signals.
    The package represents an R interface to high-efficient solvers based on
    relax-and-cut approach (Álvarez-Miranda E., Sinnl M. (2017) <doi:10.1016/j.cor.2017.05.015>)
    mixed-integer programming (Loboda A., Artyomov M., and Sergushichev A. (2016) <doi:10.1007/978-3-319-43681-4_17>)
    and simulated annealing.
The Moving-Window Lasso (MWLasso) method for genome-wide association studies. A window scans the design matrix. For predictors in the same window, their coefficients estimates are smoothed.
A two stage moving-window Ridge method for coefficients estimation and model prediction. In the first stage, moving-window penalty and L1 penalty are applied. In the second stage, ridge regression is applied.
Implements random  number generation, plotting, and estimation algorithms for the two-parameter one-sided and two-sided M-Wright (Mainardi-Wright) family.
             The M-Wright  distributions naturally generalize the widely used one-sided (Airy and half-normal or half-Gaussian) and symmetric (Airy and Gaussian or normal) models. 
             These are widely studied in time-fractional differential equations. References: Cahoy and Minkabo (2017) <doi:10.3233/MAS-170388>; Cahoy (2012) <doi:10.1007/s00180-011-0269-x>; Cahoy (2012) <doi:10.1080/03610926.2010.543299>; Cahoy (2011); Mainardi, Mura, and Pagnini (2010) <doi:10.1155/2010/104505>.
A simple function, mwsApp(), that runs a 'shiny' app spanning multiple,
  connected windows. This uses all standard 'shiny' conventions, and depends only on
  the 'shiny' package.
For single tensor data, any matrix factorization method can be specified the matricised tensor in each dimension by Multi-way Component Analysis (MWCA). An originally extended MWCA is also implemented to specify and decompose multiple matrices and tensors simultaneously (CoupledMWCA). See the reference section of GitHub README.md <https://github.com/rikenbit/mwTensor>, for details of the methods.
Generates efficient balanced mixed-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has EfNOD efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient mixed-level k-circulant design through a progress bar. The progress of 100 per cent means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs. For more details, please see Mandal, B.N., Gupta V. K. and Parsad, R. (2011). Construction of Efficient Mixed-Level k-Circulant Supersaturated Designs, Journal of Statistical Theory and Practice, 5:4, 627-648, <doi:10.1080/15598608.2011.10483735>.
Many feature selection methods for a wide range of response variables, including minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin. References: a) Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets. Journal of Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>. b) Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). Feature selection for high-dimensional temporal data. BMC Bioinformatics, 19:17. <doi:10.1186/s12859-018-2023-7>. c) Tsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I. (2018). Constraint-based causal discovery with mixed data. International Journal of Data Science and Analytics, 6(1): 19-30. <doi:10.1007/s41060-018-0097-y>. d) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018). Efficient feature selection on gene expression data: Which algorithm to use? BioRxiv. <doi:10.1101/431734>. e) Tsagris, M. (2019). Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation. Applied Artificial Intelligence, 33(2):101-123. <doi:10.1080/08839514.2018.1526760>. f) Tsagris, M. and Tsamardinos, I. (2019). Feature selection with the R package MXM. F1000Research 7: 1505. <doi:10.12688/f1000research.16216.2>. g) Borboudakis, G. and Tsamardinos, I. (2019). Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research 20: 1-39. h) The gamma-OMP algorithm for feature selection with application to gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214-1224. <doi:10.1109/TCBB.2020.3029952>.
Provides a convenient interface in 'OpenMx' for building
    Estabrook's (2015) <doi:10.1037/a0034523> Measurement Model of
    Derivatives (MMOD).
Implements methods to normalize multiplexed imaging data, including
    statistical metrics and visualizations to quantify technical variation in 
    this data type. Reference for methods listed here: Harris, C., Wrobel, J., 
    & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging 
    Data. Journal of Open Source Software, 7(71), 4180, 
    <doi:10.21105/joss.04180>.
The stepwise variable selection procedure (with iterations
 between the 'forward' and 'backward' steps) can be used to obtain
 the best candidate final regression model in regression analysis.
 All the relevant covariates are put on the 'variable list' to be
 selected. The significance levels for entry (SLE) and for stay
 (SLS) are usually set to 0.15 (or larger) for being conservative.
 Then, with the aid of substantive knowledge, the best candidate
 final regression model is identified manually by dropping the
 covariates with p value > 0.05 one at a time until all regression
 coefficients are significantly different from 0 at the chosen alpha
 level of 0.05.
The chi-squared test for goodness of fit 
    and independence test.
The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.
Perform correlation and linear regression test
    among the numeric fields in a data.frame automatically
    and make plots using pairs or lattice::parallelplot.
Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes (Drost et al. (2017) <doi:10.1093/bioinformatics/btx835>). The aim of this tool is to provide a transcriptome analysis environment to quantify the average evolutionary age of genes contributing to a transcriptome of interest (Drost et al. (2016) <doi:10.1101/051565>).
Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS.  The n1qn1 routine is useful since it allows prespecification of a Hessian.
       If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the
       'Scilab' optimization documentation located at 
       <https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from 'f2c' to make this a C only binary.
Provides some functions to get Korean text sample from news articles in
             Naver which is popular news portal service <https://news.naver.com/> in Korea.
Implements methods to perform fast approximate K-nearest neighbor search on input matrix. Algorithm based on the 'N2' implementation of an approximate nearest neighbor search using hierarchical  Navigable Small World (NSW) graphs. The original algorithm is described in "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs", Y. Malkov and D. Yashunin, <doi:10.1109/TPAMI.2018.2889473>, <arXiv:1603.09320>.
This comprehensive toolkit provide a consistent and 
    extensible framework for working with missing values in vectors. The 
    companion package 'tidyimpute' provides similar functionality for list-like 
    and table-like structures).
    Functions exist for detection, removal, replacement, imputation, 
    recollection, etc. of 'NAs'.
Functions for reading cancer record files which follow a format
    defined by the North American Association of Central Cancer Registries
    (NAACCR).
An R wrapper for 'libnabo', an exact or approximate k nearest
    neighbour library which is optimised for low dimensional spaces (e.g. 3D).
    'libnabo' has speed and space advantages over the 'ANN' library wrapped by
    package 'RANN'. 'nabor' includes a knn function that is designed as a 
    drop-in replacement for 'RANN' function nn2. In addition, objects which 
    include the k-d tree search structure can be returned to speed up repeated 
    queries of the same set of target points.
NanoString nCounter data are gene expression assays
    where there is no need for the use of enzymes or amplification
    protocols and work with fluorescent barcodes (Geiss et al. (2018)
    <doi:10.1038/nbt1385>). Each barcode is assigned a
    messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its
    target can be counted. As a result each count of a specific barcode
    represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString
    quality Control dasHbOard) is able to analyse the exported NanoString
    nCounter data and facilitates the user in performing a quality
    control. 'NACHO' does this by visualising quality control metrics,
    expression of control genes, principal components and sample specific
    size factors in an interactive web application.
Contains methods described by Dennis Helsel in 
             his book "Nondetects And Data Analysis: Statistics 
             for Censored Environmental Data".
Contains methods described by Dennis Helsel in 
             his book "Statistics for Censored Environmental Data
             using Minitab and R" (2011) and courses and videos at 
             <https://practicalstats.com>. This package adds new functions to 
             the `NADA` Package.
Creates a uniform interface for several advanced imputations missing data methods. Every available method can be used as a part of 'mlr3' pipelines which allows easy tuning and performance evaluation. Most of the used functions work separately on the training and test sets (imputation is trained on the training set and impute training data. After that imputation is again trained on the test set and impute test data).
Constructs (non)additive genetic relationship matrices, and their
    inverses, from a pedigree to be used in linear mixed effect models (A.K.A.
    the 'animal model'). Also includes other functions to facilitate the use of
    animal models. Some functions have been created to be used in conjunction
    with the R package 'asreml' for the 'ASReml' software, which can be
    obtained upon purchase from 'VSN' international 
    (<https://www.vsni.co.uk/software/asreml>).
This data package contains the Item Response Theory (IRT) parameters for the National Center for Education Statistics (NCES) items used on the National Assessment of Education Progress (NAEP) from 1990 to 2015. The values in these tables are used along with NAEP data to turn student item responses into scores and include information about item difficulty, discrimination, and guessing parameter for 3 parameter logit (3PL) items. Parameters for Generalized Partial Credit Model (GPCM) items are also included. The adjustments table contains the information regarding the treatment of items (e.g., deletion of an item or a collapsing of response categories), when these items did not appear to fit the item response models used to describe the NAEP data. Transformation constants change the score estimates that are obtained from the IRT scaling program to the NAEP reporting metric. Values from the years 2000 - 2013 were taken from the NCES website <https://nces.ed.gov/nationsreportcard/> and values from 1990 - 1998 and 2015 were extracted from their NAEP data files. All subtest names were reduced and homogenized to one word (e.g. "Reading to gain information" became "information"). The various subtest names for univariate transformation constants were all homogenized to "univariate".
Contains a sample of the 2005 Grade 8 Mathematics data from the National Assessment of Educational Progress (NAEP). This data set is called the NAEP Primer.
For use in summary functions to omit missing values 
    conditionally using specified checks.
A set of convenience functions as well as geographical/political
  data about Nigeria, aimed at simplifying work with data and information that
  are specific to the country.
An application for the empirical extrapolation of time features selecting and summarizing the most relevant patterns in time sequences.
In this implementation of the Naive Bayes classifier following class conditional distributions are available: Bernoulli, Categorical, Gaussian, Poisson and non-parametric representation of the class conditional density estimated via Kernel Density Estimation. Implemented classifiers handle missing data and can take advantage of sparse data.
In empirical studies, instrumental variable (IV) regression is the signature method to solve the endogeneity problem. If we enforce the exogeneity condition of the IV, it is likely that we end up with a large set of IVs without knowing which ones are good. Also, one could face the model uncertainty for structural equation, as large micro dataset is commonly available nowadays. This package uses adaptive group lasso and B-spline methods to select the nonparametric components of the IV function, with the linear function being a special case (naivereg). The package also incorporates two stage least squares estimator (2SLS), generalized method of moment (GMM), generalized empirical likelihood (GEL) methods post instrument selection, logistic-regression instrumental variables estimator (LIVE, for dummy endogenous variable problem), double-selection plus instrumental variable estimator (DS-IV) and double selection plus logistic regression instrumental variable estimator (DS-LIVE), where the double selection methods are useful for high-dimensional structural equation models. The naivereg is nonparametric version of 'ivregress' in 'Stata' with IV selection and high dimensional features. The package is based on the paper by Q. Fan and W. Zhong, "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective" (2018), Journal of Business & Economic Statistics <doi:10.1080/07350015.2016.1180991> as well as a series of working papers led by the same authors.
Density, distribution function, quantile function and random 
    generation for the Nakagami distribution of Nakagami (1960) 
    <doi:10.1016/B978-0-08-009306-2.50005-4>.
Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.
A system for organizing column names in data. Aimed at supporting a 
  prefix-based and suffix-based column naming scheme. Extends 'dplyr' functionality
  to add ordering by function and more explicit renaming.
User-friendly wrappers for 
 named capture regular expressions.
 Introduction and comparison in research paper
 by Hocking (2019), R Journal.
 <doi:10.32614/RJ-2019-050>
 RE2 engine ('re2r' package)
 <https://github.com/qinwf/re2r>
 was removed from CRAN in Mar 2020
 so must be installed from github.
Provides 'visual citations' containing the metadata of a
    scientific paper and a 'QR' code.  A 'visual citation' is a banner
    containing title, authors, journal and year of a publication.  This
    package can create such banners based on 'BibTeX' and 'BibLaTeX'
    references or call the reference metadata from 'Crossref'-API. The
    banners include a QR code pointing to the 'DOI'.  The resulting HTML
    object or PNG image can be included in a presentation to point the
    audience to good resources for further reading.  Styling is possible
    via predefined designs or via custom 'CSS'.  This package is not
    intended as replacement for proper reference manager packages, but a
    tool to enrich scientific presentation slides and conference posters.
The Needleman-Wunsch global alignment algorithm can be
  used to find approximate matches between sample names in different
  data sets. See Wang et al. (2010) <doi:10.4137/CIN.S5613>.
It names the 'R Markdown' chunks of files based on
    the filename.
This package provides user-level functions to manage
        namespaces not (yet) available in base R: 'registerNamespace',
        'unregisterNamespace', 'makeNamespace', and
        'getRegisteredNamespace'

  ('makeNamespaces' is extracted from the R 'base' package source code:
        src/library/base/R/namespace.R)
Wraps the 'nametag' library <https://github.com/ufal/nametag>, allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models.
    Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) <https://ufal.mff.cuni.cz/~straka/papers/2013-tsd_ner.pdf>.
Calculation of molecular number and brightness from
    fluorescence microscopy image series. The software was published in a
    2016 paper <doi:10.1093/bioinformatics/btx434>. The seminal paper for
    the technique is Digman et al. 2008 <doi:10.1529/biophysj.107.114645>.
    A review of the technique was published in 2017
    <doi:10.1016/j.ymeth.2017.12.001>.
Missing values are ubiquitous in data and need to be explored and
    handled in the initial stages of analysis. 'naniar' provides data structures 
    and functions that facilitate the plotting of missing values and examination 
    of imputations. This allows missing data dependencies to be explored with 
    minimal deviation from the common work patterns of 'ggplot2' and tidy data. 
    The work is fully discussed at Tierney & Cook (2018) <arXiv:1809.02264>.
R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ. NNG is 
    a socket library providing high-performance scalability protocols,
    implementing a cross-platform standard for messaging and communications.
    Serves as a concurrency framework for building distributed applications,
    utilising 'Aio' objects which automatically resolve upon completion of
    asynchronous operations.
This software package implements functions to simulate spherical, ellipsoid and cubic polyatomic nanoparticles with arbitrary crystal structures and to calculate the associated pair-distribution function and X-ray/neutron total-scattering signals. It also provides a target function that can be used for simultaneous fitting of small- and wide-angle total scattering data in real and reciprocal spaces. The target function can be generated either as a sum of weighted residuals for individual datasets or as a vector of residuals suitable for optimization using multi-criteria algorithms (e.g. Pareto methods).
Provides quality control (QC), normalization, and
    batch effect correction operations for 'NanoString nCounter' data,
    Talhouk et al. (2016) <doi:10.1371/journal.pone.0153844>.  Various
    metrics are used to determine which samples passed or failed QC.  Gene
    expression should first be normalized to housekeeping genes, before a
    reference-based approach is used to adjust for batch effects.  Raw
    NanoString data can be imported in the form of Reporter Code Count
    (RCC) files.
Full 64-bit resolution date and time functionality with
 nanosecond granularity is provided, with easy transition to and from
 the standard 'POSIXct' type. Three additional classes offer interval,
 period and duration functionality for nanosecond-resolution timestamps.
Conducts Bayesian Hypothesis tests of a point null hypothesis against a two-sided alternative
             using Non-local Alternative Prior (NAP) for one- and two-sample z- and t-tests 
             (Pramanik and Johnson, 2022). Under the alternative, the NAP is assumed on the standardized 
             effects size in one-sample tests and on their differences in two-sample tests. The package 
             considers two types of NAP densities: (1) the normal moment prior, and (2) the composite alternative. 
             In fixed design tests, the functions calculate the Bayes factors and the expected weight of evidence
             for varied effect size and sample size. The package also provides a sequential testing framework using the
             Sequential Bayes Factor (SBF) design. The functions calculate the operating characteristics (OC)
             and the average sample number (ASN), and also conducts sequential tests for a sequentially observed data.
An R wrapper for pulling data from the National Public Transport Access Nodes ('NaPTAN') API (<https://www.api.gov.uk/dft/national-public-transport-access-nodes-naptan-api/#national-public-transport-access-nodes-naptan-api>). This allows users to download 'NaPTAN' transport information, for the full dataset, by ATCO region code, or by name of region.
Provides a near drop-in replacement for base::Sys.sleep() that allows more types of input
    to produce delays in the execution of code and can silence/prevent typical sources of error.
Computes the nonlinear cointegrating autoregressive distributed lag model with automatic bases aic and bic lags selection of independent variables proposed by (Shin, Yu & Greenwood-Nimmo, 2014 <doi:10.1007/978-1-4899-8008-3_9>).
Stacking arrays according to dimension names, subset-aware
    splitting and mapping of functions, intersecting along arbitrary
    dimensions, converting to and from data.frames, and many other helper
    functions.
Provides functions to access NASA's Earth Imagery and Assets API
    and the Earth Observatory Natural Event Tracker (EONET) webservice.
Client for 'NASA' 'POWER' global meteorology, surface solar
    energy and climatology data 'API'.  'POWER' (Prediction Of Worldwide Energy 
    Resource) data are freely available for download with varying spatial
    resolutions dependent on the original data and with several temporal
    resolutions depending on the POWER parameter and community.  This work is
    funded through the 'NASA' Earth Science Directorate Applied Science Program.
    For more on the data themselves, the methodologies used in creating, a web-
    based data viewer and web access, please see <https://power.larc.nasa.gov/>.
This package contains tidied data from the ASA 2006 data expo,
    as well as a number of useful other related data sets.
Functions for interacting directly with the Nasdaq Data Link API to offer data in a number of formats usable in R, downloading a zip with all data from a Nasdaq Data Link database, and the ability to search. This R package uses the Nasdaq Data Link API. For more information go to <https://docs.data.nasdaq.com/>. For more help on the package itself go to <https://data.nasdaq.com/tools/r>.
Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) <ISBN:9780124167438> with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm.
NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D
    biological image data, especially traced neurons. Reads and writes 3D images
    in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf
    format. Traced neurons can be imported from and written to SWC and 'Amira'
    LineSet and SkeletonGraph formats. These data can then be visualised in 3D
    via 'rgl', manipulated including applying calculated registrations, e.g.
    using the 'CMTK' registration suite, and analysed. There is also a simple
    representation for neurons that have been subjected to 3D skeletonisation
    but not formally traced; this allows morphological comparison between
    neurons including searches and clustering (via the 'nat.nblast' extension
    package).
Extends package 'nat' (NeuroAnatomy Toolbox) by providing a
    collection of NBLAST-related functions for neuronal morphology comparison (Costa et al. (2016) <doi: 10.1016/j.neuron.2016.06.012>).
Extends package 'nat' (NeuroAnatomy Toolbox) by
    providing objects and functions for handling template brains.
Utility functions that may be of general interest but are 
    specifically required by the 'NeuroAnatomy Toolbox' ('nat'). Includes
    functions to provide a basic make style system to update files based on
    timestamp information, file locking and 'touch' utility. Convenience 
    functions for working with file paths include 'abs2rel', 'split_path' 
    and 'common_path'. Finally there are utility functions for working with 
    'zip' and 'gzip' files including integrity tests.
Fast functions implemented in C++ via 'Rcpp' to support the
    'NeuroAnatomy Toolbox' ('nat') ecosystem. These functions provide large
    speed-ups for basic manipulation of neuronal skeletons over pure R
    functions found in the 'nat' package. The expectation is that end
    users will not use this package directly, but instead the 'nat'
    package will automatically use routines from this package when it is
    available to enable large performance gains.
Provides streamlined installation for packages from the 'natverse',
    a suite of R packages for computational neuroanatomy built on top of the
    'nat' 'NeuroAnatomy Toolbox' package. Installation of the complete
    'natverse' suite requires a 'GitHub' user account and personal access token
    'GITHUB_PAT'. 'natmanager' will help the end user set this up if necessary.
Color palettes for data visualization inspired by National Parks. Currently contains 15 color schemes and checks for colorblind-friendliness of palettes.
Interface to 'NatureServe' (<https://www.natureserve.org/>).
    Includes methods to get data, image metadata, search taxonomic names,
    and make maps.
Natural strata can be used in observational studies to balance
    the distributions of many covariates across any number of treatment
    groups and any number of comparisons. These strata have proportional 
    amounts of units within each stratum across the treatments, allowing 
    for simple interpretation and aggregation across strata. Within each 
    stratum, the units are chosen using randomized rounding of a linear 
    program that balances many covariates.
    To solve the linear program, the 'Gurobi' commercial optimization software 
    is recommended, but not required. The 'gurobi' R package can be installed following the instructions 
    at <https://www.gurobi.com/documentation/9.1/refman/ins_the_r_package.html>.
Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) <arXiv:1712.02412>.
Classify occurrence records based on confidence
    levels of species identification. In addition, implement tools to filter
    occurrences inside grid cells and to manually check for possibles errors with 
	an interactive shiny application.
Provides functions related to human natural ordering.
    It handles adjacent digits in a character sequence as a number so that
    natural sort function arranges a character vector by their numbers, not digit
    characters. It is typically seen when operating systems lists file names. For
    example, a sequence a-1.png, a-2.png, a-10.png looks naturally ordered because 1
    < 2 < 10 and natural sort algorithm arranges so whereas general sort algorithms
    arrange it into a-1.png, a-10.png, a-2.png owing to their third and fourth
    characters.
Collection of example animal sounds for bioacoustic analysis.
Provides a navigation menu to enable pipe-friendly data processing for hierarchical data structures.
    By activating the menu items, you can perform operations on each item while maintaining the overall structure in attributes.
Implements the navigated weighting (NAWT) proposed by Katsumata (2020)
    <arXiv:2005.10998>, which improves the inverse probability weighting by 
    utilizing estimating equations suitable for a specific pre-specified parameter 
    of interest (e.g., the average treatment effects or the average treatment 
    effects on the treated) in propensity score estimation. It includes the 
    covariate balancing propensity score proposed by Imai and Ratkovic (2014) 
    <doi:10.1111/rssb.12027>, which uses covariate balancing conditions in 
    propensity score estimation. The point estimate of the parameter of interest
    as well as coefficients for propensity score estimation and their 
    uncertainty are produced using the M-estimation. The same functions can be 
    used to estimate average outcomes in missing outcome cases.
Model-based clustering of high-dimensional non-negative
             data that follow Generalized Negative Binomial distribution. All functions 
             in this package applies to either continuous or integer data. Correlation
            between variables are allowed, while samples are assumed to be independent.
Provides interface to the online basketball data resources such as
      Basketball reference API <https://www.basketball-reference.com/> and helps
      R users analyze basketball data.
Palettes generated from NBA jersey colorways.
Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. The treatment allocation in these designs is such that every treatment appears equally often as a neighbour with every other treatment. Neighbour Balanced Designs are employed when there is a possibility of neighbour effects from treatments used in adjacent experimental units. In the literature, a vast number of such designs have been developed. This package generates some efficient neighbour balanced block designs which are balanced and partially variance balanced for estimating the contrast pertaining to direct and neighbour effects, as well as provides a function for analysing the data obtained from such trials (Azais, J.M., Bailey, R.A. and Monod, H. (1993). "A catalogue of efficient neighbour designs with border plots". Biometrics, 49, 1252-1261 ; Tomar, J. S., Jaggi, Seema and Varghese, Cini (2005)<DOI: 10.1080/0266476042000305177>. "On totally balanced block designs for competition effects"). This package contains functions named nbbd1(),nbbd2(),nbbd3(),pnbbd1() and pnbbd2() which generates neighbour balanced block designs within a specified range of number of treatment (v). It contains another function named anlys()for performing the analysis of data generated from such trials.
We constructed 'NBBttest' for identifying genes or RNA isoforms differentially expressed between two conditions on RNA-seq count data. Package 'NBBttest' can  perform data quality check, data normalization, differential analysis, annotation and graphic analysis. In differential analysis, 'NBBttest' can identify differentially expressed genes and differential RNA isoforms in alternative splicing sites and alternative polyadenylation sites, differential sgRNA, and differential CRISPR (clustered regularly interspaced short palindromic repeats) screening genes. In graphic analysis, 'NBBttest' provides two types of heatmaps to visualize differential expression at gene or isoform level using z-score and n-score and creates pathway heatmap. 'NBBttest' can plot differentially expressed exons within a specified gene. In addition, 'NBBttest' provides a tool for annotation of genes and exons. The methods used in 'NBBttest' were new statistical methods developed from Tan and others (2015) <doi:10.1371/journal.pone.0123658>. The statistical methods are robust and very powerful in identifying genes or RNA isoforms differentially expressed between two conditions in small samples.
An implementation of the Naive Bayes Classifier (NBC) algorithm
    used for Verbal Autopsy (VA) built on code from Miasnikof et al (2015) 
    <DOI:10.1186/s12916-015-0521-2>.
It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.
Calls the 'Jupyter' script 'nbconvert' to create vignettes from
  notebooks. Those notebooks ('.ipynb' files) are files containing rich text,
  code, and its output. Code cells can be edited and evaluated interactively.
  See <https://jupyter.org/> for more information.
The Dirichlet (aka NBD-Dirichlet) model describes the purchase incidence and brand choice of consumer products.  We estimate the model and summarize various theoretical quantities of interest to marketing researchers. Also provides functions for making tables that compare observed and theoretical statistics.
Calculate various functions needed for design and monitoring clinical trials with negative binomial endpoint
    with variable follow-up. This version has a few changes compared to the previous version 1.0.0, including 
    (1) correct a typo in Type 1 censoring, mtbnull=bnull and (2) restructure the code to account for shape parameter equal to zero, i.e. Poisson scenario.
Catalogue of NBER working papers published between June 1973 and December 2021.
We developed a negative binomial factor regression model to estimate structured (sparse) associations between a feature matrix X and overdispersed count data Y.  With 'nbfar', microbiome count data Y can be used, for example, to associate host or environmental covariates with microbial abundances. Currently, two models are available: a) Negative Binomial reduced rank regression (NB-RRR), b) Negative Binomial co-sparse factor regression (NB-FAR). Please refer the manuscript 'Mishra, A. K., & Müller, C. L. (2021). Negative Binomial factor regression with application to microbiome data analysis. bioRxiv.' for more details.
We proposed a package for the classification task which uses Negative Binomial distribution within Linear Discriminant Analysis (NBLDA). It is an extension of the 'PoiClaClu' package to Negative Binomial distribution. The classification algorithms are based on the papers Dong et al. (2016, ISSN: 1471-2105) and Witten, DM (2011, ISSN: 1932-6157) for NBLDA and PLDA, respectively. Although PLDA is a sparse algorithm and can be used for variable selection, the algorithm proposed by Dong et al. is not sparse. Therefore, it uses all variables in the classifier. Here, we extend Dong et al.'s algorithm to the sparse case by shrinking overdispersion towards 0 (Yu et al., 2013, ISSN: 1367-4803) and offset parameter towards 1 (as proposed by Witten DM, 2011). We support only the classification task with this version.
Tools to run simple direct gravitational N-body simulations. It can access different external N-body simulators, but also has a simple built-in default simulator. This default simulator uses a variable block time step and lets the user choose between a range of integrators, including 4th and 6th order integrators for high-accuracy simulations. Basic top-hat smoothing is available as an option. The code also allows the definition of background particles that are fixed or in uniform motion, not subject to acceleration by other particles.
Perform non-bipartite matching and matched randomization. A
    "bipartite" matching utilizes two separate groups, e.g. smokers being
    matched to nonsmokers or cases being matched to controls. A "non-bipartite"
    matching creates mates from one big group, e.g. 100 hospitals being
    randomized for a two-arm cluster randomized trial or 5000 children who
    have been exposed to various levels of secondhand smoke and are being
    paired to form a greater exposure vs. lesser exposure comparison. At the
    core of a non-bipartite matching is a N x N distance matrix for N potential
    mates. The distance between two units expresses a measure of similarity or
    quality as mates (the lower the better). The 'gendistance()' and
    'distancematrix()' functions assist in creating this. The 'nonbimatch()'
    function creates the matching that minimizes the total sum of distances
    between mates; hence, it is referred to as an "optimal" matching. The
    'assign.grp()' function aids in performing a matched randomization. Note
    bipartite matching can be performed using the prevent option in
    'gendistance()'.
Negative Binomial (NB) models for two-group comparisons and
    regression inferences from RNA-Sequencing Data.
An implementation of network-based statistics in R using mixed effects models.
    Theoretical background for Network-Based Statistics can be found in Zalesky et al. (2010)
    <doi:10.1016/j.neuroimage.2010.06.041>. For Mixed Effects Models check the
    R package <https://CRAN.R-project.org/package=nlme>.
An interactive document on  the topic of naive Bayes classification  analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/NBShiny/>.
Estimates the relative transmission probabilities between cases in an infectious disease outbreak or cluster using naive Bayes. Included are various functions to use these probabilities to estimate transmission parameters such as the generation/serial interval and reproductive number as well as finding the contribution of covariates to the probabilities and visualizing results. The ideal use is for an infectious disease dataset with metadata on the majority of cases but more informative data such as contact tracing or pathogen whole genome sequencing on only a subset of cases. For a detailed description of the methods see Leavitt et al. (2020) <doi:10.1093/ije/dyaa031>.
User-friendly functions for extracting a data
 table (row for each match, column for each group)
 from non-tabular text data using regular expressions,
 and for melting columns that match a regular expression.
 Patterns are defined using a readable syntax
 that makes it easy to build complex patterns
 in terms of simpler, re-usable sub-patterns.
 Named R arguments are translated to column names
 in the output; capture groups without names are used
 internally in order to provide a standard interface
 to three regular expression C libraries (PCRE, RE2, ICU).
 Output can also include numeric columns via
 user-specified type conversion functions.
 RE2 engine (re2r package) was removed from CRAN in Mar 2020
 so must be installed from github.
Performs a Necessary Condition Analysis (NCA). (Dul, J. 2016. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods 19(1), 10-52,
  <https://journals.sagepub.com/doi/full/10.1177/1094428115584005>).
  NCA identifies necessary (but not sufficient) conditions in datasets, where x causes (e.g. precedes) y. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations.
  (Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g. an effort or input) is necessary but not sufficient for a (desired) level of y (e.g. good performance or output). A quick start guide for using this package can be found here: <https://repub.eur.nl/pub/78323/> or <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2624981>.
A flexible tool that can perform
    (i) traditional non-compartmental analysis (NCA) and
    (ii) Simulation-based posterior predictive checks for population
    pharmacokinetic (PK) and/or pharmacodynamic (PKPD) models using NCA metrics.
Conduct a noncompartmental analysis with industrial strength.
             Some features are
             1) CDISC SDTM terms
             2) Automatic or manual slope selection
             3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
             4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
             5) Produce pdf, rtf, text report files.
             * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
Makes NCBI taxonomic data locally available and
        searchable as an R object.
Provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets.  Using this package, netCDF files (either version 4 or "classic" version 3) can be opened and data sets read in easily.  It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files.  This package replaces the former ncdf package, which only worked with netcdf version 3 files.  For various reasons the names of the functions have had to be changed from the names in the ncdf package.  The old ncdf package is still available at the URL given below, if you need to have backward compatibility.  It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem.  However, the ncdf package does not provide an interface for netcdf version 4 files.
Contains a collection of helper functions for dealing with 
    'NetCDF' files <https://www.unidata.ucar.edu/software/netcdf/> 
    opened using 'ncdf4', particularly 'NetCDF' files that conform to the
    Climate and Forecast (CF) Metadata Conventions 
    <http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html>.
Tools to create time series and geometry 'NetCDF' files.
Tools for handling 'NetCDF' metadata in data frames. The metadata is provided
    as relations in tabular form, to avoid having to scan printed header output or to navigate 
    nested lists of raw metadata.
Computes the noncentral Dunnett's test distribution (pdf, cdf and quantile) and generates random numbers.
Spatial (cross-)covariance and related geostatistical tools: the
        nonparametric (cross-)covariance function , the spline correlogram, the
        nonparametric phase coherence function, local indicators of spatial 
        association (LISA), (Mantel) correlogram, (Partial) Mantel test.
Extract metadata from 'NetCDF' data sources, these can be files, file handles or
 servers. This package leverages and extends the lower level functions of the 'RNetCDF' package 
 providing a consistent set of functions that all return data frames. We introduce named concepts 
 of 'grid', 'axis' and 'source' which are all meaningful entities without formal definition in the 
 'NetCDF' library <https://www.unidata.ucar.edu/software/netcdf/>. 'RNetCDF' matches the library 
 itself with only the named concepts of 'variables', 'dimensions' and 'attributes'.
A set of handy functions. Includes a versatile one line progress bar, one 
 line function timer with detailed output, time delay function, text histogram, object 
 preview, CRAN package search, simpler package installer, Linux command install check, 
 a flexible Mode function, top function, simulation of correlated data, and more.
A set of techniques that can be used to develop, validate, and implement automated classifiers. A powerful tool for transforming raw data into meaningful information, 'ncodeR' (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is designed specifically for working with big data: large document collections, logfiles, and other text data.
Construct and manipulate hierarchical Archimedean copulas with multivariate compound distributions. The model used is the one of Cossette et al. (2017) <doi:10.1016/j.insmatheco.2017.06.001>.
Provides easy-to-use programming API to access real time and historical data of 'COVID'-19 cases, vaccine and therapeutics data, and a Shiny app to help users exploring the data.  Fetching data using API provided by <https://disease.sh> .
An efficient unified nonconvex penalized estimation algorithm for
    Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit,
    and Cox proportional hazard regression models.
    The unified algorithm is implemented based on the convex concave procedure and
    the algorithm can be applied to most of the existing nonconvex penalties.
    The algorithm also supports convex penalty:
    least absolute shrinkage and selection operator (LASSO).
    Supported nonconvex penalties include
    smoothly clipped absolute deviation (SCAD),
    minimax concave penalty (MCP), truncated LASSO penalty (TLP),
    clipped LASSO (CLASSO), sparse ridge (SRIDGE),
    modified bridge (MBRIDGE) and modified log (MLOG).
    For high-dimensional data (data set with many variables),
    the algorithm selects relevant variables producing a parsimonious regression model.
    Kim, D., Lee, S. and Kwon, S. (2018) <arXiv:1811.05061>,
    Lee, S., Kwon, S. and Kim, Y. (2016) <doi:10.1016/j.csda.2015.08.019>,
    Kwon, S., Lee, S. and Kim, Y. (2015) <doi:10.1016/j.csda.2015.07.001>.
    (This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine
    Graziadio Business School and the National Research Foundation of Korea.)
Provides functionality for performing Nearest Centroid (NC) Sampling. The NC sampling procedure was developed for forestry applications and selects plots for ground measurement so as to maximize the efficiency of imputation estimates. It uses multiple auxiliary variables and multivariate clustering to search for an optimal sample. Further details are given in Melville G. & Stone C. (2016) <doi:10.1080/00049158.2016.1218265>.
Inference and dependence measure for the non-central squared Gaussian, Student, Clayton, Gumbel, and Frank copula models.The description of the methodology is taken from Section 3 of Nasri, Remillard and Bouezmarni (2019) <doi:10.1016/j.jmva.2019.03.007>.
Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more.
    'NCutYX' allows clustering of variables, of samples, and both variables and samples (biclustering),
    while incorporating the dependencies across multiple types of Omics data.
    (SJ Teran Hidalgo et al (2017), <doi:10.1186/s12864-017-3990-1>).
Fits regularization paths for linear regression, GLM, and Cox
  regression models using lasso or nonconvex penalties, in particular the
  minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD)
  penalty, with options for additional L2 penalties (the "elastic net" idea).
  Utilities for carrying out cross-validation as well as post-fitting
  visualization, summarization, inference, and prediction are also provided.
  For more information, see Breheny and Huang (2011) <doi:10.1214/10-AOAS388>
  or visit the ncvreg homepage <https://pbreheny.github.io/ncvreg/>.
Non-parametric dimensionality reduction function. Reduction with and without feature selection. Plot functions. Kosztyan et. al. (2022) <doi:10.1016/j.knosys.2022.109180>.
Computes various metrics of socio-economic deprivation and disparity in
             the United States. Some metrics are considered "spatial" because they 
             consider the values of neighboring (i.e., adjacent) census geographies in
             their computation, while other metrics are "aspatial" because they only 
             consider the value within each census geography. Two types of aspatial 
             neighborhood deprivation indices (NDI) are available: including:
             (1) based on Messer et al. (2006) <doi:10.1007/s11524-006-9094-x>
             and (2) based on Andrews et al. (2020) <doi:10.1080/17445647.2020.1750066> 
             and Slotman et al. (2022) <doi:10.1016/j.dib.2022.108002>
             who use variables chosen by Roux and Mair (2010)
             <doi:10.1111/j.1749-6632.2009.05333.x>. Both are a decomposition
             of multiple demographic characteristics from the U.S. Census Bureau
             American Community Survey 5-year estimates (ACS-5; 2010 onward). Using data
             from the ACS-5 (2009 onward), the package can also (1) compute the spatial
             Racial Isolation Index (RI) based on Anthopolos et al. (2011)
             <doi:10.1016/j.sste.2011.06.002>, (2) compute the spatial Educational Isolation
             Index (EI) based on Bravo et al. (2021) <doi:10.3390/ijerph18179384>, 
             (3) compute the aspatial Index of Concentration at the Extremes (ICE) based on 
             Feldman et al. (2015) <doi:10.1136/jech-2015-205728> and Krieger et al. 
             (2016) <doi:10.2105/AJPH.2015.302955>, and (4) retrieve the aspatial Gini 
             Index based on Gini (1921) <doi:10.2307/2223319>.
Streaming 'JSON' ('ndjson') has one 'JSON' record per-line
        and many modern 'ndjson' files contain large numbers of records.
        These constructs may not be columnar in nature, but it is often
        useful to read in these files and "flatten" the structure out to
        enable working with the data in an R 'data.frame'-like context.
        Functions are provided that make it possible to read in plain
        'ndjson' files or compressed ('gz') 'ndjson' files and either
        validate the format of the records or create "flat" 'data.table'
        structures from them.
Naive discriminative learning implements learning and
    classification models based on the Rescorla-Wagner equations and their
    equilibrium equations.
An interactive presentation on  the topic of normal distribution using 'rmarkdown' and 'shiny' packages. It is helpful to those who want to learn normal distribution quickly and get a hands on experience. The presentation has a template for solving problems on normal distribution. Runtime examples are provided in the package function as well as at  <https://kartikeyastat.shinyapps.io/NormalDistribution/>.
Renders dynamic network data from 'networkDynamic' objects as movies, interactive animations, or other representations of changing relational structures and attributes.
Implements a path algorithm for Near-Isotonic
        Regression. For more details see the help files.
Includes functions and examples to compute NEAT, the Network Enrichment Analysis Test described in Signorelli et al. (2016, <DOI:10.1186/s12859-016-1203-6>).
Simplify the exploratory data analysis process for multiple network
             data sets with the help of hierarchical clustering, consensus 
             clustering and heatmaps. Multiple network data consists of multiple
             disjoint networks that have common variables (e.g. ego networks). 
             This package contains the necessary tools for exploring such data,
             from the data pre-processing stage to the creation of dynamic
             visualizations.
Collapse, partition, combine, fill gaps in and expand date/time ranges.
User-friendly, clear and simple statistics, primarily for
  publication in psychological science. The main functions are wrappers for
  other packages, but there are various additions as well. Every relevant step
  from data aggregation to reportable printed statistics is covered for basic
  experimental designs.
A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.
Tools to generate Necklaces, Bracelets, Lyndon words and de Bruijn sequences. The generation relies on integer partitions and uses the 'KStatistics' package. Methods used in the package refers to E. Di Nardo and G. Guarino (2022) <arXiv:2208.06855>.
Showcasing needmining (the semi-automatic extraction of customer needs from
	social media data) with Twitter data. It uses the handling of the Twitter API provided by
	the package 'rtweet' and the textmining algorithms provided by the package 'tm'.
	Niklas Kuehl (2016) <doi:10.1007/978-3-319-32689-4_14> wrote an introduction to the topic of needmining.
A simple function for easier package loading and auto-installation.
needy is a small utility library designed to make testing function
    inputs less difficult. R is a dynamically typed language, but larger
    projects need input checking for scalabity. needy offers a single
    function, require_a( ), which lets you specify the traits an input object
    should have, such as class, size, numerical properties or number of
    parameters, while reducing boilerplate code and aiding debugging.
Methods to reduce confounding bias from unmeasured confounders in observational studies of vaccine efficacy using negative control outcomes.
The Negative Binomial regression with mean and shape modeling and mean and variance modeling and Beta Binomial regression with mean and dispersion modeling.
Estimating the number of essential genes in a genome on
    the basis of data from a random transposon mutagenesis experiment,
    through the use of a Gibbs sampler.
    Lamichhane et al. (2003) <doi:10.1073/pnas.1231432100>.
Researchers often want to evaluate whether there is a negligible
    relationship among variables. The 'negligible' package provides functions that 
    are useful for conducting negligible effect testing (also called
    equivalence testing). For example, there are functions for evaluating the 
    equivalence of means or the presence of a negligible association (correlation/
    regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) <doi:10.20982/tqmp.16.4.p424>.
    Beribisky, N., Davidson, H., Cribbie, R. A. (2019) <doi:10.7717/peerj.6853>.
    Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) <doi:10.20982/tqmp.14.3.p167>.
    Mara, C. & Cribbie, R. A. (2017) <doi:10.1080/00220973.2017.1301356>.
    Counsell, A. & Cribbie, R. A. (2015) <doi:10.1111/bmsp.12045>.
    van Wieringen, K. & Cribbie, R. A. (2014) <doi:10.1111/bmsp.12015>.
    Goertzen, J. R. & Cribbie, R. A. (2010) <doi:10.1348/000711009x475853>.
    Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) <doi:10.1002/jclp.10217>.
A bootstrap method for Respondent-Driven Sampling (RDS) that relies on the underlying structure of the RDS network to estimate uncertainty.
Neighbourhood functions are key components of
  local-search algorithms such as Simulated Annealing or
  Threshold Accepting.  These functions take a solution and
  return a slightly-modified copy of it, i.e. a neighbour.
  The package provides a function neighbourfun() that
  constructs such neighbourhood functions, based on
  parameters such as admissible ranges for elements in a
  solution.  Supported are numeric and logical solutions.
  The algorithms were originally created for
  portfolio-optimisation applications, but can be used for
  other models as well.  Several recipes for neighbour
  computations are taken from "Numerical Methods and
  Optimization in Finance" by M. Gilli, D. Maringer and
  E. Schumann (2019, ISBN:978-0128150658).
Classification, regression, and clustering with k nearest neighbors
    algorithm. Implements several distance and similarity measures, covering
    continuous and logical features. Outputs ranked neighbors. Most features of
    this package are directly based on the PMML specification for KNN.
Provides several direct search optimization algorithms based on the
  simplex method. The provided algorithms are direct search algorithms, i.e.
  algorithms which do not use the derivative of the cost function. They are
  based on the update of a simplex. The following algorithms are available: the
  fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained
  optimization with a fixed shape simplex, 1962) <doi:10.1080/00401706.1962.10490033>, 
  the variable shape simplex method of Nelder and Mead (unconstrained optimization
  with a variable shape simplex made, 1965) <doi:10.1093/comjnl/7.4.308>, and 
  Box's complex method (constrained optimization with a variable shape simplex,
  1965) <doi: 10.1093/comjnl/8.1.42>.
The aim of the neo2R is to provide simple and low level connectors
   for querying neo4j graph databases (<https://neo4j.com/>).
   The objects returned by the query functions are either lists or data.frames
   with very few post-processing.
   It allows fast processing of queries returning many records.
   And it let the user handle post-processing according to the data model
   and his needs.
Sends queries to a specified 'Neo4J' graph database, capturing results in a dataframe where appropriate.  
    Other useful functions for the importing and management of data on the 'Neo4J' server and basic local server admin.
A Modern and Flexible 'Neo4J' Driver, allowing you to query 
    data on a 'Neo4J' server and handle the results in R. It's modern in 
    the sense it provides a driver 
    that can be easily integrated in a data analysis workflow, especially by 
    providing an API working smoothly with other data analysis and graph 
    packages. It's flexible in the  way it returns the results, by 
    trying to stay as close as 
    possible to the way 'Neo4J' returns data. That way, you have the control 
    over the way you will compute the results. At the same time, the result 
    is not too complex, so that the "heavy lifting" of data wrangling is not 
    left to the user.
Functions for downloading,
    calibrating, and analyzing atmospheric isotope data bundled
    into the eddy covariance data products of the National Ecological
    Observatory Network (NEON) <https://www.neonscience.org>. 
    Calibration tools are provided for carbon and water isotope products.
    Carbon isotope calibration details are found in Fiorella et al. (2021)
    <doi:10.1029/2020JG005862>, and the readme 
    file at <https://github.com/lanl/NEONiso>. Tools for calibrating water 
    isotope products have been added as of 0.6.0, but have known deficiencies
    and should be considered very experimental currently.
NEON observational data are provided via the NEON Data Portal <https://www.neonscience.org> 
  and NEON API, and can be downloaded and reformatted by the 'neonUtilities' package. NEON observational data 
  (human-observed measurements, and analyses derived from human-collected samples, such as tree diameters and 
  algal chemistry) are published in a format consisting of one or more tabular data files. This package provides 
  tools for performing common operations on NEON observational data, including checking for duplicates and 
  joining tables.
The National Ecological Observatory Network (NEON) provides access
  to its numerous data products through its REST API, 
  <https://data.neonscience.org/data-api/>. This package provides a
  high-level user interface for downloading and storing NEON data products.
  Unlike 'neonUtilities', this package will avoid repeated downloading,
  provides persistent storage, and improves performance.  'neonstore' can also
  construct a local 'duckdb' database of stacked tables, making it possible
  to work with tables that are far to big to fit into memory.
NEON data packages can be accessed through the NEON Data Portal <https://www.neonscience.org>
    or through the NEON Data API (see <https://data.neonscience.org/data-api> for documentation). Data delivered from
    the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files
    can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting 
    data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and 
    converting formats. For more information, see the readme file at <https://github.com/NEONScience/NEON-utilities>.
Set of functions to estimate kidney function and other phenotypes of interest in nephrology based on different biomechimal traits. MDRD, CKD-EPI, and Virga equations are compared in Pattaro (2013) <doi:10.1159/000351043>, where the respective references are given. In addition, the software includes Stevens (2008) <doi:10.1053/j.ajkd.2007.11.018> and Cockroft (1976) <doi:10.1159/000180580> formulas. The race-free CKD-EPI equations (2021) <doi:10.1056/NEJMoa2102953> are also implemented from ver.1.3.
Package for a Network assisted algorithm for Epigenetic studies using mean and variance Combined signals: NEpiC. NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network.
An interface to Neptune. A metadata store for MLOps, built for teams that run a lot of experiments.
    It gives you a single place to log, store, display, organize, compare, and query all your model-building metadata.
    Neptune is used for:
    • Experiment tracking: Log, display, organize, and compare ML experiments in a single place.
    • Model registry: Version, store, manage, and query trained models, and model building metadata.
    • Monitoring ML runs live: Record and monitor model training, evaluation, or production runs live
    For more information see <https://neptune.ai/>.
Serves data from the United States Environmental Protection Agency (USEPA) National Eutrophication Survey <https://www.epa.gov/national-aquatic-resource-surveys>.
This tool set provides a set of functions to fit the nested Dirichlet process mixture of products of multinomial distributions (NDPMPM) model for nested categorical household data in the presence of impossible combinations. It has direct applications in imputing missing values for and generating synthetic versions of nested household data.
Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.
Provides a nested menu widget for usage in 'Shiny'
    applications. This is useful for hierarchical choices (e.g. continent,
    country, city).
A modelling framework for nested data using the 'tidymodels' 
    ecosystem. Specify how to nest data using the 'recipes' package, create 
    testing and training splits using 'rsample', and fit models to this data
    using the 'parsnip' and 'workflows' packages. Allows any model to be fit
    to nested data.
Implementation of forward selection based on cross-validated
             linear and logistic regression.
This R package provides a calculation of between-cases AUC estimate, corresponding covariance, and variance estimate in the nested data problem.  Also, the package has the function to simulate the nested data. The calculated between-cases AUC estimate is used to evaluate the reader's diagnostic performance in clinical tasks with nested data. For more details on the above methods, please refer to the paper by H Du, S Wen, Y Guo, F Jin, BD Gallas (2022) <doi:10.1177/09622802221111539>.
Facilitates building a nesting or hierarchical structure as a list or data frame by using a human friendly syntax.
Infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.
In networks, treatments may spill over from the treated individual to his or her social contacts and outcomes may be contagious over time. Under this setting, causal inference on the collective outcome observed over all network is often of interest. We use chain graph models approximating the projection of the full longitudinal data onto the observed data to identify the causal effect of the intervention on the whole outcome. Justification of such approximation is demonstrated in Ogburn et al. (2018) <arXiv:1812.04990>.
Clustering unilayer and multilayer network data by means of finite mixtures is the main utility of 'netClust'.
Facilitates network clustering and evaluation of cluster
        configurations.
Implements a class of univariate and multivariate spatio-network generalised linear mixed models for areal unit and network data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson. Spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution following the Leroux model (Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>). Network structures are modelled by a set of random effects that reflect a multiple membership structure (Browne et al. (2001) <doi:10.1177/1471082X0100100202>).
Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of 'JavaScript' in one package.
Infer system functioning with empirical NETwork COMparisons. These methods are part of a growing paradigm in network science that uses relative comparisons of networks to infer mechanistic classifications and predict systemic interventions. They have been developed and applied in Langendorf and Burgess (2021) <doi:10.1038/s41598-021-99251-7>, Langendorf (2020) <doi:10.1201/9781351190831-6>, and Langendorf and Goldberg (2019) <arXiv:1912.12551>.
Implementations of various control theory methods for use 
            in brain and psychological networks. Contains controllability
            statistics from Pasqualetti, Zampieri & Bullo (2014) <doi:10.1109/TCNS.2014.2310254>,
            optimal control algorithms from Lewis, Vrabie & Syrmos (2012, ISBN:978-0-470-63349-6), and various utilities.
The 'NetCoupler' algorithm identifies potential direct effects of 
    correlated, high-dimensional variables formed as a network with an external 
    variable. The external variable may act as the dependent/response variable 
    or as an independent/predictor variable to the network.
Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.
Social network analysis has become an essential tool in the study of complex systems. 'NetExplorer' allows to visualize and explore complex systems. It is based on 'd3js' library that brings 1) Graphical user interface;  2) Circular, linear, multilayer and force Layout; 3) Network live exploration and 4) SVG exportation.
Functions to analyze and visualize communication data, 
      based on network theory and resampling methods. 
      Farine, D. R. (2017) <doi:10.1111/2041-210X.12772>;
      Carsey, T., & Harden, J. (2014) <doi:10.4135/9781483319605>.
      Primarily targeted at datasets of facial expressions coded with the Facial Action Coding System. 
      Ekman, P., Friesen, W. V., & Hager, J. C. (2002). "Facial action coding system - investigator's guide" <https://www.paulekman.com/facial-action-coding-system/>.
Methods for the generation of a wide range of network geographies,
    e.g., grid networks or clustered networks. Useful for the generation of
    benchmarking instances for the investigation of, e.g., Vehicle-Routing-Problems
    or Travelling Salesperson Problems.
Carry out network-based gene set analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. Implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>.
A multi-core R package that contains a set of tools based on copula graphical
             models for accomplishing the three interrelated goals in genetics and genomics in an
			 unified way: (1) linkage map construction, (2) constructing linkage disequilibrium
			 networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-
			 phenotype-environment interactions networks. 
			 The 'netgwas' package can deal with biparental inbreeding and outbreeding species with
			 any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes),
			 tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where 
			 number of variables p is considerably larger than number of sample sizes (p >> n). 
			 The computations is memory-optimized using the sparse matrix output. The 'netgwas' 
			 implements the methodological developments in Behrouzi and Wit (2017)
             <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <doi:10.1093/bioinformatics/bty777>.
The Bayesian hierarchical model named antigen-T cell interaction estimation is to estimate the history of the immune pressure on the evolution of the tumor clones.The model is based on the estimation result from Andrew Roth (2014) <doi:10.1038/nmeth.2883>.
Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.
Implementation of network integration approaches
    comprising unweighted and weighted integration methods. Unweighted integration
    is performed considering the average, per-edge average, maximum and minimum of
    networks edges. Weighted integration takes into account a weight for each 
    network during the fusion process, where the weights express
    the ''predictiveness strength'' of each network considering a specific predictive
    task. Weights can be learned using a machine learning algorithm able to associate
    the weights to the assessment of the accuracy of the learning algorithm 
    trained on the network itself. The implemented methods can be applied to 
    effectively integrate different biological networks modelling a wide range
    of problems in bioinformatics (e.g. disease gene prioritization, protein
    function prediction, drug repurposing, clinical outcome prediction).
Tools for managing large sets of network data and performing whole network analysis.
    This package is focused on the network based statistic jackknife method, and 
    implements a framework that can be extended to other network manipulations and analyses.
Represent 'network' or 'igraph' objects whose vertices can be represented by features in an 'sf' object as a network graph surmising a 'sf' plot. Fits into 'ggplot2' grammar.
Estimates micro effects on macro structures (MEMS) and average micro mediated effects (AMME) when using statistical models for network structure.
	URL: <https://github.com/sduxbury/netmediate>.
	BugReports: <https://github.com/sduxbury/netmediate/issues>.
	Long, J. Scott, and Sarah Mustillo (2018) <doi:10.1177/0049124118799374>.
   	Mize, Trenton D. (2019) <doi:10.15195/v6.a4>.
	Imai, Kosuke (2010) <doi:10.1037/a0020761>.
	Imai, Kosuke (2010) <doi:10.1214/10-STS321>.
A comprehensive set of functions providing frequentist methods for network meta-analysis and supporting Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis":
 - frequentist network meta-analysis following Rücker (2012) <DOI:10.1002/jrsm.1058>;
 - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <DOI:10.1186/1471-2288-13-35>;
 - measures characterizing the flow of evidence between two treatments by König et al. (2013) <DOI:10.1002/sim.6001>;
 - ranking of treatments (frequentist analogue of SUCRA) according to Rücker & Schwarzer (2015) <DOI:10.1186/s12874-015-0060-8>;
 - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <DOI:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <DOI:10.1002/jrsm.1270>;
 - split direct and indirect evidence to check consistency (Dias et al., 2010) <DOI:10.1002/sim.3767>, (Efthimiou et al., 2019) <DOI:10.1002/sim.8158>;
 - league table with network meta-analysis results;
 - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <DOI:10.1002/bimj.201800167>;
 - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <DOI:10.1002/sim.8158>;
 - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <DOI:10.1002/jrsm.57>;
 - automated drawing of network graphs described in Rücker & Schwarzer (2016) <DOI:10.1002/jrsm.1143>;
 - rankograms and ranking by SUCRA;
 - contribution matrix as described in Papakonstantinou et al. (2018) <DOI:10.12688/f1000research.14770.3> and Davies et al. (2021) <arXiv:2107.02886>.
The field of immunology benefits from software that can
    predict which peptide sequences trigger an immune response.
    'NetMHCIIpan' is a such a tool: it predicts the
    binding strength of a short peptide to a Major Histocompatibility
    Complex class II (MHC-II) molecule.
    'NetMHCIIpan' can be used from a web server at 
    <https://services.healthtech.dtu.dk/service.php?NetMHCIIpan-3.2/>
    or from the command-line, using a local installation. This package
    allows to call 'NetMHCIIpan' from R.
Stochastic collapsed variational inference on mixed-membership stochastic blockmodel for networks,
             incorporating node-level predictors of mixed-membership vectors, as well as 
             dyad-level predictors. For networks observed over time, the model defines a hidden
             Markov process that allows the effects of node-level predictors to evolve in discrete,
             historical periods. In addition, the package offers a variety of utilities for 
             exploring results of estimation, including tools for conducting posterior 
             predictive checks of goodness-of-fit and several plotting functions. The package 
             implements methods described in Olivella, Pratt and Imai (2019) 'Dynamic Stochastic
             Blockmodel Regression for Social Networks: Application to International Conflicts',
             available at <https://www.santiagoolivella.info/pdfs/socnet.pdf>.
Performs network-based source estimation. Different approaches are available: effective distance median (Manitz et al., 2014; <doi:10.1371/currents.outbreaks.f3fdeb08c5b9de7c09ed9cbcef5f01f2>), recursive backtracking (Manitz et al., 2016; <doi:10.1111/rssc.12176>), and centrality-based source estimation (Li et al., 2021; <doi:10.1371/journal.pcbi.1008545>). Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.
A graph visualization engine that puts an emphasis on 
  aesthetics at the same time of providing default parameters that yield
  out-of-the-box-nice visualizations. The package is built on top of
  'The Grid Graphics Package' and seamlessly work with 'igraph' and 
  'network' objects.
Network Pre-Processing and normalization. Methods for normalizing graphs, including Chua normalization, Laplacian normalization, Binary magnification, min-max normalization and others. Methods to sparsify adjacency matrices. Methods for graph pre-processing and for filtering edges of the graph.
Implements methods for centrality related analyses of networks. 
    While the package includes the possibility to build more than 20 indices, 
    its main focus lies on index-free assessment of centrality via partial 
    rankings obtained by neighborhood-inclusion or positional dominance. These 
    partial rankings can be analyzed with different methods, including 
    probabilistic methods like computing expected node ranks and relative 
    rank probabilities (how likely is it that a node is more central than another?).
    The methodology is described in depth in the vignettes and in
    Schoch (2018) <doi:10.1016/j.socnet.2017.12.003>.
Regress network responses (both directed and undirected) onto covariates of interest that may be actor-, relation-, or network-valued. In addition, compute principled variance estimates of the coefficients assuming that the errors are jointly exchangeable. Missing data is accommodated. Additionally implements building and inversion of covariance matrices under joint exchangeability, and  generates random covariance matrices from this class. For more detail on methods, see Marrs, Fosdick, and McCormick (2017) <arXiv:1701.05530>.
Functions for assessing the replication/preservation of a network 
  module's topology across datasets through permutation testing; Ritchie et al. 
  (2015) <doi: 10.1016/j.cels.2016.06.012>.
Statistical entropy analysis of network data as introduced by Frank and Shafie (2016) <doi:10.1177/0759106315615511>, and in a forthcoming book by Nowicki, Shafie and Frank (2022).
Calculates network measures such as Largest Connected Component (LCC), Proximity, Separation, Jaccard Index, 
    along with permutation, when needed.
Segregation is a network-level property such that edges between 
  predefined groups of vertices are relatively less likely. Network homophily 
  is a individual-level tendency to form relations with people who are similar 
  on some attribute (e.g. gender, music taste, social status, etc.). In general
  homophily leads to segregation, but segregation might arise without 
  homophily. This package implements descriptive indices measuring 
  homophily/segregation. It is a computational companion 
  to Bojanowski & Corten (2014) <doi:10.1016/j.socnet.2014.04.001>.
The network structural equation modeling conducts a network 
    statistical analysis on a data frame of coincident observations of 
    multiple continuous variables [1]. 
    It builds a pathway model by exploring a pool of domain knowledge guided 
    candidate statistical relationships between each of the variable pairs, 
    selecting the 'best fit' on the basis of a specific criteria such as 
    adjusted r-squared value.
    This material is based upon work supported by the U.S. National Science 
    Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, 
    under the NSF Solicitation: 
    NSF 20-570 Industry-University Cooperative Research Centers Program 
    [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, 
    Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) 
    <doi:10.1109/ACCESS.2013.2267611>.
We developed a comprehensive tool that helps with visualization and analysis of networks with the same variables across multiple factor levels. The 'netShiny' contains most of the popular network features such as centrality measures, modularity, and other summary statistics (e.g. clustering coefficient). It also contains known tools to look at the (dis)similarities between two networks, such as pairwise distance measures between networks, set operations on the nodes of the networks, distribution of the weights of the edges and a network representing the difference between two correlation matrices. The package 'netShiny' also contains tools to perform bootstrapping and find clusters in networks. See the 'netShiny' manual for more information, documentation and examples.
Assists actuaries and other insurance modellers in pricing,
    reserving and capital modelling for non-life insurance and
    reinsurance modelling. Provides functions that help model
    excess levels, capping and pure Incurred but not reported
    claims (pure IBNR).
    Includes capped mean, exposure curves and increased limit
    factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced
    LogNormal-Pareto and Sliced Gamma-Pareto distributions.
    Includes mean, probability density function (pdf), cumulative
    probability function (cdf) and inverse cumulative probability
    function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto
    distributions.
    Includes calculating pure IBNR exposure with LogNormal and
    Gamma distribution for reporting delay.
R interface for the 'netstat' command line utility used to retrieve and parse 
    commonly used network statistics, including available and in-use 
    transmission control protocol (TCP) ports. Primers offering technical background information 
    on the 'netstat' command line utility are available in the "Linux System Administrator's Manual" 
    by Michael Kerrisk (2014) 
    <https://man7.org/linux/man-pages/man8/netstat.8.html>, and on the Microsoft website (2017) 
    <https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/netstat>.
A set of functions for studying network robustness, resilience and vulnerability.
Features tools for the network data analysis and community detection. 
    Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models. 
    Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models.
    Implements the following: 
    Amini, Chen, Bickel and Levina (2013) <doi:10.1214/13-AOS1138>
    Bickel and Sarkar (2015) <doi:10.1111/rssb.12117>
    Lei (2016) <doi:10.1214/15-AOS1370>
    Wang and Bickel (2017) <doi:10.1214/16-AOS1457>
    Zhang and Amini (2020) <arXiv:2012.15047>
    Le and Levina (2022) <doi:10.1214/21-EJS1971>.
Enables users to retrieve data, meta-data, and codebooks 
    from <https://nettskjema.no/>. The data from the API is richer than from the
    online data portal. Mowinckel (2021) <doi:10.5281/zenodo.4745481>.
Provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon 'igraph'.
Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.
Tools to create and modify network objects.  The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.
We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network.
Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.
This permutation based hypothesis test, suited for Gaussian and binary data, 
    assesses the difference between two networks based on several invariance measures 
    (e.g., network structure invariance, global strength invariance, edge invariance). 
    Network structures are estimated with l1-regularized partial correlations (Gaussian data) 
    or with l1-regularized logistic regression (eLasso, binary data). Suited for comparison 
    of independent and dependent samples. For dependent samples, only supported for data of 
    one group which is measured twice. See van Borkulo et al. (2017) 
    <doi:10.13140/RG.2.2.29455.38569>.
Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey
    graphs from 'R'.
Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) <doi:10.3233/978-1-60750-692-8-227> for an overview on spectral class of inter-graph distance measures.
Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.
A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.
Simulates the extinction of species in the food web and it analyzes
             its cascading effects, described in Dunne et al. (2002) <doi:10.1073/pnas.192407699>.
A network Maze generator that creates different types of network mazes.
This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010)<doi:10.1145/1835804.1835933>. Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process.
Collection of functions for fast manipulation, handling, and analysis of large-scale
    networks based on family and social data. Functions are utility functions used to manipulate data
    in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data.
    When possible, the functions should be able to handle millions of data points quickly for use in combination
    with data from large public national registers and databases.
    Kenneth Lange (2003, ISBN:978-8181281135).
Implements some risk measures for (financial) networks, such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity.
Provides a variety of Network Scale-up Models for researchers to analyze Aggregated Relational Data, mostly through the use of Stan. In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2021) <arXiv:2109.10204>, Zheng, T., Salganik, M. J., and Gelman, A. (2006) <doi:10.1198/016214505000001168>, Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., and Bernard, H. R. (1998) <doi:10.1016/S0378-8733(96)00305-X>, and Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and Johnsen, E. C. (1998) <doi:10.1177/0193841X9802200205>.
Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.
Includes assorted tools for network analysis. Bridge centrality; goldbricker; MDS, PCA, & eigenmodel network plotting.
Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) <doi:10.1007/s11336-020-09731-4>.
Training of neural networks using backpropagation,
    resilient backpropagation with (Riedmiller, 1994) or without
    weight backtracking (Riedmiller and Braun, 1993) or the
    modified globally convergent version by Anastasiadis et al.
    (2005). The package allows flexible settings through
    custom-choice of error and activation function. Furthermore,
    the calculation of generalized weights (Intrator O & Intrator
    N, 1993) is implemented.
Visualization and analysis tools to aid in the interpretation of
    neural network models.  Functions are available for plotting,
    quantifying variable importance, conducting a sensitivity analysis, and
    obtaining a simple list of model weights.
Analysis functions to quantify inputs importance in neural network models.
  Functions are available for calculating and plotting the inputs importance and obtaining
  the activation function of each neuron layer and its derivatives. The importance of a given
  input is defined as the distribution of the derivatives of the output with respect to that
  input in each training data point <doi:10.18637/jss.v102.i07>.
Base package for 'Neuroconductor', which includes many helper 
    functions that interact with objects of class 'nifti', implemented by
    package 'oro.nifti', for reading/writing and also other manipulation 
    functions.
Annotated neuroblastoma copy number profiles,
	     a benchmark data set for change-point detection algorithms.
Installs 'Neuroconductor' packages from the release repository <https://neuroconductor.org/releases/> or from 'GitHub'.
Neural decoding is method of analyzing neural data that  
    uses a pattern classifiers to predict experimental conditions based 
    on neural activity. 'NeuroDecodeR' is a system of objects that 
    makes it easy to run neural decoding analyses. For more information
    on neural decoding see Meyers & Kreiman (2004)
    <doi:10.7551/mitpress/8404.003.0024>.
Downloads and reads data from Human 'Connectome' Project 
    <https://db.humanconnectome.org> using Amazon Web Services ('AWS') 
    'S3' buckets.
A collection of data structures that represent
    volumetric brain imaging data. The focus is on basic data handling for 3D
    and 4D neuroimaging data. In addition, there are function to read and write
    NIFTI files and limited support for reading AFNI files.
Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at <arXiv:1911.04387>.
Preprocessing pipeline for normalizing and cleaning T1-weighted, T2-weighted and FLAIR MRI images coming from different sources, diseases, patients, scanners and sites.
Generates functional Magnetic Resonance Imaging (fMRI) 
             time series or 4D data. Some high-level 
             functions are created for fast data generation with only 
             a few arguments and a diversity of functions to define 
             activation and noise. For more advanced users it is possible 
             to use the low-level functions and manipulate the arguments.
             See Welvaert et al. (2011) <doi:10.18637/jss.v044.i10>.
Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.
In the working paper titled "Why You Should Never Use the Hodrick-Prescott 
   Filter", James D. Hamilton proposes a new alternative to economic time series 
   filtering. The neverhpfilter package provides functions and data for reproducing his work. Hamilton (2017) <doi:10.3386/w23429>.
Computes the probability density function, cumulative distribution function, quantile function, random numbers and measures of inference for the following general families of  distributions (each family defined in terms of an arbitrary cdf G): Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, and exponentiated exponential Poisson G distributions.
Closed testing has been proved powerful for true discovery guarantee. The computation of closed testing is, however, quite burdensome. A general way to reduce computational complexity is to combine partial closed testings for some prespecified feature sets of interest. Partial closed testings are performed at Bonferroni-corrected alpha level to guarantee the lower bounds for the number of true discoveries in prespecified sets are simultaneously valid. For any post hoc chosen sets of interest, coherence property is used to get the lower bound. In this package, we implement closed testing with globaltest to calculate the lower bound for number of true discoveries, see Ningning Xu et.al (2021) <arXiv:2001.01541> for detailed description.
Extends the classical Newman studentized range statistic
  in various ways that can be applied to genome-scale transcriptomic
  or other expression data.
Interface to gather news from the 'News API', based on a multilevel query <https://newsapi.org/>. A personal API key is required.
Programmatically collect normalized news from
    (almost) any website. An 'R' clone of the
    <https://github.com/kotartemiy/newscatcher> 'Python' module.
Semissupervised model for geographical document classification (Watanabe 2018) <doi:10.1080/21670811.2017.1293487>. 
    This package currently contains seed dictionaries in English, German, French, Spanish, Italian, Russian, Hebrew, Arabic Japanese and Chinese (Simplified and Traditional).
Adding updates (version or bullet points) to the NEWS.md
    file.
Implements the routines to compare the survival curves with recurrent events, including the estimations of survival curves. The first model is a  model for recurrent event, when the data are correlated or not  correlated. It was proposed by Wang and Chang (1999) <doi:10.2307/2669690>. In the independent case, the survival function can be  estimated by the generalization of the limit product model of Pena (2001) <doi:10.1198/016214501753381922>.
Responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts and style sheets are based on 'jQuery' <https://jquery.com/> and 'Bootstrap' <https://getbootstrap.com/>.
Indices, heuristics and strategies to help determine the number of factors/components to retain:
                1. Acceleration factor (af with or without Parallel Analysis);
                2. Optimal Coordinates (noc with or without Parallel Analysis);
                3. Parallel analysis (components, factors and bootstrap);
                4. lambda > mean(lambda) (Kaiser, CFA and related);
                5. Cattell-Nelson-Gorsuch (CNG);
                6. Zoski and Jurs multiple regression (b, t and p);
                7. Zoski and Jurs standard error of the regression coeffcient (sescree);
                8. Nelson R2;
                9. Bartlett khi-2;
               10. Anderson khi-2;
               11. Lawley khi-2 and
               12. Bentler-Yuan khi-2.
Commodity pricing models are (systems of) stochastic differential equations that are utilized for the valuation and hedging of commodity contingent claims (i.e. derivative products on the commodity) and other commodity related investments. Commodity pricing models that capture market dynamics are of great importance to commodity market participants in order to exercise sound investment and risk-management strategies. Parameters of commodity pricing models are estimated through maximum likelihood estimation, using available term structure futures data of a commodity. 'NFCP' (n-factor commodity pricing) provides a framework for the modeling, parameter estimation, probabilistic forecasting, option valuation and simulation of commodity prices through state space and Monte Carlo methods, risk-neutral valuation and Kalman filtering. 'NFCP' allows the commodity pricing model to consist of n correlated factors, with both random walk and mean-reverting elements. The n-factor commodity pricing model framework was first presented in the work of Cortazar and Naranjo (2006) <doi:10.1002/fut.20198>. Examples presented in 'NFCP' replicate the two-factor crude oil commodity pricing model presented in the prolific work of Schwartz and Smith (2000) <doi:10.1287/mnsc.46.7.893.12034> with the approximate term structure futures data applied within this study provided in the 'NFCP' package.
This is the R API for the 'nfer' formalism (<http://nfer.io/>).
    'nfer' was developed to specify event stream abstractions for spacecraft 
    telemetry such as the Mars Science Laboratory.  Users write rules using 
    a syntax that borrows heavily from Allen's Temporal Logic that, when 
    applied to an event stream, construct a hierarchy of temporal intervals 
    with data.  The R API supports loading rules from a file or mining them 
    from historical data.  Traces of events or pools of intervals are 
    provided as data frames.
A set of functions to estimate outcomes of fourth down
    plays in the National Football League and obtain fourth down plays
    from <https://www.nfl.com/> and <https://www.espn.com/>.
A set of functions to access National Football
    League play-by-play data from <https://www.nfl.com/>.
A set of functions to visualize National Football League
    analysis in 'ggplot2'.
A minimal package for downloading data from 'GitHub'
    repositories of the 'nflverse' project.
A set of functions to simulate National Football
    League seasons including the sophisticated tie-breaking procedures.
The intent here is to enable the simulation of plays/drives and
    evaluate game-play strategies in the National Football League (NFL).
    Built-in strategies include going for it on fourth down and varying the 
    proportion of passing/rushing plays during a drive. The user should be
    familiar with nflscrapR data before trying to write his/her own 
    strategies. This work is inspired by a blog post by Mike Lopez, 
    currently the  Director of Data and Analytics at the NFL, Lopez (2019) <https://statsbylopez.netlify.app/post/resampling-nfl-drives/>.
The 'nflverse' is a set of packages dedicated to data of the
    National Football League. This package is designed to make it easy to
    install and load multiple 'nflverse' packages in a single step. Learn
    more about the 'nflverse' at <https://nflverse.nflverse.com/>.
Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a technical description of the model <https://www.mcw.edu/-/media/MCW/Departments/Biostatistics/tr72.pdf?la=en>.
Density, distribution function, quantile function and random generation for the 3D Navarro, Frenk & White (NFW) profile. For details see Robotham & Howlett (2018) <arXiv:1805.09550>.
Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.
Posterior distribution of case-control fine-mapping. Specifically, Bayesian variable selection for single-nucleotide polymorphism (SNP) data using the normal-gamma prior. Alenazi A.A., Cox A., Juarez M,. Lin W-Y. and Walters, K. (2019) Bayesian variable selection using partially observed categorical prior information in fine-mapping association studies, Genetic Epidemiology. <doi:10.1002/gepi.22213>.
Provides an 'htmlwidgets' <https://www.htmlwidgets.org/> interface to 'NGL.js' <http://nglviewer.org/ngl/api/>.
    'NGLvieweR' can be used to visualize and interact with protein databank ('PDB') and structural files in R and Shiny applications.
    It includes a set of API functions to manipulate the viewer after creation in Shiny.
An n-gram is a sequence of n "words" taken, in order, from a
    body of text.  This is a collection of utilities for creating,
    displaying, summarizing, and "babbling" n-grams.  The
    'tokenization' and "babbling" are handled by very efficient C
    code, which can even be built as its own standalone library.
    The babbler is a simple Markov chain.  The package also offers
    a vignette with complete example 'workflows' and information about
    the utilities offered in the package.
Retrieve and plot word frequencies through time from the "Google
    Ngram Viewer" <https://books.google.com/ngrams>.
A simple n-gram (contiguous sequences of n items from a
    given sequence of text) tokenizer to be used with the 'tm' package with no
    'rJava'/'RWeka' dependency.
Provides tools for analyzing spatial data, especially non-
    Gaussian areal data. The current version supports the sparse restricted
    spatial regression model of Hughes and Haran (2013) <DOI:10.1111/j.1467-9868.2012.01041.x>,
	the centered autologistic model of Caragea and Kaiser (2009) <DOI:10.1198/jabes.2009.07032>,
	and the Bayesian spatial filtering model of Hughes (2017) <arXiv:1706.04651>.
Body Shape and related measurements from the US National Health
    and Nutrition Examination Survey (NHANES, 1999-2004).  See
    http://www.cdc.gov/nchs/nhanes.htm for details.
Utility to retrieve data from the National Health and Nutrition 
	Examination Survey (NHANES) website <https://www.cdc.gov/nchs/nhanes/index.htm>.
Tools for traversing and working with National Hydrography Dataset Plus (NHDPlus) data. All methods implemented in 'nhdplusTools' are available in the NHDPlus documentation available from the US Environmental Protection Agency <https://www.epa.gov/waterdata/basic-information>.
Tools for working with the National Hydrography Dataset, with 
    functions for querying, downloading, and networking both the NHD 
    <https://www.usgs.gov/national-hydrography> 
    and NHDPlus <https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus> datasets.
Retrieves and processes the data exposed by the open 'NHL' API. This includes information on players, teams, games, tournaments, drafts, standings, schedules and other endpoints. A lower-level interface to access the data via URLs directly is also provided.
Each dataset contains scores for every game during a specific season of the NHL.
Add game events to a database file to use for statistical analysis of hockey games. This means we only call the 'NHL' API
    once for each game we want to add. We will have very fast retrieval of data once games have been added since the data is stored locally.
    We use the API located at <https://statsapi.web.nhl.com/api/v1/teams> with supplemental data from <https://www.nhl.com/scores/>.
    Other endpoints can be found at <https://gitlab.com/dword4/nhlapi>.
Fits non-homogeneous Markov multistate models and misclassification-type hidden Markov models in continuous time to intermittently observed data. Implements the methods in Titman (2011) <doi:10.1111/j.1541-0420.2010.01550.x>. Uses direct numerical solution of the Kolmogorov forward equations to calculate the transition probabilities.
Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations.  Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.
Tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time.
Calculate Overall Survival or Recurrence-Free Survival for breast cancer patients, using 'NHS Predict'. 
            The time interval for the estimation can be set up to 15 years, with default at 10.
            Incremental therapy benefits are estimated for hormone therapy, chemotherapy, trastuzumab, and bisphosphonates.
            An additional function, suited for SCAN audits, features a more user-friendly version of the code, with fewer inputs, but necessitates
            the correct standardised inputs.
            This work is not affiliated with the development of 'NHS Predict' and its underlying statistical model.
            Details on 'NHS Predict' can be found at: <doi:10.1186/bcr2464>.
            The web version of 'NHS Predict': <https://breast.predict.nhs.uk/>.
            A small dataset of 50 fictional patient observations is provided for the purpose of running examples with the main two functions,
            and an additional dataset is provided for running example with the dedicated SCAN function.
Providing a common set of simplified web scraping tools for working with the NHS Data Dictionary <https://datadictionary.nhs.uk/data_elements_overview.html>.
    The intended usage is to access the data elements section of the NHS Data Dictionary to access key lookups.
    The benefits of having it in this package are that the lookups are the live lookups on the website and will not need to be maintained.
    This package was commissioned by the NHS-R community <https://nhsrcommunity.com/> to provide this consistency of lookups.
    The OpenSafely lookups have now been added <https://www.opencodelists.org/docs/>.
Provides functions for working with NHS number checksums.
    The UK's National Health Service issues NHS numbers to all users of its
    services and this package implements functions for verifying that the
    numbers are valid according to the checksum scheme the NHS use.
    Numbers can be validated and checksums created.
Free United Kingdom National Health Service (NHS) and other healthcare, or population health-related data for education and training purposes. This package contains synthetic data based on real healthcare datasets, or cuts of open-licenced official data.  This package exists to support skills development in the NHS-R community: <https://nhsrcommunity.com/>.
Provides tools for drawing Statistical Process Control (SPC) charts. This package supports the NHSE/I
  programme 'Making Data Count', and allows users to draw XmR charts, use change points and apply rules with summary
  indicators for when rules are breached.
Illustrate graphically the most common Null Hypothesis Significance Testing procedures. More specifically, this package provides functions to plot Chi-Squared, F, t (one- and two-tailed) and z (one- and two-tailed) tests, by plotting the probability density under the null hypothesis as a function of the different test statistic values. Although highly flexible (color theme, fonts, etc.), only the minimal number of arguments (observed test statistic, degrees of freedom) are necessary for a clear and useful graph to be plotted, with the observed test statistic and the p value, as well as their corresponding value labels. The axes are automatically scaled to present the relevant part and the overall shape of the probability density function. This package is especially intended for education purposes, as it provides a helpful support to help explain the Null Hypothesis Significance Testing process, its use and/or shortcomings.
The implementation of Markov Model Multiple Imputation
  with the application to COVID-19 scale, NIAID OS.
Color palettes based on nature inspired colours in "Sri Lanka".
Get or set UNIX priority (niceness) of running R process.
Species Identification using DNA Barcodes Integrated with 
             Environmental Niche Models.
Implementation of a probabilistic method to calculate 'nicheROVER' (_niche_ _r_egion and niche _over_lap) metrics using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%).  Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions.  It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region.  The article by Swanson et al. (Ecology, 2015) provides a detailed description of the methodology.  See the package vignette for a worked example using fish stable isotope data.
Tools for reading and writing NIfTI-1.1 (NII) files, including optimized voxelwise read/write operations and a simplified method to write dataframes to NII.
    Specification of the NIfTI-1.1 format can be found here <https://nifti.nimh.nih.gov/nifti-1>.
    Scientific publication first using these tools
        Koscik TR, Man V, Jahn A, Lee CH, Cunningham WA (2020) <doi:10.1016/j.neuroimage.2020.116764> "Decomposing the neural pathways in a simple, value-based choice." Neuroimage, 214, 116764.
Computes and plots the boundary between night and day.
A collection of functions used in remote sensing analysis (e.g., conversion from digital numbers to radiance, reflectance, and temperature). It includes several algorithms to calculate the albedo: Liang (2000) <doi:10.1016/S0034-4257(00)00205-4>, Silva et al. (2016) <doi:10.32614/RJ-2016-051>, Tasumi et al. (2008) <doi:10.1061/(ASCE)1084-0699(2008)13:2(51)>, among others; and include functions to derive several spectral indices. Although the current version implements basic functions, it will be expandable to a more robust tool for water cycle modeling (e.g., to include surface runoff and evapotranspiration calculations) in the near future. 
    This package is under development at the Institute about Natural Resources Research (INIRENA) from the Universidad Michoacana de San Nicolas de Hidalgo.
Routines for enumerating all existing nonnegative integer solutions of a linear Diophantine equation. The package provides routines for solving 0-1, bounded and unbounded knapsack problems; 0-1, bounded and unbounded subset sum problems; additive partitioning of natural numbers; and one-dimensional bin-packing problem.
Th-U-Pb electron microprobe age dating of monazite, as originally 
             described in <doi:10.1016/0009-2541(96)00024-1>.
Miscellaneous R functions developed as collateral damage over the
    course of work in statistical and scientific computing for research. These
    include, for example, utilities that supplement existing idiosyncrasies of
    the R language, extend existing plotting functionality and aesthetics, help
    prepare data objects for imputation, and extend access to command line tools
    and systems-level information.
Functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.
A system for writing hierarchical statistical models largely
    compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models
    and do basic R-style math, and compiling both models and nimbleFunctions via
    custom-generated C++. 'NIMBLE' includes default methods for MCMC, Monte Carlo
    Expectation Maximization, and some other tools. The nimbleFunction system makes
    it easy to do things like implement new MCMC samplers from R, customize the
    assignment of samplers to different parts of a model from R, and compile the
    new samplers automatically via C++ alongside the samplers 'NIMBLE' provides.
    'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New
    distributions and functions can be added, including as calls to external
    compiled code. Although most people think of MCMC as the main goal of the
    'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing
    arbitrary other kinds of model-generic algorithms as well. A full User Manual is
    available at <https://r-nimble.org>.
Functions for adaptive parallel tempering (APT) with NIMBLE models. Adapted from 'Lacki' & 'Miasojedow' (2016) <DOI:10.1007/s11222-015-9579-0> and 'Miasojedow, Moulines and Vihola' (2013) <DOI:10.1080/10618600.2013.778779>.
Provides utility functions and custom probability distribution for Bayesian analyses of radiocarbon dates within the 'nimble' modelling framework.  It includes various population growth models, nimbleFunction objects, as well as a suite of functions for prior and posterior predictive checks for demographic inference (Crema and Shoda (2021) <doi:10.1371/journal.pone.0251695>) and other analyses.
Common ecological distributions for 'nimble' models in the form of nimbleFunction objects. 
  Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models.
  (Jolly (1965) <DOI: 10.2307/2333826>, Seber (1965) <DOI: 10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).
A collection of common univariate bounded probability distributions transformed to the unbounded real line, for the purpose of increased MCMC efficiency.
Provides utility functions, distributions, and fitting methods for Bayesian Spatial Capture-Recapture (SCR) and Open Population Spatial Capture-Recapture (OPSCR) modelling using the nimble package (de Valpine et al. 2017 <doi:10.1080/10618600.2016.1172487 >). Development of the package was motivated primarily by the need for flexible and efficient analysis of large-scale SCR data (Bischof et al. 2020 <doi:10.1073/pnas.2011383117 >). Computational methods and techniques implemented in nimbleSCR include those discussed in Turek et al. 2021 <doi:10.1002/ecs2.3385>; among others. For a recent application of nimbleSCR, see Milleret et al. (2021) <doi:10.1098/rsbl.2021.0128>.
Includes five particle filtering algorithms for use with state space
    models in the 'nimble' system: 'Auxiliary', 'Bootstrap', 'Ensemble Kalman filter',
    'Iterated Filtering 2', and 'Liu-West', as described in Michaud et al. (2021),
    <doi:10.18637/jss.v100.i03>. A full User Manual is available at
    <https://r-nimble.org>.
Principal Components Analysis of a matrix using Non-linear Iterative Partial Least Squares or weighted Expectation Maximization PCA with Gram-Schmidt orthogonalization of the scores and loadings. Optimized for speed. See Andrecut (2009) <doi:10.1089/cmb.2008.0221>.
An implementation of the National Information Platforms for 
    Nutrition or NiPN's analytic methods for assessing quality of anthropometric 
    datasets that include measurements of weight, height or length, middle upper 
    arm circumference, sex and age. The focus is on anthropometric status but 
    many of the presented methods could be applied to other variables.
Digital map data of Japan for choropleth mapping, including a circle cartogram.
Fast and Accurate Trisomy Prediction in Non-Invasive Prenatal
    Testing.
Provides transfusion-related differential tests on Near-infrared spectroscopy (NIRS) time series with detection limit, which contains two testing statistics: Mean Area Under the Curve (MAUC) and slope statistic. This package applied a penalized spline method within imputation setting. Testing is conducted by a nested permutation approach within imputation. Refer to Guo et al (2018) <doi:10.1177/0962280218786302> for further details.
Datasets for testing nonlinear regression routines.
Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for 
    SI (International System of Units) and non-SI units, plus unit conversions
    Based on the data from NIST (National Institute of Standards and Technology, USA)
Contains data, code, and figures from Hill et al. 2018a (Journal of Experimental Marine Biology and Ecology; <DOI: 10.1016/j.jembe.2018.07.006>) and Hill et al. 2018b (Data In Brief <DOI: 10.1016/j.dib.2018.09.133>). Datasets document plant allometry, stem heights, nutrient and stable isotope content, and sediment denitrification enzyme assays. The data and analysis offer an examination of nitrogen uptake and allocation in two salt marsh plant species.
Noninferiority tests for difference in failure rates at a prespecified control rate or prespecified time.
Snow water equivalent is modeled with the process based 'delta.snow' model and empirical regression models using relationships between density and diverse at-site parameters. The methods are described in Winkler et al. (2021) <doi:10.5194/hess-25-1165-2021>, Guyennon et al. (2019) <doi:10.1016/j.coldregions.2019.102859>, Pistocchi (2016) <doi:10.1016/j.ejrh.2016.03.004>, Jonas et al. (2009) <doi:10.1016/j.jhydrol.2009.09.021> and Sturm et al. (2010) <doi:10.1175/2010JHM1202.1>.
Provides an R interface to free geocoding REST APIs maintained by the New Jersey Office of GIS <https://njgin.nj.gov/njgin/edata/geocoding/index.html#!/> and commonly used shapefiles.
SQL like query interface to fetch data from any 'Jira' installation. The data is fetched using 'Jira' REST API, which can be found at the following URL: <https://developer.atlassian.com/cloud/jira/platform/rest/v2>.
Download and analyze motor vehicle crash data released by the New Jersey Department of Transportation (NJDOT).
  The data in this package is collected through the filing of NJTR-1 form by police officers, which provide a standardized way of documenting a motor vehicle crash that occurred in New Jersey.
  3 different data tables containing data on crashes, vehicles & pedestrians released from 2001 to the present can be downloaded & cleaned using this package.
Client for programmatic access to the 2007 and 2012 National 
  Lakes Assessment database <https://www.epa.gov/national-aquatic-resource-surveys/nla> 
  containing data for hundreds of lakes in the lower 48 states of the contiguous US.
Optimization for nonlinear objective and constraint functions.  Linear or nonlinear equality and inequality constraints are allowed.  It accepts the input parameters as a constrained matrix.
Nested loop cross validation for classification purposes for misclassification error rate estimation.
  The package supports several methodologies for feature selection: random forest, Student t-test, limma, 
  and provides an interface to the following classification methods in the 'MLInterfaces' package: linear, 
  quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized 
  linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of
  the classifier are included: plot of the ranks of the features, scores plot for a specific 
  classification algorithm and number of features, misclassification rate 
  for the different number of features and classification algorithms tested and ROC plot.
  For further details about the methodology, please check:
  Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004) 
  <doi:10.2202/1544-6115.1078>.
Solve a system of nonlinear equations using a Broyden or a Newton method
             with a choice of global strategies such as line search and trust region.
             There are options for using a numerical or user supplied Jacobian,
             for specifying a banded numerical Jacobian and for allowing
             a singular or ill-conditioned Jacobian.
Models for non-linear time series analysis and causality detection. The main functionalities of this package consist of an implementation of the classical causality test (C.W.J.Granger 1980) <doi:10.1016/0165-1889(80)90069-X>,  and a non-linear version of it based on feed-forward neural networks. This package contains also an implementation of the Transfer Entropy <doi:10.1103/PhysRevLett.85.461>, and the continuous Transfer Entropy using an approximation based on the k-nearest neighbors <doi:10.1103/PhysRevE.69.066138>. There are also some other useful tools, like the VARNN (Vector Auto-Regressive Neural Network) prediction model, the Augmented test of stationarity, and the discrete and continuous entropy and mutual information.
Create and manipulate numeric list ('nlist') objects.  An
    'nlist' is an S3 list of uniquely named numeric objects.  An numeric
    object is an integer or double vector, matrix or array.  An 'nlists'
    object is a S3 class list of 'nlist' objects with the same names,
    dimensionalities and typeofs.  Numeric list objects are of interest
    because they are the raw data inputs for analytic engines such as
    'JAGS', 'STAN' and 'TMB'.  Numeric lists objects, which are useful for
    storing multiple realizations of of simulated data sets, can be
    converted to coda::mcmc and coda::mcmc.list objects.
Estimation of relatively complex nonlinear mixed-effects models, including the Sigmoidal Mixed Model and the Piecewise Linear Mixed Model with abrupt or smooth transition, through a single intuitive line of code and with automated generation of starting values.
Fit and compare Gaussian linear and nonlinear mixed-effects models.
Datasets and utility  functions enhancing functionality of nlme package. Datasets, functions and scripts are described in book titled 'Linear Mixed-Effects Models:
 A Step-by-Step Approach' by Galecki and Burzykowski (2013). Package is under development.
Various visual and numerical diagnosis methods for the nonlinear mixed effect model, including visual predictive checks, numerical predictive checks, and coverage plots (Karlsson and Holford, 2008, <https://www.page-meeting.org/?abstract=1434>).
Fit and compare nonlinear mixed-effects models in differential
    equations with flexible dosing information commonly seen in pharmacokinetics
    and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 
    <doi:10.1007/s10928-015-9409-1>). Differential equation solving is 
    by compiled C code provided in the 'rxode2' package
    (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Datasets for 'nlmixr2' and 'rxode2'. 'nlmixr2' is used for fitting and comparing
    nonlinear mixed-effects models in differential
    equations with flexible dosing information commonly seen in pharmacokinetics
    and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 
    <doi:10.1007/s10928-015-9409-1>). Differential equation solving is 
    by compiled C code provided in the 'rxode2' package
    (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Fit and compare nonlinear mixed-effects models in
    differential equations with flexible dosing information commonly seen
    in pharmacokinetics and pharmacodynamics (Almquist, Leander, and
    Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation
    solving is by compiled C code provided in the 'rxode2' package (Wang,
    Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Fit and compare nonlinear mixed-effects models in
    differential equations with flexible dosing information commonly seen
    in pharmacokinetics and pharmacodynamics (Almquist, Leander, and
    Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation
    solving is by compiled C code provided in the 'rxode2' package (Wang,
    Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for
    support functions like preconditioned fits
    <doi:10.1208/s12248-016-9866-5>, boostrap and stepwise covariate
    selection.
A model library for 'nlmixr2'.  The models include
  (and plan to include) pharmacokinetic, pharmacodynamic, and
  disease models used in pharmacometrics.  Where applicable,
  references for each model are included in the meta-data for
  each individual model.  The package also includes model
  composition and modification functions to make model updates
  easier.
Fit and compare nonlinear mixed-effects models in
    differential equations with flexible dosing information commonly seen
    in pharmacokinetics and pharmacodynamics (Almquist, Leander, and
    Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation
    solving is by compiled C code provided in the 'rxode2' package (Wang,
    Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for
    'ggplot2' plotting methods for 'nlmixr2' objects.
Replacement for nls() tools for working with nonlinear least squares problems.
      The calling structure is similar to, but much simpler than, that of the nls()
      function. Moreover, where nls() specifically does NOT deal with small or zero
      residual problems, nlmrt is quite happy to solve them. It also attempts to be
      more robust in finding solutions, thereby avoiding 'singular gradient' messages
      that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach
      in nlmrt generally works more reliably to get a solution, though this may be 
      one of a set of possibilities, and may also be statistically unsatisfactory.
      Added print and summary as of August 28, 2012.
Package to select best model  among several linear and nonlinear models. The main function uses the gnls() function from the 'nlme' package to fit the data to nine regression models, named: "linear", "quadratic", "cubic", "logistic", "exponential", "power", "monod", "haldane", "logit".
It includes four methods: DCOL-based K-profiles clustering, non-linear network reconstruction, non-linear hierarchical clustering, and variable selection for generalized additive model. References:  Tianwei Yu (2018)<DOI: 10.1002/sam.11381>; Haodong Liu and others (2016)<DOI: 10.1371/journal.pone.0158247>; Kai Wang and others (2015)<DOI: 10.1155/2015/918954>; Tianwei Yu and others (2010)<DOI: 10.1109/TCBB.2010.73>.
Solve optimization problems using an R interface to NLopt. NLopt is a 
    free/open-source library for nonlinear optimization, providing a common
    interface for a number of different free optimization routines available
    online as well as original implementations of various other algorithms.
    See <https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/> for more
    information on the available algorithms. Building from included sources 
    requires 'CMake'. On Linux and 'macOS', if a suitable system build of 
    NLopt (2.7.0 or later) is found, it is used; otherwise, it is built 
    from included sources via 'CMake'. On Windows, NLopt is obtained through 
    'rwinlib' for 'R <= 4.1.x' or grabbed from the 'Rtools42 toolchain' for 
    'R >= 4.2.0'.
Basic classes and methods for Natural Language Processing.
Stanford 'CoreNLP' annotation client.
  Stanford 'CoreNLP' <https://stanfordnlp.github.io/CoreNLP/index.html> integrates all 
  NLP tools from the Stanford Natural Language Processing Group, 
  including a part-of-speech (POS) tagger, a named entity recognizer (NER), 
  a parser, and a coreference resolution system, and provides model files 
  for the analysis of English. More information can be found in the README.
Methods for obtaining improved estimates of non-linear cross-validated risks are obtained using targeted minimum loss-based estimation, estimating equations, and one-step estimation (Benkeser, Petersen, van der Laan (2019), <doi:10.1080/01621459.2019.1668794>). Cross-validated area under the receiver operating characteristics curve (LeDell, Petersen, van der Laan (2015), <doi:10.1214/15-EJS1035>) and other metrics are included.
Utilities for Natural Language Processing.
Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) <doi:10.1093/aob/mcg029>. There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty eight (28) new self-start (SS) functions in total. This package also supports the publication 'Nonlinear regression Models and applications in agricultural research' by Archontoulis and Miguez (2015) <doi:10.2134/agronj2012.0506>, a book chapter with similar material <doi:10.2134/appliedstatistics.2016.0003.c15> and a publication by Oddi et. al. (2019) in Ecology and Evolution <doi:10.1002/ece3.5543>. The function 'nlsLMList' uses 'nlsLM' for fitting, but it is otherwise almost identical to 'nlme::nlsList'.In addition, this release of the package provides functions for conducting simulations for 'nlme' and 'gnls' objects as well as bootstrapping. These functions are intended to work with the modeling framework of the 'nlme' package. It also provides four vignettes with extended examples.
Likelihood inference based on higher order approximations 
  for nonlinear models with possibly non constant variance.
This is a package which can help you search for the root
        of a equation.
Estimate the non-linear odds ratio and plot it against a continuous exposure.
Setup, run and analyze 'NetLogo' (<https://ccl.northwestern.edu/netlogo/>) model simulations in 'R'.
    'nlrx' experiments use a similar structure as 'NetLogos' Behavior Space experiments. 
    However, 'nlrx' offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses.
    The user defines all information that is needed in an intuitive framework, using class objects.
    Experiments are submitted from 'R' to 'NetLogo' via 'XML' files that are dynamically written, based on specifications defined by the user.
    By nesting model calls in future environments, large simulation design with many runs can be executed in parallel.
    This also enables simulating 'NetLogo' experiments on remote high performance computing machines.
    In order to use this package, 'Java' and 'NetLogo' (>= 5.3.1) need to be available on the executing system.
Non-linear least squares regression with the Levenberg-Marquardt algorithm using multiple starting values for increasing the chance that the minimum found is the global minimum.
Adds brute force and multiple starting values to nls.
Estimation of structural equation models with nonlinear effects
  and underlying nonnormal distributions.
A few utilities for summarizing, testing, and plotting non-linear
    regression models fit with nls(), nlsList() or nlme().
Non-linear shrinkage estimation of population eigenvalues and covariance
    matrices, based on publications by Ledoit and Wolf (2004, 2015, 2016).
We solve non linear least squares problems with optional
    equality and/or inequality constraints. Non linear iterations are
    globalized with back-tracking method. Linear problems are solved by
    dense QR decomposition from 'LAPACK' which can limit the size of
    treated problems. On the other side, we avoid condition number
    degradation which happens in classical quadratic programming approach.
    Inequality constraints treatment on each non
    linear iteration is based on 'NNLS' method (by Lawson and Hanson).
    We provide an original function 'lsi_ln' for solving linear least squares
    problem with inequality constraints in least norm sens. Thus if Jacobian of
    the problem is rank deficient a solution still can be provided.
    However, truncation errors are probable in this case.
    Equality constraints are treated by using a basis of Null-space.
    User defined function calculating residuals must return a list having
    residual vector (not their squared sum) and Jacobian. If Jacobian is
    not in the returned list, package 'numDeriv' is used to calculated
    finite difference version of Jacobian. The 'NLSIC' method was fist
    published in Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.
Data sets and nonlinear regression models dedicated to predictive microbiology.
Fit univariate non-linear scale mixture of skew-normal(NL-SMSN) regression, details in Garay, Lachos and Abanto-Valle (2011) <doi:10.1016/j.jkss.2010.08.003> and Lachos, Bandyopadhyay and Garay (2011) <doi:10.1016/j.spl.2011.03.019>.
Provides tools for working with nonlinear least squares problems.
      For the estimation of models reliable and robust tools than nls(), where the
      the Gauss-Newton method frequently stops with 'singular gradient' messages. 
      This is accomplished by using, where possible, analytic derivatives to compute
      the matrix of derivatives and a stabilization of the solution of the estimation
      equations. Tools for approximate or externally supplied derivative matrices
      are included. Bounds and masks on parameters are handled properly.
Set of functions implementing the algorithm described in Fernandez 
    Torvisco et al. (2018) for fitting separable nonlinear regression curves. 
    See Fernandez Torvisco, Rodriguez-Arias Fernandez and Cabello Sanchez (2018)
    <doi:10.2298/FIL1812233T>.
Several tools for assessing the quality of fit of a gaussian nonlinear model are provided.
Uses a modified lifting algorithm on which it builds the
        nondecimated lifting transform. It has applications in wavelet
        shrinkage.
Fits a non-linear transformation model ('nltm') for
        analyzing survival data, see Tsodikov (2003) <doi:10.1111/1467-9868.00414>. The
        class of 'nltm' includes the following currently supported
        models: Cox proportional hazard, proportional hazard cure,
        proportional odds, proportional hazard - proportional hazard
        cure, proportional hazard - proportional odds cure, Gamma
        frailty, and proportional hazard - proportional odds.
R functions for (non)linear time series analysis with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.
Provides functions to calculate the normalised Lineage-Through-
    Time (nLTT) statistic, given two phylogenetic trees. The nLTT statistic measures
    the difference between two Lineage-Through-Time curves, where each curve is
    normalised both in time and in number of lineages.
Wald Test for nonlinear restrictions on model parameters and confidence
             intervals for nonlinear functions  of parameters using delta-method.  Applicable
             after ANY model, provided parameters estimates and their covariance matrix are
             available.
Set of functions for accessing database of network meta-analyses described in 
  Petropoulou M, et al. Bibliographic study showed improving statistical methodology of network
  meta-analyses published between 1999 and 2015
  <doi:10.1016/j.jclinepi.2016.11.002>. The database is hosted in a REDcap database at the 
  Institute of Social and Preventive Medicine (ISPM) in the University of Bern.
Implements HSROC (hierarchical summary receiver operating characteristic) model developed by Ma, Lian, Chu, Ibrahim, and Chen (2018) <doi:10.1093/biostatistics/kxx025>
  and hierarchical model developed by Lian, Hodges, and Chu (2019) <doi:10.1080/01621459.2018.1476239> for performing meta-analysis for 1-5 diagnostic tests to simultaneously 
    compare multiple tests within a missing data framework. This package evaluates the accuracy of 
    multiple diagnostic tests and also gives graphical representation of the results.
Performs network meta-analysis using integrated nested Laplace approximations ('INLA') 
             which is described in Guenhan, Held, and Friede (2018) <doi:10.1002/jrsm.1285>. 
             Includes methods to assess the heterogeneity and inconsistency in the network. 
             Contains more than ten different network meta-analysis dataset. 
             'INLA' package can be obtained from <https://www.r-inla.org>.
A set of functions providing several outlier (i.e., studies with extreme findings) and influential detection measures and methodologies in network meta-analysis :
               - simple outlier and influential detection measures
               - outlier and influential detection measures by considering study deletion (shift the mean)
               - plots for outlier and influential detection measures
	       - Q-Q plot for network meta-analysis
               - Forward Search algorithm in network meta-analysis. 
               - forward plots to monitor statistics in each step of the forward search algorithm
               - forward plots for summary estimates and their confidence intervals in each step of forward search algorithm.
A graphical display of results from network meta-analysis (NMA). 
  It is suitable for outcomes like odds ratio (OR), risk ratio (RR), 
  risk difference (RD) and standardized mean difference (SMD). 
  It also has an option to visually display and compare 
  the surface under the cumulative ranking (SUCRA) of different treatments.
Derives the most frequent hierarchies along with their probability of occurrence. One can also define complex hierarchy criteria and calculate their probability. Methodology based on Papakonstantinou et al. (2021) <DOI:10.21203/rs.3.rs-858140/v1>.
Calculation and presentation of decision-invariant bias adjustment
    thresholds and intervals for Network Meta-Analysis, as described by 
    Phillippo et al. (2018) <doi:10.1111/rssa.12341>. These describe the 
    smallest changes to the data that would result in a change of decision.
Essentials for PK/PD (pharmacokinetics/pharmacodynamics) such as area under the curve, (geometric) coefficient of variation, and other calculations that are not part of base R. This is not a noncompartmental analysis (NCA) package.
Efficient tools for preparation, checking and post-processing of data in PK/PD (pharmacokinetics/pharmacodynamics) modeling, with focus on use of Nonmem. Helps with trivial but tedious tasks and tries to identify errors to save time on debugging. Implemented in 'data.table', but easily integrated with 'base' and 'tidyverse'.
Provides a framework to perform Non-negative Matrix
    Factorization (NMF). The package implements a set of already published algorithms
    and seeding methods, and provides a framework to test, develop and plug
    new/custom algorithms. Most of the built-in algorithms have been optimized
    in C++, and the main interface function provides an easy way of performing
    parallel computations on multicore machines.
Calculates the normalized mutual information (NMI) of two community structures in network analysis.
Contains functions useful for debugging, set operations on vectors,
    and 'UTC' date and time functionality. It adds a few vector manipulation 
    verbs to 'purrr' and 'dplyr' packages. It can also generate an R file to 
    install and update packages to simplify deployment into production. The 
    functions were developed at the data science firm 'Numeract LLC' and are 
    used in several packages and projects.
A program for Bayesian analysis of univariate normal mixtures with an unknown number
  of components, following the approach of Richardson and Green (1997) <doi:10.1111/1467-9868.00095>.
  This makes use of reversible jump Markov chain Monte Carlo methods that are capable of jumping
  between the parameter sub-spaces corresponding to different numbers of components in the mixture.
  A sample from the full joint distribution of all unknown variables is thereby generated,
  and this can be used as a basis for a thorough presentation of many aspects of the posterior
  distribution.
Provides residuals and overdispersion metrics to assess the fit of N-mixture models obtained using the package 'unmarked'. 
    Details on the methods are given in Knape et al. (2017) <doi:10.1101/194340>.
We fit inverse probability weighting estimator and the augmented inverse probability weighting for non-monotone missing at random data.
Functions, examples and data from the first and
  the second edition of "Numerical Methods and Optimization
  in Finance" by M. Gilli, D. Maringer and E. Schumann
  (2019, ISBN:978-0128150658).  The package provides
  implementations of optimisation heuristics (Differential
  Evolution, Genetic Algorithms, Particle Swarm
  Optimisation, Simulated Annealing and Threshold
  Accepting), and other optimisation tools, such as grid
  search and greedy search.  There are also functions for
  the valuation of financial instruments such as bonds and
  options, for portfolio selection and functions that help
  with stochastic simulations.
Industrialisation of 'NONMEM'
  <https://www.iconplc.com/innovation/nonmem/> via fully and rapidly reusable 
  model development 'workflows' entirely within 'RStudio'. Quickly get started
  with new models by importing 'NONMEM' templates from the built-in code
  library. Manipulate 'NONMEM' code from within R either via the tracked 
  'manual edit' interface or 'programmatically' via convenience functions. 
  Script 'workflows' by piping sequences of model building steps from control 
  file creation, to execution, to post-processing and evaluation. Run caching 
  makes 'workflows' R markdown friendly for easy documentation of thoughts and
  modelling decisions alongside executable code. Share, reuse and recycle
  'workflows' for new problems.
A Non-Metric Space Library ('NMSLIB' <https://github.com/nmslib/nmslib>) wrapper, which according to the authors "is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The goal of the 'NMSLIB' <https://github.com/nmslib/nmslib> Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods". The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the 'NMSLIB' <https://github.com/nmslib/nmslib> 'Python' Library.
Perform an exploration and a preliminary analysis on the dose-
    response relationship of nanomaterial toxicity. Several functions are
    provided for data exploration, including functions for creating a subset of
    dataset, frequency tables and plots. Inference for order restricted dose-
    response data is performed by testing the significance of monotonic
    dose-response relationship, using Williams, Marcus, M, Modified M and 
    Likelihood ratio tests. Several methods of multiplicity adjustment 
    are also provided. Description of the methods can be found in <https://github.com/rahmasarina/dose-response-analysis/blob/main/Methodology.pdf>.
Due to 'Rstudio's' status as open source software, we believe it will be utilized frequently for future data analysis by users whom lack formal training or experience with 'R'. The 'NMVANOVA' (Novice Model Variation ANOVA) a streamlined variation of experimental design functions that allows novice 'Rstudio' users to perform different model variations one-way analysis of variance without downloading  multiple libraries or packages. Users can easily manipulate the data block, and needed inputs so that users only have to plugin the four designed variables/values.
This shows how NONMEM(R) <http://www.iconplc.com/innovation/nonmem/> software works. NONMEM's classical estimation methods like 'First Order(FO) approximation', 'First Order Conditional Estimation(FOCE)', and 'Laplacian approximation' are explained.
Calculates spatial pattern analysis using a T-square sample procedure.
  This method is based on two measures "x" and "y".
  "x" - Distance from the random point to the nearest individual.
  "y" - Distance from individual to its nearest neighbor.
  This is a methodology commonly used in phytosociology or marine benthos ecology to analyze the species' distribution (random, uniform or clumped patterns).
  Ludwig & Reynolds (1988, ISBN:0471832359).
Datasets and functions to benchmark (convergence, speed, ease of use) R packages dedicated to regression with neural networks (no classification in this version). The templates for the tested packages are available in the R, R Markdown and HTML formats at <https://github.com/pkR-pkR/NNbenchmarkTemplates> and <https://theairbend3r.github.io/NNbenchmarkWeb/index.html>. The submitted article to the R-Journal can be read at <https://www.inmodelia.com/gsoc2020.html>.
Provides nearest-neighbors matching and 
    analysis of case-control data. Cui, Z., Marder, E. P., Click, E. S., 
    Hoekstra, R. M., & Bruce, B. B. (2022) <doi:10.1097/EDE.0000000000001504>.
Software for feed-forward neural networks with a single
  hidden layer, and for multinomial log-linear models.
Automatic time series modelling with neural networks. 
    Allows fully automatic, semi-manual or fully manual specification of networks. For details of the
	specification methodology see: (i) Crone and Kourentzes (2010) <doi:10.1016/j.neucom.2010.01.017>;
	and (ii) Kourentzes et al. (2014) <doi:10.1016/j.eswa.2013.12.011>.
Functions to compute the non-negative garrote estimator as 
             proposed by Breiman (1995) <https://www.jstor.org/stable/1269730>
             with the penalized initial estimators extension as proposed by 
             Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.
K-nearest neighbor search for projected and non-projected 'sf' spatial layers. Nearest neighbor search uses (1) C code from 'GeographicLib' for lon-lat point layers, (2) function knn() from package 'nabor' for projected point layers, or (3) function st_distance() from package 'sf' for line or polygon layers. The package also includes several other utility functions for spatial analysis.
Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.
Contains a module to define neural networks from custom components and versions of Autoencoder, BP, LVQ, MAM NN.
An R interface to the Lawson-Hanson implementation of an
        algorithm for non-negative least squares (NNLS).  Also allows
        the combination of non-negative and non-positive constraints.
Imputation for both missing covariates and censored observations (optional) for survival data with missing covariates by the nearest neighbor based multiple imputation algorithm as described in Hsu et al. (2006) <doi:10.1002/sim.2452>, and Hsu and Yu (2018) <doi: 10.1177/0962280218772592>. Note that the current version can only impute for a situation with one missing covariate.
Nonlinear nonparametric statistics using partial moments.  Partial moments are the elements of variance and asymptotically approximate the area of f(x).  These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences.  NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance.  All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).
Contains the functions for testing the spatial patterns (of segregation, spatial symmetry, 
      association, disease clustering, species correspondence and reflexivity) based on nearest neighbor relations, 
      especially using contingency tables such as 
      nearest neighbor contingency tables (Ceyhan (2010) <doi:10.1007/s10651-008-0104-x> and 
      Ceyhan (2017) <doi:10.1016/j.jkss.2016.10.002> and references therein),
      nearest neighbor symmetry contingency tables (Ceyhan (2014) <doi:10.1155/2014/698296>),
      species correspondence contingency tables and reflexivity contingency tables (Ceyhan (2018) <doi:10.2436/20.8080.02.72>)
      for two (or higher) dimensional data. Also contains functions for generating patterns of segregation, association,
      uniformity in a multi-class setting (Ceyhan (2014) <doi:10.1007/s00477-013-0824-9>), 
      and various non-random labeling patterns for disease clustering in two dimensional cases (Ceyhan (2014)
      <doi:10.1002/sim.6053>), and for visualization of all these patterns for the two dimensional data.
      The tests are usually (asymptotic) normal z-tests and chi-square tests.
Estimate the NNT using the proposed method in Yang and Yin's paper (2019)
  <doi:10.1371/journal.pone.0223301>, in which the NNT-RMST (number needed to 
  treat based on the restricted mean survival time) is defined as the RMST (restricted 
  mean survival time) in the control group divided by the difference in RMSTs 
  between the treatment and control groups up to a chosen time t.
Helps a clinical trial team  discuss
    the clinical goals  of a well-defined biomarker with a diagnostic,
    staging, prognostic, or predictive purpose.  From this discussion will
    come a statistical plan for a (non-randomized) validation trial.
    Both prospective and retrospective trials are supported. In a specific
    focused discussion, investigators should determine the range of
    "discomfort" for the NNT, number needed to treat.  The meaning of
    the discomfort range, [NNTlower, NNTupper], is that within this range
    most physicians would feel discomfort either in treating or withholding
    treatment. A pair of NNT values bracketing that range, NNTpos and NNTneg,
    become the targets of the study's design.  If the trial can demonstrate
    that a positive biomarker test yields an NNT less than NNTlower,
    and that a negative biomarker test yields an NNT less than NNTlower,
    then the biomarker may be useful for patients. A highlight of the package
    is visualization of a "contra-Bayes" theorem, which produces criteria for
    retrospective case-controls studies.
Some functions for performing non-negative matrix factorization, non-negative CANDECOMP/PARAFAC (CP) decomposition, non-negative Tucker decomposition, and generating toy model data. See Andrzej Cichock et al (2009) and the reference section of GitHub README.md <https://github.com/rikenbit/nnTensor>, for details of the methods.
Functions for revealing what happens when effect size estimates 
    from previous studies are taken into account when evaluating each new dataset 
    in a study sequence. The analyses can be conducted for cumulative
    meta-analyses and for Bayesian data analyses. The package contains sample 
    data for a wide selection of research topics. Jointly considering 
    previous findings along with new data is more likely to result in correct 
    conclusions than does the traditional practice of not incorporating previous
    findings, which often results in a back and forth ping-pong of conclusions 
    when evaluating a sequence of studies.
    O'Connor & Ermacora (2021, <doi:10.3758/bf03200807>).
Provides a small set of tools for collecting data from National 
    Oceanic and Atmospheric Administration (NOAA) data sources.  The functions 
    provided in the package are wrappers around NOAA's existing APIs which is
    found at <https://api.tidesandcurrents.noaa.gov/api/prod/>.
Allows users to explore and plot data from the
    National Oceanic and Atmospheric Administration (NOAA) 
    Storm Events database through R for United States counties. 
    Functionality includes matching storm event listings by time and 
    location to hurricane best tracks data. This work was 
    supported by grants from the Colorado Water Center, the National Institute 
    of Environmental Health Sciences (R00ES022631) and the National Science 
    Foundation (1331399).
Generate pseudonymous animal names that are delightful and easy to 
    remember like the Likable Leech and the Proud Chickadee. A unique pseudonym 
    can be created for every unique element in a vector or row in a data frame. 
    Pseudonyms can be customized and tracked over time, so that the same input 
    is always assigned the same pseudonym.
A Bayesian approach to estimate the number of occurred-but-not-yet-reported cases from incomplete, time-stamped reporting data for disease outbreaks. 'NobBS' learns the reporting delay distribution and the time evolution of the epidemic curve to produce smoothed nowcasts in both stable and time-varying case reporting settings, as described in McGough et al. (2019) <doi:10.1101/663823>.
Designed to be compatible with the 'R' package 'DBI' (Database Interface)
    when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>.
    To do this the 'R' 'AWS' Software Development Kit ('SDK') 'paws' 
    <https://github.com/paws-r/paws> is used as a driver.
Simplified document database access and manipulation,
     providing a common API across supported 'NoSQL' databases 
     'Elasticsearch', 'CouchDB', 'MongoDB' as well as 
     'SQLite/JSON1', 'PostgreSQL', and 'DuckDB'.
Given any graph, the 'node2vec' algorithm can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.The techniques are detailed in the paper "node2vec: Scalable Feature Learning for Networks" by Aditya Grover, Jure Leskovec(2016),available at <arXiv:1607.00653>.
Simulate DNA sequences for the node substitution model.
    In the node substitution model, substitutions accumulate additionally 
    during a speciation event, providing a potential mechanistic explanation for 
    substitution rate variation. This package provides tools to simulate
    such a process, simulate a reference process with only substitutions along
    the branches, and provides tools to infer phylogenies from alignments. More
    information can be found in Janzen (2021) <doi:10.1093/sysbio/syab085>.
An implementation of the nodiv algorithm, see Borregaard, M.K., Rahbek, C., Fjeldsaa, J., Parra, J.L., Whittaker, R.J. & Graham, C.H. 2014. Node-based analysis of species distributions. Methods in Ecology and Evolution 5(11): 1225-1235. <DOI:10.1111/2041-210X.12283>. Package for phylogenetic analysis of species distributions. The main function goes through each node in the phylogeny, compares the distributions of the two descendant nodes, and compares the result to a null model. This highlights nodes where major distributional divergence have occurred. The distributional divergence for these nodes is mapped using the SOS statistic.
Provides a compact variation of the usual syntax of function
  declaration, in order to support tidyverse-style quasiquotation of a
  function's arguments and body.
The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008).
Functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186.  Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471.
Implementation of models for the controlled introduction of errors in 
	classification datasets. This package contains the noise models described in 
	Saez (2022) <doi:10.3390/math10203736> that allow corrupting class labels, 
	attributes and both simultaneously.
Cross-Entropy optimisation of unconstrained deterministic and noisy
    functions illustrated in Rubinstein and Kroese (2004, ISBN:
    978-1-4419-1940-3) through a highly flexible and customisable function which 
    allows user to define custom variable domains, sampling distributions,
    updating and smoothing rules, and stopping criteria. Several built-in
    methods and settings make the package very easy-to-use under standard
    optimisation problems.
Quantifies and removes technical noise from high-throughput 
        sequencing data. Two approaches are used, one based on the count
        matrix, and one using the alignment BAM files directly.
        Contains several options for every step of the process, as well
        as tools to quality check and assess the stability of output.
Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph 
    and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying 
    binary graph. This procedure comes with a control of the false discovery rate. The method is described
    in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, 
    E. Roquain, F. Villers (2020) <arXiv:1907.10176>.
Converts numeric vectors to character vectors of English
    number names. Provides conversion to cardinals, ordinals, numerators,
    and denominators. Supports negative and non-integer numbers.
Similarity measures for hierarchical clustering of objects characterized by
    nominal (categorical) variables. Evaluation criteria for nominal data clustering.
Lite interface for getting data from 'OSM' service
    'Nominatim' <https://nominatim.org/release-docs/latest/>. Extract
    coordinates from addresses, find places near a set of coordinates,
    search for amenities and return spatial objects on 'sf' format.
Access UK official statistics from the 'Nomis' database. 
    'Nomis' includes data from the Census, the Labour Force Survey, DWP benefit 
    statistics and other economic and demographic data from the Office for 
    National Statistics, based around statistical geographies. See 
    <https://www.nomisweb.co.uk/api/v01/help> for full API documentation.
A tool for drawing sassy 'UML' diagrams based on a simple syntax,
  see <https://www.nomnoml.com>. Supports styling, R Markdown and exporting diagrams 
  in the PNG format.
A nomogram can not be easily applied,
    because it is difficult to calculate the points or even the survival probability.
  The package, including a function of nomogramEx(),
    is to extract the polynomial equations to calculate the points of each variable,
    and the survival probability corresponding to the total points.
A nomogram, which can be carried out in 'rms' package, provides a 
    graphical explanation of a prediction process. However, it is not very easy
    to draw straight lines, read points and probabilities accurately. Even, it 
    is hard for users to calculate total points and probabilities for all 
    subjects.
  This package provides formula_rd() and formula_lp() functions to fit the 
    formula of total points with raw data and linear predictors respectively by
    polynomial regression. Function points_cal() will help you calculate the 
    total points. prob_cal() can be used to calculate the probabilities after
    lrm(), cph() or psm() regression. 
  For more complex condition, interaction or restricted cubic spine, TotalPoints.rms() 
    can be used.
Implements the nomord_probe() function, which performs a statistical analysis on an input vector (sequence) of nominal (categorical) values.
Conduct a noncompartmental analysis with industrial strength.
             Some features are
             1) Use of CDISC SDTM terms
             2) Automatic or manual slope selection
             3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
             4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
             * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).