This function takes a vector or matrix of data and smooths
    the data with an improved Savitzky Golay transform. The Savitzky-Golay
    method for data smoothing and differentiation calculates convolution
    weights using Gram polynomials that exactly reproduce the results of
    least-squares polynomial regression. Use of the Savitzky-Golay
    method requires specification of both filter length and
    polynomial degree to calculate convolution weights. For maximum
    smoothing of statistical noise in data, polynomials with
    low degrees are desirable, while a high polynomial degree
    is necessary for accurate reproduction of peaks in the data.
    Extension of the least-squares regression formalism with
    statistical testing of additional terms of polynomial degree
    to a heuristically chosen minimum for each data window leads
    to an adaptive-degree polynomial filter (ADPF). Based on noise
    reduction for data that consist of pure noise and on signal
    reproduction for data that is purely signal, ADPF performed
    nearly as well as the optimally chosen fixed-degree
    Savitzky-Golay filter and outperformed sub-optimally chosen
    Savitzky-Golay filters. For synthetic data consisting of noise
    and signal, ADPF outperformed both optimally chosen and
    sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) <doi:10.1021/ac00113a006> for more information.
Obtain overlapping clustering models for object-by-variable data
        matrices using the Additive Profile Clustering (ADPROCLUS) method. 
        Also contains the low dimensional ADPROCLUS method 
        for simultaneous dimension reduction and overlapping clustering. 
        For reference see Depril, Van Mechelen, Mirkin (2008) 
        <doi:10.1016/j.csda.2008.04.014> and Depril, Van Mechelen, Wilderjans 
        (2012) <doi:10.1007/s00357-012-9112-5>.
Provides the functions for planning and conducting a
  clinical trial with adaptive sample size determination. Maximal statistical
  efficiency will be exploited even when dramatic or multiple adaptations
  are made. Such a trial consists of adaptive determination of sample size
  at an interim analysis and implementation of frequentist statistical test at the
  interim and final analysis with a prefixed significance level. The required
  assumptions for the stage-wise test statistics are independent and stationary
  increments and normality. Predetermination of adaptation rule is not required.
Perform first- and second-order multi-scale analyses derived from Ripley K-function (Ripley B. D. (1977) <doi:10.1111/j.2517-6161.1977.tb01615.x>), for univariate,
 multivariate and marked mapped data in rectangular, circular or irregular shaped sampling windows, with tests of 
 statistical significance based on Monte Carlo simulations.
Interact with Google Ads Data Hub API <https://developers.google.com/ads-data-hub/reference/rest>. The functionality allows to fetch customer details, submit queries to ADH.
Analyzes autocorrelation and partial autocorrelation using surrogate methods and 
  bootstrapping, and computes the acceleration constants for the vectorized moving block 
  bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated 
  intervals and estimates partial autocorrelations using Durbin-Levinson. This package 
  calculates the autocorrelation power spectrum, computes cross-correlations between two 
  time series, computes bandwidth for any time series, and performs autocorrelation frequency 
  analysis. It also calculates the periodicity of a time series.
Provides different functionalities and calculations used in the world of basketball to analyze the statistics of the players, the statistics of the teams, the statistics of the quintets and the statistics of the plays. For more details of the calculations included in the package can be found in the book Basketball on Paper written by Dean Oliver.
Functions to compute upper Clopper-Pearson confidence limits of early life failure probabilities and required sample sizes of burn-in studies under further available information, e.g. from other products or technologies.
This software solves an Advection Bi-Flux Diffusive Problem using the Finite Difference Method FDM. Vasconcellos, J.F.V., Marinho, G.M., Zanni, J.H., 2016, Numerical analysis of an anomalous diffusion with a bimodal flux distribution. <doi:10.1016/j.rimni.2016.05.001>. Silva, L.G., Knupp, D.C., Bevilacqua, L., Galeao, A.C.N.R., Silva Neto, A.J., 2014, Formulation and solution of an Inverse Anomalous Diffusion Problem with Stochastic Techniques. <doi:10.5902/2179460X13184>. In this version, it is possible to include a source as a function depending on space and time, that is, s(x,t).
Interactive 'R' tutorials written using 'learnr' for Field (2016), "An Adventure in Statistics", <ISBN:9781446210451>.
    Topics include general workflow in 'R' and 'Rstudio', the 'R' environment and 'tidyverse', summarizing data, model fitting, central tendency, 
    visualising data using 'ggplot2', inferential statistics and robust estimation, hypothesis testing, the general linear model, comparing means,
    repeated measures designs, factorial designs, multilevel models, growth models, and generalized linear models (logistic regression).
Accurate point and interval estimation methods for multiple linear regression coefficients, under classical normal and independent error assumptions, taking into account variable selection.
The irregularly-spaced data are interpolated onto regular latitude-longitude grids by weighting each station according to its distance and angle from the center of a search radius. In addition to this, we also provide a simple way (Jones and Hulme, 1996) to grid the irregularly-spaced data points onto regular latitude-longitude grids by averaging all stations in grid-boxes.
Implements wavelet-based approaches for describing population admixture. Principal Components Analysis (PCA) is used to define the population structure and produce a localized admixture signal for each individual. Wavelet summaries of the PCA output describe variation present in the data and can be related to population-level demographic processes. For more details, see J Sanderson, H Sudoyo, TM Karafet, MF Hammer and MP Cox. 2015. Reconstructing past admixture processes from local genomic ancestry using wavelet transformation. Genetics 200:469-481 <doi:10.1534/genetics.115.176842>.
Allows access to selected services that are part of the 
    'Google Adwords' API <https://developers.google.com/adwords/api/docs/guides/start>.
	'Google Adwords' is an online advertising service by 'Google', that delivers Ads 
	to users. This package offers a authentication process using 'OAUTH2'.  Currently, 
	there are two methods of data of accessing the API, depending on the type of 
	request. One method uses 'SOAP' requests which require building an 'XML' structure 
	and then sent to the API. These are used for the 'ManagedCustomerService' and 
	the 'TargetingIdeaService'. The second method is by building 'AWQL' queries for 
	the reporting side of the 'Google Adwords' API.
A powerful tool for automating the early detection of disease
             outbreaks in time series data. 'aeddo' employs advanced statistical 
             methods, including hierarchical models, in an innovative manner to 
             effectively characterize outbreak signals. It is particularly useful 
             for epidemiologists, public health professionals, and researchers 
             seeking to identify and respond to disease outbreaks in a timely 
             fashion. For a detailed reference on hierarchical models, consult 
             Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.
Package to incorporate change point analysis in ARIMA forecasting.
A powerful tool for automating the early detection of seasonal epidemic
             onsets in time series data. It offers the ability to estimate growth rates
             for consecutive time intervals and calculate the sum of cases (SoC) within 
             those intervals. It is particularly useful for epidemiologists, public 
             health professionals, and researchers seeking to identify and respond to 
             seasonal epidemics in a timely fashion.
             For reference on growth rate estimation, see Walling and Lipstich (2007) 
             <doi:10.1098/rspb.2006.3754> and Obadia et al. (2012) <doi:10.1186/1472-6947-12-147>.
We extend existing gene enrichment tests to perform adverse
    event enrichment analysis. Unlike the continuous gene expression data,
    adverse event data are counts. Therefore, adverse event data has many zeros
    and ties. We propose two enrichment tests. One is a modified Fisher's exact
    test based on pre-selected significant adverse events, while the other is
    based on a modified Kolmogorov-Smirnov statistic. We add Covariate
    adjustment to improve the analysis."Adverse event enrichment tests using
    VAERS" Shuoran Li, Lili Zhao (2020) <arXiv:2007.02266>.
Developed for Computing the probability density function, cumulative 
             distribution function, random generation, estimating the parameters of asymmetric
             exponential power distribution, and robust regression analysis with error
             term that follows asymmetric exponential power distribution. The asymmetric
             exponential power distribution studied here is a special case of that introduced
             by Dongming and Zinde-Walsh (2009) <doi:10.1016/j.jeconom.2008.09.038>.
Functions, data sets, examples, demos, and vignettes for the book
             Christian Kleiber and Achim Zeileis (2008),
	     Applied Econometrics with R, Springer-Verlag, New York.
	     ISBN 978-0-387-77316-2. <doi:10.1007/978-0-387-77318-6>
             (See the vignette "AER" for a package overview.)
Different tools for managing databases of airborne particles, elaborating the main calculations and visualization of results. In a first step, data are checked using tools for quality control and all missing gaps are completed. Then, the main parameters of the pollen season are calculated and represented graphically. Multiple graphical tools are available: pollen calendars, phenological plots, time series, tendencies, interactive plots, abundance plots...
Estimate ideal efficiencies of aerosol sampling through sample
    lines. Functions were developed consistent with the approach described in 
    Hogue, Mark; Thompson, Martha; Farfan, Eduardo; Hadlock, Dennis, (2014), 
    "Hand Calculations for Transport of Radioactive Aerosols through Sampling 
    Systems" Health Phys 106, 5, S78-S87, <doi:10.1097/HP.0000000000000092>.
Estimates the attributable fraction in different sampling designs
    adjusted for measured confounders using logistic regression (cross-sectional
    and case-control designs), conditional logistic regression (matched case-control
    design), Cox proportional hazard regression (cohort design with time-to-
    event outcome), gamma-frailty model with a Weibull baseline hazard and instrumental variables analysis.
    An exploration of the AF with a genetic exposure can be found in the package 'AFheritability' Dahlqwist E et al. (2019) <doi:10.1007/s00439-019-02006-8>.
This is an implementation of the Generalized Discrimination Score
    (also known as Two Alternatives Forced Choice Score, 2AFC) for various 
    representations of forecasts and verifying observations. The Generalized 
    Discrimination Score is a generic forecast verification framework which 
    can be applied to any of the following verification contexts: dichotomous, 
    polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble.
    A comprehensive description of the Generalized Discrimination Score, including 
    all equations used in this package, is provided by Mason and Weigel (2009) 
    <doi:10.1175/MWR-D-10-05069.1>.
Government Analysis Function recommended colours for use in
    charts on gov.uk to help meet accessibility guidance.
Estimate diagnosis performance (Sensitivity, Specificity, 
    Positive predictive value, Negative predicted value) of a diagnostic test
    where can not measure the golden standard but can estimate it using the 
    attributable fraction.
Convenience functions for analyzing factorial experiments using ANOVA or
         mixed models. aov_ez(), aov_car(), and aov_4() allow specification of
         between, within (i.e., repeated-measures), or mixed (i.e., split-plot) 
         ANOVAs for data in long format (i.e., one observation per row),
         automatically aggregating multiple observations per individual and cell 
         of the design. mixed() fits mixed models using lme4::lmer() and computes 
         p-values for all fixed effects using either Kenward-Roger or Satterthwaite 
         approximation for degrees of freedom (LMM only), parametric bootstrap 
         (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). 
         afex_plot() provides a high-level interface for interaction or one-way 
         plots using ggplot2, combining raw data and model estimates. afex uses 
         type 3 sums of squares as default (imitating commercial statistical software).
We aim to deal with data with measurement error in the response and misclassification censoring status under an AFT model. This package primarily contains three functions, which are used to generate artificial data, correction for error-prone data and estimate the functional covariates for an AFT model.
Tools for raster georeferencing, grid affine transforms, and general raster logic. 
 These functions provide converters between raster specifications, world vector, geotransform, 
 'RasterIO' window, and 'RasterIO window' in 'sf' package list format. There are functions to offset
 a matrix by padding any of four corners (useful for vectorizing neighbourhood operations), and
 helper functions to harvesting user clicks on a graphics device to use for simple georeferencing
 of images.  Methods used are available from <https://en.wikipedia.org/wiki/World_file> and
 <https://gdal.org/user/raster_data_model.html>.
Tools to study sorting patterns in matching markets and to estimate the affinity matrix of both the bipartite one-to-one matching model without frictions and with Transferable Utility by 'Dupuy' and 'Galichon' (2014) <doi:10.1086/677191> and its 'unipartite' variant by 'Ciscato', 'Galichon' and 'Gousse' (2020) <doi:10.1086/704611>. It also contains all the necessary tools to implement the 'saliency' analysis, to run rank tests of the affinity matrix and to build tables and plots summarizing the findings.
Enables to compute the statistical indices of affluence (richness) with bootstrap errors, and inequality and polarization indices. Moreover, gives the possibility of calculation of Medeiros affluence line. In 2.1 version some simple errors are fixed.
The AFfunction() is a function which returns an estimate of the Attributable Fraction (AF) and a plot of the AF as a function of heritability, disease prevalence, size of target group and intervention effect. 
             Since the AF is a function of several factors, a shiny app is used to better illustrate how the relationship between the AF and heritability depends on several other factors. The app is ran by the function runShinyApp().
             For more information see Dahlqwist E et al. (2019) <doi:10.1007/s00439-019-02006-8>.
Provides Atomic Force Microscope images analysis such as Gaussian mixes identification, Power
    Spectral Density, roughness against lengthscale, experimental variogram and variogram models,
    fractal dimension and scale, 2D network analysis. The AFM images can be exported to STL format for 3D
    printing.
Set of functions for analyzing Atomic Force Microscope (AFM) force-distance curves. It allows to obtain the contact and unbinding points, perform the baseline correction, estimate the Young's modulus, fit up to two exponential decay function to a stress-relaxation / creep experiment, obtain adhesion energies. These operations can be done either over a single F-d curve or over a set of F-d curves in batch  mode.
Allows estimation and modelling of flight costs in animal (vertebrate) flight,
    implementing the aerodynamic power model described in Klein Heerenbrink et al.
    (2015) <doi:10.1098/rspa.2014.0952>. Taking inspiration from the program
    'Flight', developed by Colin Pennycuick (Pennycuick (2008) "Modelling the flying
    bird". Amsterdam: Elsevier. ISBN 0-19-857721-4), flight performance is estimated
    based on basic morphological measurements such as body mass, wingspan and wing
    area. 'afpt' can be used to make predictions on how animals should adjust their
    flight behaviour and wingbeat kinematics to varying flight conditions.
Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data.
  The package includes functions from existing R packages adapted for banking sector of Kazakhstan.
  The purpose of the package is to optimize statistical functions for easier interpretation for bank analysts and non-statisticians.
An R API providing access to a relational database with macroeconomic data for Africa. 
             The database contains >700 macroeconomic time series from mostly international sources, 
             grouped into 50 macroeconomic and development-related topics. Series are carefully selected
             on the basis of data coverage for Africa, frequency, and relevance to the macro-development context. 
             The project is part of the 'Kiel Institute Africa Initiative' 
             <https://www.ifw-kiel.de/institute/initiatives/kiel-institute-africa-initiative/>, 
             which, amongst other things, aims to develop a parsimonious database with highly relevant indicators 
             to monitor macroeconomic developments in Africa, accessible through a fast API and a web-based platform
             at <https://africamonitor.ifw-kiel.de/>. 
             The database is maintained at the Kiel Institute for the World Economy <https://www.ifw-kiel.de/>.
A collection of methods for both the rank-based estimates and least-square estimates to the Accelerated Failure Time (AFT) model. For rank-based estimation, it provides approaches that include the computationally efficient Gehan's weight and the general's weight such as the logrank weight. Details of the rank-based estimation can be found in Chiou et al. (2014) <doi:10.1007/s11222-013-9388-2> and Chiou et al. (2015) <doi:10.1002/sim.6415>. For the least-square estimation, the estimating equation is solved with generalized estimating equations (GEE). Moreover, in multivariate cases, the dependence working correlation structure can be specified in GEE's setting. Details on the least-squares estimation can be found in Chiou et al. (2014) <doi:10.1007/s10985-014-9292-x>.
Functions for Posterior estimates of Accelerated Failure Time(AFT) model with MCMC and Maximum likelihood estimates of AFT model without MCMC for univariate and multivariate analysis in high dimensional gene expression data are available in this 'afthd' package.
            AFT model with Bayesian framework for multivariate in high dimensional data has been proposed by Prabhash et al.(2016)
             <doi:10.21307/stattrans-2016-046>.
Compute the R-squared measure under the accelerated failure time (AFT) models proposed in Chan et. al. (2018) <doi:10.1080/03610918.2016.1177072>.
A collection of model checking methods for semiparametric accelerated 
    failure time (AFT) models under the rank-based approach. For the (computational)
    efficiency, Gehan's weight is used. It provides functions to verify whether the 
    observed data fit the specific model assumptions such as a functional form of 
    each covariate, a link function, and an omnibus test. The p-value offered in this 
    package is based on the Kolmogorov-type supremum test and the variance of the 
    proposed test statistics is estimated through the re-sampling method. Furthermore, 
    a graphical technique to compare the shape of the observed residual to a number of 
    the approximated realizations is provided.
Tools for downloading and extracting data from the Copernicus "Agrometeorological indicators 
  from 1979 to present derived from reanalysis"
  <https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview> (AgERA5).
Calculate 'ActiGraph' counts from the X, Y, and Z axes of a triaxial 
    accelerometer. This work was inspired by Neishabouri et al. who published the 
    article "Quantification of Acceleration as Activity Counts in 'ActiGraph' Wearables" 
    on February 24, 2022. The link to the article (<https://pubmed.ncbi.nlm.nih.gov/35831446>) 
    and 'python' implementation of this code (<https://github.com/actigraph/agcounts>).
Tools for the analysis of growth data: to extract an 
    LMS table from a gamlss object, to calculate the standard 
    deviation scores and its inverse, and to superpose two wormplots 
    from different models. The package contains a some varieties of 
    reference tables, especially for The Netherlands.
Pair of simple convenience functions to convert a vector of birth dates to age and age distributions. These functions may be helpful when related age and custom age distributions are desired given a vector of birth dates.
An R wrapper for 'agena.ai' <https://www.agena.ai> which provides users capabilities to work with 'agena.ai' using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to 'agena.ai' cloud or local API for calculations. Note: running calculations requires a valid 'agena.ai' API license (past the initial trial period of the local API).
Provides a collection of efficient functions for working with
  individual ages and corresponding intervals. These include functions for
  conversion from an age to an interval, aggregation of ages with associated
  counts in to intervals and the splitting of interval counts based on
  specified age distributions.
Implements the Agnostic Fay-Herriot model, an extension of 
    the traditional small area model. In place of normal sampling errors, the 
    sampling error distribution is estimated with a Gaussian process to 
    accommodate a broader class of distributions. This flexibility is most 
    useful in the presence of bounded, multi-modal, or heavily skewed sampling 
    errors.
The 'agghoo' procedure is an alternative to usual cross-validation.
    Instead of choosing the best model trained on V subsamples, it determines
    a winner model for each subsample, and then aggregates the V outputs.
    For the details, see "Aggregated hold-out" by Guillaume Maillard,
    Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890>
    published in Journal of Machine Learning Research 22(20):1--55.
Convenience functions for aggregating a data frame or data table.
    Currently mean, sum and variance are supported. For Date variables, the recency
    and duration are supported. There is also support for dummy variables in
    predictive contexts. Code has been completely re-written in data.table for computational speed.
Contains functionality for performing the following methods of p-value aggregation: Fisher's method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher's method) [Lancaster, HO (1961, <doi:10.1111/j.1467-842X.1961.tb00058.x>)], and Sidak correction [Sidak, Z (1967, <doi:10.1080/01621459.1967.10482935>)].  Please cite Yi et al., the manuscript corresponding to this package [Yi, L et al., (2017), <doi:10.1101/190199>].
Nonparametric data-driven approach to discovering heterogeneous subgroups in a selection-on-observables framework. 
    aggTrees allows researchers to assess whether there exists relevant heterogeneity in treatment effects by generating a sequence of optimal groupings, 
    one for each level of granularity. For each grouping, we obtain point estimation and inference about the Group Average Treatment Effects. 
    Please reference the use as Di Francesco (2022) <doi:10.2139/ssrn.4304256>.
Provides several methods for aggregating probabilistic forecasts. You have a group of
    people who have made probabilistic forecasts for the same event. You want to take advantage of
    the "wisdom of the crowd" and combine these forecasts in some sensible way. This package
    provides implementations of several strategies, including geometric mean of odds, an extremized
    aggregate (Neyman, Roughgarden (2021) <doi:10.1145/3490486.3538243>), and "high-density trimmed
    mean" (Powell et al. (2022) <doi:10.1037/dec0000191>).
Computation of A (pedigree), G (genomic-base), and H (A corrected
    by G) relationship matrices for diploid and autopolyploid species. Several methods
    are implemented considering additive and non-additive models.
Adaptive Gauss Hermite Quadrature for Bayesian inference.
    The AGHQ method for normalizing posterior distributions
    and making Bayesian inferences based on them. Functions are provided for doing
    quadrature and marginal Laplace approximations, and summary methods are provided
    for making inferences based on the results. 
    See Stringer (2021). "Implementing Adaptive Quadrature for Bayesian Inference: 
    the aghq Package" <arXiv:2101.04468>.
Provides functions to fit Accurate Generalized Linear Model (AGLM) models, visualize them, and predict for new data. AGLM is defined as a regularized GLM which applies a sort of feature transformations using a discretization of numerical features and specific coding methodologies of dummy variables. For more information on AGLM, see Suguru Fujita, Toyoto Tanaka, Kenji Kondo and Hirokazu Iwasawa (2020) <https://www.institutdesactuaires.com/global/gene/link.php?doc_id=16273&fg=1>.
Tools supporting multi-criteria and group decision making,
    including variable number of criteria, by means of
    aggregation operators, spread measures,
    fuzzy logic connectives, fusion functions,
    and preordered sets. Possible applications include,
    but are not limited to, quality management, scientometrics,
    software engineering, etc.
Functionalities to simulate space-time data and to estimate dynamic-spatial panel data models. 
             Estimators implemented are the BCML (Elhorst (2010), <doi:10.1016/j.regsciurbeco.2010.03.003>), the MML (Elhorst (2010) <doi:10.1016/j.regsciurbeco.2010.03.003>) and the INLA Bayesian estimator (Lindgren and Rue, (2015) <doi:10.18637/jss.v063.i19>; Bivand, Gomez-Rubio and Rue, (2015) <doi:10.18637/jss.v063.i20>) 
             adapted to panel data. The package contains functions to replicate the analyses of the scientific article entitled "Agricultural Productivity in Space" (Baldoni and Esposti (2021), <doi:10.1111/ajae.12155>)).
Bland-Altman plot and scatter plot with identity line 
             for visualization and point and 
             interval estimates for different metrics related to 
             reproducibility/repeatability/agreement including
             the concordance correlation coefficient, 
             intraclass correlation coefficient,
             within-subject coefficient of variation,
             smallest detectable difference, 
             and mean normalized smallest detectable difference.
A tool for calculating agreement interval of two measurement methods (Jason Liao (2015) <DOI:10.1515/ijb-2014-0030>) and present results in plots with discordance rate and/or clinically meaningful limit to quantify agreement quality.
Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.
Visualization of Design of Experiments from the 'agricolae' package with 'ggplot2' framework
    The user provides an experiment design from the 'agricolae' package, calls the corresponding function and will receive a 
    visualization with 'ggplot2' based functions that are specific for each design. As there are many different designs, each design is tested on its type.
    The output can be modified with standard 'ggplot2' commands or with other packages with 'ggplot2' function extensions.
Datasets from books, papers, and websites related to agriculture.
    Example graphics and analyses are included. Data come from small-plot trials,
    multi-environment trials, uniformity trials, yield monitors, and more.
Functions to calculate Gray Level Co-occurrence Matrix(GLCM), RGB-based Vegetative Index(RGB VI) and Normalized Difference Vegetation Index(NDVI) family image features. GLCM calculations are based on Haralick (1973) <doi:10.1109/TSMC.1973.4309314>.
Example software for the analysis of data from designed 
	experiments, especially agricultural crop experiments. The basics 
	of the analysis of designed experiments are discussed 
	using real examples from agricultural field trials. 
	A range of statistical methods using a range
	of R statistical packages are  exemplified . The experimental
	data is made available as separate data sets for each example
	and the R analysis code is made available as example code.
	The example code can be readily extended, as required.
Utilities designed to make the analysis of field trials easier and
 more accessible for everyone working in plant breeding. It provides a simple
 and intuitive interface for conducting single and multi-environmental trial
 analysis, with minimal coding required. Whether you're a beginner or an
 experienced user, 'agriutilities' will help you quickly and easily carry out
 complex analyses with confidence. With built-in functions for fitting Linear
 Mixed Models, 'agriutilities' is the ideal choice for anyone who wants to save
 time and focus on interpreting their results.
 Some of the functions require the R package 'asreml' for the 'ASReml' software,
 this can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r>).
Spatial modeling of energy balance and actual 
    evapotranspiration using satellite images and meteorological data. 
    Options of satellite are: Landsat-8 (with and without thermal bands), 
    Sentinel-2 and MODIS. Respectively spatial resolutions are 30, 100, 
    10 and 250 meters. User can use data from a single meteorological 
    station or a grid of meteorological stations (using any spatial 
    interpolation method). Silva, Teixeira, and Manzione (2019) <doi:10.1016/j.envsoft.2019.104497>.
Calculates concentration and dispersion in ordered rating scales. It implements various measures of concentration and dispersion to describe what researchers variably call agreement, concentration, consensus, dispersion, or polarization among respondents in ordered data. It also implements other related measures to classify distributions. In addition to a generic city-block based concentration measure and a generic dispersion measure, the package implements various measures, including van der Eijk's (2001) <DOI: 10.1023/A:1010374114305> measure of agreement A, measures of concentration by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, Berry and Mielke, Reardon, and Garcia-Montalvo and Reynal-Querol. Furthermore, the package provides an implementation of Galtungs AJUS-system to classify distributions, as well as a function to identify the position of multiple modes.
Performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), <doi:10.14393/ufu.te.2019.1244>). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas).
Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). <doi:10.2134/agronj2012.0506>). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.
Statistical procedures to perform stability analysis in plant breeding and to identify stable genotypes under diverse environments. It is possible to calculate coefficient of homeostaticity by Khangildin et al. (1979), variance of specific adaptive ability by Kilchevsky&Khotyleva (1989), weighted homeostaticity index by Martynov (1990), steadiness of stability index by Udachin (1990), superiority measure by Lin&Binn (1988) <doi:10.4141/cjps88-018>, regression on environmental index by Erberhart&Rassel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>, Tai's (1971) stability parameters <doi:10.2135/cropsci1971.0011183X001100020006x>, stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>, ecovalence by Wricke (1962), nonparametric stability parameters by Nassar&Huehn (1987) <doi:10.2307/2531947>, Francis&Kannenberg's parameters of stability (1978) <doi:10.4141/cjps78-157>.
In total it has 7 functions, three for calculating machine calibration, which determine application rate (L/ha), nozzle flow (L/min) and amount of product (L or kg) to be added. to the tank with each sprayer filling. Two functions for graphs of the flow distribution of the nozzles (L/min) in the application bar and, of the temporal variability of the meteorological conditions (air temperature, relative humidity of the air and wind speed). Two functions to determine the spray deposit (uL/cm2), through the methodology called spectrophotometry, with the aid of bright blue (Palladini, L.A., Raetano, C.G., Velini, E.D. (2005), <doi:10.1590/S0103-90162005000500005>) or metallic markers (Chaim, A., Castro, V.L.S.S., Correles, F.M., Galvão, J.A.H., Cabral, O.M.R., Nicolella, G. (1999), <doi:10.1590/S0100-204X1999000500003>). The package supports the analysis and representation of information, using a single free software that meets the most diverse areas of activity in application technology.
Construct time series for Germany's municipalities (Gemeinden) and districts (Kreise) using a annual crosswalk constructed by the Federal Office for Building and Regional Planning (BBSR).
Fast and automatic gradient tree boosting designed
    to avoid manual tuning and cross-validation by utilizing an information
    theoretic approach. This makes the algorithm adaptive to the dataset at
    hand; it is completely automatic, and with minimal worries of overfitting.
    Consequently, the speed-ups relative to state-of-the-art implementations
    can be in the thousands while mathematical and technical knowledge required
    on the user are minimized.
Create and evaluate models using 'tidymodels' and 'h2o' <https://h2o.ai/>. The
    package enables users to specify 'h2o' as an engine for several
    modeling methods.
'Align-GVGD' ('A-GVGD') is a method to predict the impact of
    'missense' substitutions based on the properties of amino acid side
    chains and protein multiple sequence alignments
    <doi:10.1136/jmg.2005.033878>. 'A-GVGD' is an extension of the original
    'Grantham' distance to multiple sequence alignments. This package
    provides an alternative R implementation to the web version found on
    <http://agvgd.hci.utah.edu/>.
Computationally efficient procedures for regularized
        estimation with the semiparametric additive hazards regression
        model.
An implementation of the additive heredity model for the mixture-of-mixtures experiments of Shen et al. (2019) in Technometrics <doi:10.1080/00401706.2019.1630010>. The additive heredity model considers an additive structure to inherently connect the major components with the minor components. The additive heredity model has a meaningful interpretation for the estimated model because of the hierarchical and heredity principles applied and the nonnegative garrote technique used for variable selection.
Provides functions to simulate data sets from hierarchical ecological models, including all the simulations described in the two volume publication 'Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS' by Marc Kéry and Andy Royle: volume 1 (2016, ISBN: 978-0-12-801378-6) and volume 2 (2021, ISBN: 978-0-12-809585-0), <https://www.mbr-pwrc.usgs.gov/pubanalysis/keryroylebook/>. It also has all the utility functions and data sets needed to replicate the analyses shown in the books.
Methods for fitting additive hazards model.
             Perform the maximum likelihood method as well as the traditional Aalen's method for estimating the additive hazards model. 
             For details see Chengyuan Lu(2021) <arXiv:2004.06156>.
Implementation of the Artificial Hydrocarbon Networks for data
    modeling.
Aho-Corasick is an optimal algorithm for finding many
    keywords in a text. It can locate all matches in a text in O(N+M) time; i.e.,
    the time needed scales linearly with the number of keywords (N) and the size of
    the text (M). Compare this to the naive approach which takes O(N*M) time to loop
    through each pattern and scan for it in the text. This implementation builds the
    trie (the generic name of the data structure) and runs the search in a single
    function call. If you want to search multiple texts with the same trie, the
    function will take a list or vector of texts and return a list of matches to
    each text. By default, all 128 ASCII characters are allowed in both the keywords
    and the text. A more efficient trie is possible if the alphabet size can be
    reduced. For example, DNA sequences use at most 19 distinct characters and
    usually only 4; protein sequences use at most 26 distinct characters and usually
    only 20. UTF-8 (Unicode) matching is not currently supported.
Implements the Analytic Hierarchy Process (AHP) method using Gaussian normalization (AHPGaussian) to derive the relative weights of the criteria and alternatives. It also includes functions for visualizing the results and generating graphical outputs. Method as described in: dos Santos, Marcos (2021) <doi:10.13033/ijahp.v13i1.833>.
The AHP method (Analytic Hierarchy Process) is a multi-criteria decision-making method addressing
             choice and outranking problems. The method enables to perform the analysis of alternatives in each
             type of criterion and then provides a global performance of each alternative in the decision context.
             The main difference of this package is the possibility of evaluating the alternatives using quantitative
             data, by numerical representation, and qualitative data, using the Saaty scale, providing preference
             relation between variables by a pairwise evaluation.
The Analytic Hierarchy Process is a versatile multi-criteria decision-making tool introduced by Saaty (1987) <doi:10.1016/0270-0255(87)90473-8> that allows decision-makers to weigh attributes and evaluate alternatives presented to them.    This package provides a consistent methodology for researchers to reformat data and run analytic hierarchy process in R on data that are formatted using the survey data entry mode. It is optimized for performing the analytic hierarchy process with many decision-makers, and provides tools and options for researchers to aggregate individual preferences and test multiple options. It also allows researchers to quantify, visualize and correct for inconsistency in the decision-maker's comparisons.
A Swiss Army knife of utility functions for users of the Analytic
    Hierarchy Process (AHP) which will help you to assess the consistency of a
    PCM as well as to improve its consistency ratio, to compute the sensitivity
    of a PCM, create a logical, not a random PCM, from the preferences you 
    provide for the alternatives, and a function that helps evaluate the actual
    consistency of a PCM based on objective, fair bench marking. The various
    functions in the toolkit additionally provide the flexibility to users to 
    specify only the upper triangular comparison ratios of the PCM in order to
    performs its assigned task.
Implementation of a hybrid MCDM method build from the AHP (Analytic Hierarchy Process) and TOPSIS-2N (Technique for Order of Preference by Similarity to Ideal Solution - with two normalizations). This method is described in Souza et al. (2018) <doi: 10.1142/S0219622018500207>.
Compute a tree level hierarchy, judgment matrix, consistency index and ratio, priority vectors, hierarchic synthesis and rank. Based on the book entitled "Models, Methods, Concepts and Applications of the Analytic Hierarchy Process" by Saaty and Vargas (2012, ISBN 978-1-4614-3597-6).
Flexible parametric Accelerated Hazards (AH) regression models in overall and relative survival frameworks with 13 distinct Baseline Distributions. The AH Model can also be applied to lifetime data with crossed survival curves. Any user-defined parametric distribution can be fitted, given at least an R function defining the cumulative hazard and hazard rate functions. See Chen and Wang (2000) <doi:10.1080/01621459.2000.10474236>, and Lee (2015) <doi:10.1007/s10985-015-9349-5> for more details.
Estimates continuous time weights for performing causal survival
             analysis. For instance, weighted Nelson-Aalen or Kaplan-Meier
             estimates can be given a causal interpretation. See Ryalen, Stensrud, 
             and Røysland (2019) <doi:10.1007/s10985-019-09468-y> and Ryalen (2019)
             <https://www.duo.uio.no/handle/10852/70353>
             for theory and examples.
A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) <doi:10.1016/j.acags.2020.100026>),  compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <doi:10.1007/BF00891269>) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages 'ALDEx2', 'edgeR' and 'DESeq2' (Fernandes et al (2014) <doi:10.1186/2049-2618-2-15>, Anders et al. (2013)<doi:10.1038/nprot.2013.099>).
Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes.  The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes.  The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities.  Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs', 'rjags', and 'jagsUI' classes.  Functions also implement model selection using BIC.  Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.
Provides tools for model selection and model averaging of PerMANOVA 
    models using Akaike Information Criterion corrected for small sample sizes 
    (AICc) and Information Theoretic criteria principles. The package is built 
    around the PERMANOVA analysis from the 'vegan' package and provides a 
    streamlined workflow for generating and comparing models, obtaining model 
    weights, and summarizing results using model averaging approaches.  The 
    methods implemented in this package are based on the practical information-
    theoretic approach described by Burnham, K. P. and Anderson, D. R. (2002) 
    (<doi:10.1007/b97636>).
Performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data.
Read objects from the AIDA (<http://aida.freehep.org/>) file and make them available as dataframes in R.
A supportive collection of functions for pooled analysis of aggregate data. The current version supports users to test assumptions before relevant analysis of bias from study size and sequential analysis such as mentioned by Wetterslev, J., Jakobsen, J. C., & Gluud, C. (2017) <doi:10.1186/s12874-017-0315-7>.
In social and educational settings, the use of Artificial
    Intelligence (AI) is a challenging task. Relevant data is often only
    available in handwritten forms, or the use of data is restricted by
    privacy policies. This often leads to small data sets. Furthermore, in the educational and social sciences,
    data is often unbalanced in terms of
    frequencies. To support educators as well as educational and social
    researchers in using the potentials of AI for their work, this package
    provides a unified interface for neural nets in 'keras',
    'tensorflow', and 'pytorch' to deal with natural language problems. In addition,
    the package ships with a shiny app, providing a graphical user interface.
    This allows the usage of AI for people without skills in writing python/R scripts.
    The tools integrate existing mathematical and statistical methods for dealing
    with small data sets via pseudo-labeling (e.g. Lee (2013)
    <https://www.researchgate.net/publication/280581078_Pseudo-Label_The_Simple_and_Efficient_Semi-Supervised_Learning_Method_for_Deep_Neural_Networks>,
    Cascante-Bonilla et al. (2020) <doi:10.48550/arXiv.2001.06001>) and
    imbalanced data via the creation of synthetic cases (e.g.
    Bunkhumpornpat et al. (2012) <doi:10.1007/s10489-011-0287-y>).
    Performance evaluation of AI is connected to measures from content
    analysis which educational and social researchers are generally more
    familiar with (e.g. Berding & Pargmann (2022) <doi:10.30819/5581>,
    Gwet (2014) <ISBN:978-0-9708062-8-4>, Krippendorff (2019)
    <doi:10.4135/9781071878781>). Estimation of energy consumption and CO2
    emissions during model training is done with the 'python' library
    'codecarbon'.  Finally, all objects created with this package allow to
    share trained AI models with other people.
Provides statistical methods for analyzing experimental 
    evaluation of the causal impacts of algorithmic recommendations 
    on human decisions developed by Imai, Jiang, Greiner, Halen, and 
    Shin (2023) <doi:10.1093/jrsssa/qnad010>.
    The data used for this paper, and made available here, are interim, 
    based on only half of the observations in the study and (for those 
    observations) only half of the study follow-up period. We use them 
    only to illustrate methods, not to draw substantive conclusions.
Create a pie like plot to visualise if the aim or several aims of a
             project is achieved or close to be achieved i.e the aim is achieved when the point is at the
             center of the pie plot. Imagine it's like a dartboard and the center means 100%
             completeness/achievement. Achievement can also be understood as 100%
             coverage. The standard distribution of completeness allocated in the pie plot
             is 50%, 80% and 100% completeness.
Named after the Irish name for weather, this package contains 
    tidied data from the Irish Meteorological Service's hourly observations for 2017. 
    In all, the data sets include observations from 25 weather stations, and also
    latitude and longitude coordinates for each weather station. Now includes energy 
    generation data for Ireland and Northern Ireland (2017), including Wind Generation data.
A toolkit for archaeological time series. This package
    provides a system of classes and methods to represent and work with
    archaeological time series. Dates are represented as "rata die" and
    can be converted to (virtually) any calendar defined by Reingold and
    Dershowitz (2018) <doi:10.1017/9781107415058>. This packages offers a
    simple API that can be used by other specialized packages.
The 'AIPW' pacakge implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the 'AIPW' package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021, In Press). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology". Visit: <https://yqzhong7.github.io/AIPW/> for more information.
An R console utility that lets you ask R related questions to the
    'OpenAI' large language model. It can answer 'how-to()' questions by  
    providing code, and 'whatis()' questions by explaining what given code does.
    You must provision your own key for the 'OpenAI' API 
    <https://platform.openai.com/docs/api-reference>.
Tools for downloading hourly averages, daily maximums and
    minimums from each of the pollution, wind, and temperature measuring
    stations or geographic zones in the Mexico City metro area. The
    package also includes the locations of each of the stations and zones.
    See <http://aire.cdmx.gob.mx/> for more information.
Hydrological modelling tools developed at INRAE-Antony (HYCAR Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR5H, GR4J, GR5J, GR6J, GR2M, GR1A) that can be applied either on a lumped or semi-distributed way. A snow accumulation and melt model (CemaNeige) and the associated functions for the calibration and evaluation of models are also included. Use help(airGR) for package description and references.
Sample of hydro-meteorological datasets extracted from the 'CAMELS-FR' French database <https://hal.inrae.fr/hal-03687235>. It provides metadata and catchment-scale aggregated hydro-meteorological time series on a pool of French catchments for use by the 'airGR' packages.
Add-on to the 'airGR' package which provides the tools to assimilate observed discharges in daily GR hydrological models. The package consists in two functions allowing to perform the assimilation of observed discharges via the Ensemble Kalman filter or the Particle filter as described in Piazzi et al. (2021) <doi:10.1029/2020WR028390>.
Semi-distributed Precipitation-Runoff Modelling based on
    'airGR' package models integrating human infrastructures and their
    managements.
Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables.
Get information about air quality using 'Airly' <https://airly.eu/> API through R.
Utilities for working with hourly air quality monitoring data
    with a focus on small particulates (PM2.5). A 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".
    Algorithms to calculate NowCast and the associated Air Quality Index (AQI)
    are defined at the US Environmental Projection Agency AirNow program:
    <https://www.airnow.gov/sites/default/files/2020-05/aqi-technical-assistance-document-sept2018.pdf>.
Retrieve air quality data via the 'AirNow'
    <https://www.airnow.gov/> API.
Retrieves open source airport data and provides tools to look up information, translate names into codes and vice-verse, as well as some basic calculation functions for measuring distances. Data is licensed under the Open Database License.
Geographic, use, and property related data on airports.
These dataset contains daily quality air measurements in 
  Spain over a period of 18 years (from 2001 to 2018). The measurements refer to 
  several pollutants. These data are openly published by the Government of Spain.  
  The datasets were originally spread over a number of files and formats. Here, 
  the same information is contained in simple dataframe for convenience of 
  researches, journalists or general public. See the Spanish Government website 
  <http://www.miteco.gob.es/> for more information.
Schema definitions and read, write and validation tools for data 
    formatted in accordance with the AIRR Data Representation schemas defined 
    by the AIRR Community <http://docs.airr-community.org>.
An evaluation framework for algorithm portfolios using Item Response
    Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce
    algorithm characteristics such as stability, effectiveness and anomalousness 
    (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.
Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics.
Nonparametric estimation of additive isotonic covariate effects for proportional hazards model.
Uncertainty quantification and inverse estimation by probabilistic generative models  from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <arXiv:2309.02468>.
A thin wrapper around the 'ajv' JSON validation package for
    JavaScript. See <http://epoberezkin.github.io/ajv/> for details.
A tidy framework for automatic knowledge classification and visualization. Currently, the core functionality of the framework is mainly supported by modularity-based clustering (community detection) in keyword co-occurrence network, and focuses on co-word analysis of bibliometric research. However, the designed functions in 'akc' are general, and could be extended to solve other tasks in text mining as well.
Continuous and discrete (count or categorical) estimation of density, probability mass function (p.m.f.) and regression functions are performed using  associated kernels. The cross-validation  technique and the local Bayesian procedure are also implemented for bandwidth selection.
Flagger to detect acute kidney injury (AKI) in a patient dataset.
Several cubic spline interpolation methods of H. Akima for irregular and
  regular gridded data are available through this package, both for the bivariate case
  (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697).
  Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas
  triangulation code which is part of Akimas Fortran code. A bilinear interpolator
  for regular grids was also added for comparison with the bicubic interpolator on
  regular grids.
Implements the alternating k-means biclustering algorithm in Fraiman and Li (2020) <arXiv:2009.04550>.
Augmented Lagrangian Adaptive Barrier Minimization
        Algorithm for optimizing smooth nonlinear objective functions
        with constraints. Linear or nonlinear equality and inequality
        constraints are allowed.
Provides methods for high-throughput adaptive immune 
    receptor repertoire sequencing (AIRR-Seq; Rep-Seq) analysis. In 
    particular, immunoglobulin (Ig) sequence lineage reconstruction, 
    lineage topology analysis, diversity profiling, amino acid property 
    analysis and gene usage.
    Citations: 
    Gupta and Vander Heiden, et al (2017) <doi:10.1093/bioinformatics/btv359>,
    Stern, Yaari and Vander Heiden, et al (2014) <doi:10.1126/scitranslmed.3008879>.
Utility functions to download and process data produced by the ALARM Project,
    including 2020 redistricting files Kenny and McCartan (2021) 
    <https://alarm-redist.org/posts/2021-08-10-census-2020/> and the 50-State 
    Redistricting Simulations of McCartan, Kenny, Simko, Garcia, Wang, Wu, 
    Kuriwaki, and Imai (2022) <doi:10.7910/DVN/SLCD3E>. The package extends 
    the data introduced in McCartan, Kenny, Simko, Garcia, Wang, Wu, Kuriwaki,
    and Imai (2022) <doi:10.1038/s41597-022-01808-2> to also include states with 
    only a single district.
Penalized variable selection tools for the Cox 
    proportional hazards model with interval censored and possibly 
    left truncated data. It performs variable selection via 
    penalized nonparametric maximum likelihood estimation with an 
    adaptive lasso penalty. The optimal thresholding parameter can be 
    searched by the package based on the profile Bayesian information 
    criterion (BIC). The asymptotic validity of the methodology is 
    established in Li et al. (2019 <doi:10.1177/0962280219856238>). 
    The unpenalized nonparametric maximum likelihood estimation for 
    interval censored and possibly left truncated data is also 
    available.
Perform parallel factor analysis (PARAFAC: Hitchcock, 1927)
	<doi:10.1002/sapm192761164> on fluorescence excitation-emission
	matrices: handle scattering signal and inner filter effect, scale
	the dataset, fit the model; perform split-half validation or
	jack-knifing. Modified approaches such as Whittaker interpolation,
	randomised split-half, and fluorescence and scattering model
	estimation are also available. The package has a low dependency
	footprint and has been tested on a wide range of R versions.
Implements discrete time deterministic and stochastic age-structured population dynamics models described in Erguler and others (2016) <doi:10.1371/journal.pone.0149282> and Erguler and others (2017) <doi:10.1371/journal.pone.0174293>.
It provides the density, distribution function, quantile function, 
             random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for
             the three parameter Asymmetric Laplace Distribution defined 
             in Koenker and Machado (1999). This is a special case of the skewed family of distributions
             available in Galarza et.al. (2017) <doi:10.1002/sta4.140> useful for quantile regression.
EM algorithm for estimation of parameters and other methods in a quantile regression.
The goal of the package 'aldvmm' is to fit adjusted limited 
    dependent variable mixture models of health state utilities. Adjusted 
    limited dependent variable mixture models are finite mixtures of normal 
    distributions with an accumulation of density mass at the limits, and a gap 
    between 100% quality of life and the next smaller utility value. The 
    package 'aldvmm' uses the likelihood and expected value functions proposed 
    by Hernandez Alava and Wailoo (2015) <doi:10.1177/1536867X1501500307> using 
    normal component distributions and a multinomial logit model of 
    probabilities of component membership.
Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. As such, ALE values are not affected by the presence or absence of interactions among variables in a mode. Moreover, its computation is relatively rapid. This package rewrites the original code from the 'ALEPlot' package for calculating ALE data and it completely reimplements the plotting of ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference. For more details, see Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. <arXiv:2310.09877>. <doi:10.48550/arXiv.2310.09877>.
Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models. The package creates either Accumulated Local Effects (ALE) plots and/or Partial Dependence (PD) plots, given a fitted supervised learning model.
Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample.
Allows you to connect to an 'Alfresco' content management repository and interact
  with its contents using simple and intuitive functions.  You will be able to establish a connection session to the 'Alfresco' repository,
  read and upload content and manage folder hierarchies.  For more details on the 'Alfresco' content management repository
  see <https://www.alfresco.com/ecm-software/document-management>.
Provides direct access to the ALFRED (<https://alfred.stlouisfed.org>) and FRED (<https://fred.stlouisfed.org>) databases.
    Its functions return tidy data frames for different releases of the specified time series. 
    Note that this product uses the FRED© API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.
Algorithmic experimental designs. Calculates exact and
        approximate theory experimental designs for D,A, and I
        criteria. Very large designs may be created. Experimental
        designs may be blocked or blocked designs created from a
        candidate list, using several criteria.  The blocking can be
        done when whole and within plot factors interact.
Two unordered pairs of data of two different snips positions is haplotyped by resolving a small number ob closed equations.
Allows the user to implement an address search auto completion menu on 'shiny' text inputs.
    This is done using the 'Algolia Places' 'JavaScript' library. See <https://community.algolia.com/places/>.
The company, Algorithmia, houses the largest marketplace of online
    algorithms. This package essentially holds a bunch of REST wrappers that
    make it very easy to call algorithms in the Algorithmia platform and access
    files and directories in the Algorithmia data API. To learn more about the
    services they offer and the algorithms in the platform visit
    <http://algorithmia.com>. More information for developers can be found at
    <https://algorithmia.com/developers>.
Queries multiple resources authors HGNC (2019) <https://www.genenames.org>, authors limma (2015) <doi:10.1093/nar/gkv007> 
    to find the correspondence between evolving nomenclature of human gene symbols, aliases, previous symbols or synonyms with 
    stable, curated gene entrezID from NCBI database. This allows fast, accurate and up-to-date correspondence
    between human gene expression datasets from various date and platform (e.g: gene symbol: BRCA1 - ID: 672).
Easily estimate the introduction rates of alien species given first records data.
    It specializes in addressing the role of sampling on the pattern of discoveries, 
    thus providing better estimates than using Generalized Linear Models which assume perfect 
    immediate detection of newly introduced species.
A dynamic time warping (DTW) algorithm for stratigraphic alignment,
    translated into R from the original published 'MATLAB' code by Hay et al. (2019)
    <doi:10.1130/G46019.1>. The DTW algorithm incorporates two geologically relevant
    parameters (g and edge) for augmenting the typical DTW cost matrix, allowing
    for a range of sedimentologic and chronologic conditions to be explored, as 
    well as the generation of an alignment library (as opposed to a single alignment
    solution). The g parameter relates to the relative sediment accumulation rate
    between the two time series records,  while the edge parameter relates to the 
    amount of total shared time between the records. Note that this algorithm is
    used for all DTW alignments in the Align Shiny application, detailed in Hagen
    et al. (in review).
A lightweight, dependency-free toolbox for pre-processing XY
    data from experimental methods (i.e. any signal that can be measured
    along a continuous variable). This package provides methods for
    baseline estimation and correction, smoothing, normalization,
    integration and peaks detection. Baseline correction methods includes
    polynomial fitting as described in Lieber and Mahadevan-Jansen (2003)
    <doi:10.1366/000370203322554518>, Rolling Ball algorithm after Kneen
    and Annegarn (1996) <doi:10.1016/0168-583X(95)00908-6>, SNIP algorithm
    after Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, 4S Peak
    Filling after Liland (2015) <doi:10.1016/j.mex.2015.02.009> and more.
Acknowledge all contributors to a project via a single
    function call. The function appends to a 'README' or other specified
    file(s) a table with names of all individuals who contributed via code
    or repository issues.  The package also includes several additional
    functions to extract and quantify contributions to any repository.
Tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker,
    probability and proportion of missing genotypes, recombination rate, etc).
    Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried
    out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding
    haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited
    from each parent) and thus imputation and reconstruction results can be deterministically calculated.
Tools for the identification of unique of multilocus genotypes when both genotyping error and missing data may be present; targeted for use with large datasets and databases containing multiple samples of each individual (a common situation in conservation genetics, particularly in non-invasive wildlife sampling applications). Functions explicitly incorporate missing data and can tolerate allele mismatches created by genotyping error. If you use this package, please cite the original publication in Molecular Ecology Resources (Galpern et al., 2012), the details for which can be generated using citation('allelematch'). For a complete vignette, please access via the Data S1 Supplementary documentation and tutorials (PDF) located at <doi:10.1111/j.1755-0998.2012.03137.x>.
Simulate the effect of management or demography on allele retention and inbreeding accumulation in bottlenecked populations of animals with overlapping generations.
Methods (<doi:10.7717/peerj.11534>) are provided of calibrating and predicting shifts in allele frequencies through redundancy analysis ('vegan::rda()') and generalized additive models ('mgcv::gam()'). Visualization functions for predicted changes in allele frequencies include 'shift.dot.ggplot()', 'shift.pie.ggplot()', 'shift.moon.ggplot()', 'shift.waffle.ggplot()' and 'shift.surf.ggplot()' that are made with input data sets that are prepared by helper functions for each visualization method. Examples in the documentation show how to prepare animated climate change graphics through a time series with the 'gganimate' package. Function 'amova.rda()' shows how Analysis of Molecular Variance can be directly conducted with the results from redundancy analysis.
Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, O’Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2022) "An allelic series rare variant association test for candidate gene discovery" <doi:10.1101/2022.12.23.521658>.
Estimates and plots effect estimates from models with all possible 
    combinations of a list of variables. It can be used for assessing treatment 
    effects in clinical trials or risk factors in bio-medical and epidemiological 
    research. Like Stata command 'confall' (Wang Z (2007) <doi:10.1177/1536867X0700700203> ), 
    'allestimates' calculates and stores all effect estimates, and plots them against p values or 
    Akaike information criterion (AIC) values. It currently has functions for linear 
    regression: all_lm(), logistic and Poisson regression: all_glm(), 
    and Cox proportional hazards regression: all_cox().
Provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models' performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmott’s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202> and Garai et al. (2024) <doi:10.1007/s11063-024-11552-w>.
Evaluates acute lymphoblastic leukemia maintenance therapy practice at patient and cohort level.
Streamline use of the 'All of Us' Researcher Workbench (<https://www.researchallofus.org/data-tools/workbench/>)with tools to extract and manipulate data from the 'All of Us' database. Increase interoperability with the Observational Health Data Science and Informatics ('OHDSI') tool stack by decreasing reliance of 'All of Us' tools and allowing for cohort creation via 'Atlas'. Improve reproducible and transparent research using 'All of Us'.
Access allometric models used in forest resource analysis, such as
    volume equations, taper equations, biomass models, among many others. Users
    are able to efficiently find and select allometric models suitable for their
    project area and use them in analysis. Additionally, 'allometric' provides a
    structured framework for adding new models to an open-source models
    repository.
Implementation of the technique of Lleonart et al. 
    (2000) <doi:10.1006/jtbi.2000.2043> to scale body measurements 
    that exhibit an allometric growth. This procedure is a theoretical 
    generalization of the technique used by Thorpe 
    (1975) <doi:10.1111/j.1095-8312.1975.tb00732.x> and
    Thorpe (1976) <doi:10.1111/j.1469-185X.1976.tb01063.x>.
We developed a lightweight machine learning tool for RNA profiling of acute lymphoblastic leukemia (ALL), however, it can be used for any problem where multiple classes need to be identified from multi-dimensional data. The methodology is described in Makinen V-P, Rehn J, Breen J, Yeung D, White DL (2022) Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia, International Journal of Molecular Sciences 23:4574, <doi:10.3390/ijms23094574>. The classifier contains optimized mean profiles of the classes (centroids) as observed in the training data, and new samples are matched to these centroids using the shortest Euclidean distance. Centroids derived from a dataset of 1,598 ALL patients are included, but users can train the models with their own data as well. The output includes both numerical and visual presentations of the classification results. Samples with mixed features from multiple classes or atypical values are also identified.
Creating alluvial diagrams (also known as parallel sets plots) for multivariate
  and time series-like data.
Provides tools for defining recurrence rules and recurrence
    sets. Recurrence rules are a programmatic way to define a recurring
    event, like the first Monday of December. Multiple recurrence rules
    can be combined into larger recurrence sets. A full holiday and
    calendar interface is also provided that can generate holidays within
    a particular year, can detect if a date is a holiday, can respect
    holiday observance rules, and allows for custom holidays.
A collection of datasets on the Alone survival TV series in tidy format. 
  Included in the package are 4 datasets detailing the survivors, their loadouts,
  episode details and season information.
A collection of tools that support data splitting, predictive modeling, and model evaluation. 
    A typical function is to split a dataset into a training dataset and a test dataset. 
    Then compare the data distribution of the two datasets.
    Another feature is to support the development of predictive models and to compare the performance of several predictive models, 
    helping to select the best model.
Creates all leave-one-out models and produces predictions for test samples.
Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <arXiv:1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <arXiv:2004.12655>.
Provides the alpha-adjustment correction from "Benjamini, Y., & Hochberg, Y. (1995) <doi:10.1111/j.2517-6161.1995.tb02031.x> Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), 289-300". For researchers interested in using the exact mathematical formulas and procedures as used in the original paper.
Provides algorithms for frequency-based pairing of alpha-beta T
  cell receptors.
Calculate confidence intervals for alpha and standardized
    alpha using asymptotic theory or the studentized bootstrap, with or
    without transformations. Supports the asymptotic distribution-free
    method of Maydeu-Olivares, et al. (2007)
    <doi:10.1037/1082-989X.12.2.157>, the pseudo-elliptical method of Yuan
    & Bentler (2002) <doi:10.1007/BF02294845>, and the normal method of
    van Zyl et al. (1999) <doi:10.1007/BF02296146>, for both coefficient
    alpha and standardized alpha.
Computation of the alpha-shape and alpha-convex
        hull of a given sample of points in the plane. The concepts of
        alpha-shape and alpha-convex hull generalize the definition of
        the convex hull of a finite set of points. The programming is
        based on the duality between the Voronoi diagram and Delaunay
        triangulation. The package also includes a function that
        returns the Delaunay mesh of a given sample of points and its
        dual Voronoi diagram in one single object.
Sets the alpha level for coefficients in a regression model
    as a decreasing function of the sample size through the use of
    Jeffreys' Approximate Bayes factor. You tell alphaN() your sample
    size, and it tells you to which value you must lower alpha to avoid
    Lindley's Paradox. For details, see Wulff and Taylor (2023)
    <doi:10.31234/osf.io/3cbh7>.
Given the parameters of a distribution, the package uses the concept of alpha-outliers by Davies and Gather (1993) to flag outliers in a data set. See Davies, L.; Gather, U. (1993): The identification of multiple outliers, JASA, 88 423, 782-792, <doi:10.1080/01621459.1993.10476339> for details.
A software that implements a method for partitioning genetic trends to
    quantify the sources of genetic gain in breeding programmes.
    The partitioning method is described in Garcia-Cortes et al.
    (2008) <doi:10.1017/S175173110800205X>. The package includes the
    main function AlphaPart for partitioning breeding values and auxiliary
    functions for manipulating data and summarizing, visualizing, and saving
    results.
Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set.
The successor to the 'AlphaSim' software for breeding program 
  simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. 
  Used for stochastic simulations of breeding programs to the level of DNA 
  sequence for every individual. Contained is a wide range of functions for 
  modeling common tasks in a breeding program, such as selection and crossing. 
  These functions allow for constructing simulations of highly complex plant and 
  animal breeding programs via scripting in the R software environment. Such 
  simulations can be used to evaluate overall breeding program performance and 
  conduct research into breeding program design, such as implementation of 
  genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') 
  for fast simulation of biallelic sequences according to a population 
  demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].
Developed to perform the tasks given by the following. 1-computing the probability density function and distribution function of a univariate stable distribution; 2- generating from univariate stable, truncated stable, multivariate elliptically contoured stable, and bivariate strictly stable distributions; 3- estimating the parameters of univariate symmetric stable, skew stable, Cauchy, multivariate elliptically contoured stable, and multivariate strictly stable distributions; 4- estimating
             the parameters of the mixture of symmetric stable and mixture of Cauchy distributions.
Alpha Vantage has free historical financial information. 
    All you need to do is get a free API key at <https://www.alphavantage.co>.
    Then you can use the R interface to retrieve free equity information.
    Refer to the Alpha Vantage website for more information.
Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available.
Datasets to Accompany S. Weisberg (2014, ISBN: 978-1-118-38608-8), 
 "Applied Linear Regression," 4th edition.  Many data files 
 in this package are included in the `alr3` package as well, so only one of them 
 should be used.
Alternating least squares is often used to resolve
        components contributing to data with a bilinear structure; the
        basic technique may be extended to alternating constrained
        least squares.  Commonly applied constraints include
        unimodality, non-negativity, and normalization of components.
        Several data matrices may be decomposed simultaneously by
        assuming that one of the two matrices in the bilinear
        decomposition is shared between datasets.
Using of the accelerated line search algorithm  for simultaneously diagonalize a set of symmetric positive definite matrices.
Functions for interacting directly with the 'ALTADATA' API. With this R package, developers can build applications around the 'ALTADATA' API without having to deal with accessing and managing requests and responses. 'ALTADATA' is a curated data marketplace for more information go to <https://www.altadata.io>.
Interface to 'Altair' <https://altair-viz.github.io>, which itself 
  is a 'Python' interface to 'Vega-Lite' <https://vega.github.io/vega-lite/>.
  This package uses the 'Reticulate' framework 
  <https://rstudio.github.io/reticulate/> to manage the interface between R
  and 'Python'.
This is a simple and powerful package to create, render, preview, 
    and deploy documentation websites for 'R' packages. It is a lightweight and 
    flexible alternative to 'pkgdown', with support for many documentation 
    generators, including 'Quarto', 'Docute', 'Docsify', and 'MkDocs'.
Inference of protein complex states from quantitative proteomics data. The package takes information on known stable protein interactions (i.e. protein components of the same complex) and assesses how protein quantitative ratios change between different conditions. It reports protein pairs for which relative protein quantities to each other have been significantly altered in the tested condition.
Provides a number of functions to access the 
    National Energy Research Laboratory Alternate Fuel Locator API 
    <https://developer.nrel.gov/docs/transportation/alt-fuel-stations-v1/>. 
    The Alternate Fuel Locator shows the location of alternate fuel stations in the United States 
    and Canada. This package also includes the data from the US Department of Energy Alternate Fuel
    database as a data set.
Provides alternative statistical methods for meta-analysis, including:
 - bivariate generalized linear mixed models for synthesizing odds ratios, relative risks,
   and risk differences
   (Chu et al., 2012 <doi:10.1177/0962280210393712>)
 - heterogeneity tests and measures and penalization methods that are robust to outliers
   (Lin et al., 2017 <doi:10.1111/biom.12543>;
    Wang et al., 2022 <doi:10.1002/sim.9261>);
 - measures, tests, and visualization tools for publication bias or small-study effects
   (Lin and Chu, 2018 <doi:10.1111/biom.12817>;
    Lin, 2019 <doi:10.1002/jrsm.1340>;
    Lin, 2020 <doi:10.1177/0962280220910172>;
    Shi et al., 2020 <doi:10.1002/jrsm.1415>);
 - meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc.
   (Reitsma et al., 2005 <doi:10.1016/j.jclinepi.2005.02.022>;
    Chu and Cole, 2006 <doi:10.1016/j.jclinepi.2006.06.011>);
 - meta-analysis methods for synthesizing proportions
   (Lin and Chu, 2020 <doi:10.1097/ede.0000000000001232>);
 - models for multivariate meta-analysis, measures of inconsistency degrees of freedom
   in Bayesian network meta-analysis, and predictive P-score
   (Lin and Chu, 2018 <doi:10.1002/jrsm.1293>;
    Lin, 2020 <doi:10.1080/10543406.2020.1852247>;
    Rosenberger et al., 2021 <doi:10.1186/s12874-021-01397-5>).
Creates the optimal (D, U and I) designs for the accelerated life
    testing with right censoring or interval censoring. It uses generalized 
    linear model (GLM) approach to derive the asymptotic variance-covariance 
    matrix of regression coefficients. The failure time distribution is assumed 
    to follow Weibull distribution with a known shape parameter and log-linear 
    link functions are used to model the relationship between failure time 
    parameters and stress variables. The acceleration model may have multiple 
    stress factors, although most ALTs involve only two or less stress factors. 
    ALTopt package also provides several plotting functions including contour plot,
    Fraction of Use Space (FUS) plot and Variance Dispersion graphs of Use Space
    (VDUS) plot. For more details, see Seo and Pan (2015) <doi:10.32614/RJ-2015-029>.
Provides alternatives to the normal adjusted R-squared estimator for the estimation of the multiple squared correlation in regression models, 
              as fitted by the lm() function. The alternative estimators are described in Karch (2016) <DOI:10.31234/osf.io/v8dz5>.
Evaluates land suitability for different crops production. 
  The package is based on the Food and Agriculture Organization (FAO) and the 
  International Rice Research Institute (IRRI) methodology for land evaluation. 
  Development of ALUES is inspired by similar tool for land evaluation, Land Use 
  Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate 
  land suitability of a particular area based on inputs such as rainfall, temperature, 
  topography, and soil properties. The membership functions used for fuzzy modeling are 
  the following: Triangular, Trapezoidal and Gaussian. The methods for computing the 
  overall suitability of a particular area are also included, and these are the Minimum, 
  Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms 
  written in C++.
Combination of results for meta-analysis using significance
    and effect size only. P-values and fold-change are combined to obtain
    a global significance on each metabolite. Produces a volcano plot
    summarising the relevant results from meta-analysis. Vote-counting
    reports for metabolites. And explore plot to detect discrepancies
    between studies at a first glance. Methodology is described in the
    Llambrich et al. (2021) <doi:10.1093/bioinformatics/btab591>.
Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal 
    Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides
    a novel algorithm for solving the sparse principal component analysis problem which provides
    advantages over existing methods in terms of efficiency and convergence guarantees.
    Chen, S., Ma, S., Xue, L., & Zou, H. (2020) <doi:10.1287/ijoo.2019.0032>.
    Zou, H., Hastie, T., & Tibshirani, R. (2006) <doi:10.1198/106186006X113430>.
    Zou, H., & Xue, L. (2018) <doi:10.1109/JPROC.2018.2846588>.
Tools for Clustering and Principal Component Analysis
        (With robust methods, and parallelized functions).
Getting and parsing data of location geocode/reverse-geocode and administrative regions from 'AutoNavi Maps'<https://lbs.amap.com/api/webservice/summary> API.
Build and control interactive 2D and 3D maps with 'R/Shiny'.  Lean set of powerful commands wrapping native calls to 'AMap' <https://lbs.amap.com/api/jsapi-v2/summary/>.
    Deliver rich mapping functionality with minimal overhead.
Read, manipulate and write voxel spaces. Voxel spaces are
    read from text-based output files of the 'AMAPVox' software. 'AMAPVox'
    is a LiDAR point cloud voxelisation software that aims at estimating
    leaf area through several theoretical/numerical approaches. See more
    in the article Vincent et al. (2017) <doi:10.23708/1AJNMP> and the
    technical note Vincent et al. (2021) <doi:10.23708/1AJNMP>.
Collect  your data on digital marketing campaigns from Amazon Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Collect  your data on digital marketing campaigns from Amazon S3 using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Collect  your data on digital marketing campaigns from Amazon Sp using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
'Amber' is a server application for capturing electronic data records.
    Rich forms are used to collect data. This 'Amber' client allows
    to perform data extraction for reporting or data transfer at persistent location
    purposes.
Generation of natural looking noise has many application within 
    simulation, procedural generation, and art, to name a few. The 'ambient' 
    package provides an interface to the 'FastNoise' C++ library and allows for
    efficient generation of perlin, simplex, worley, cubic, value, and white 
    noise with optional perturbation in either 2, 3, or 4 (in case of simplex and
    white noise) dimensions.
Simulation and estimation tools for 
    various types of ambit processes, including trawl processes and weighted
    trawl processes.
Accompanies "Designing experiments and 
    analyzing data: A model comparison perspective" (3rd ed.) by 
    Maxwell, Delaney, & Kelley (2018; Routledge). 
    Contains all of the data sets in the book's chapters and 
    end-of-chapter exercises. Information about the book is available at 
    <http://www.DesigningExperiments.com>.
Generate code for use with the Optical Mark Recognition free software Auto Multiple Choice (AMC). More specifically, this package provides functions that use as input the question and answer texts, and output the LaTeX code for AMC.
A tool that "multiply imputes" missing data in a single cross-section
  (such as a survey), from a time series (like variables collected for
  each year in a country), or from a time-series-cross-sectional data
  set (such as collected by years for each of several countries).
  Amelia II implements our bootstrapping-based algorithm that gives
  essentially the same answers as the standard IP or EMis approaches,
  is usually considerably faster than existing approaches and can
  handle many more variables.  Unlike Amelia I and other statistically
  rigorous imputation software, it virtually never crashes (but please
  let us know if you find to the contrary!).  The program also
  generalizes existing approaches by allowing for trends in time series
  across observations within a cross-sectional unit, as well as priors
  that allow experts to incorporate beliefs they have about the values
  of missing cells in their data.  Amelia II also includes useful
  diagnostics of the fit of multiple imputation models.  The program
  works from the R command line or via a graphical user interface that
  does not require users to know R.
Implements anomaly detection as binary classification for cross-sectional data. Uses maximum likelihood estimates and normal probability functions to classify observations as anomalous. The method is presented in the following lecture from the Machine Learning course by Andrew Ng: <https://www.coursera.org/learn/machine-learning/lecture/C8IJp/algorithm/>, and is also described in: Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava (2003) <doi:10.1137/1.9781611972733.3>.
Analysis of dyadic network and relational data using additive and
    multiplicative effects (AME) models. The basic model includes
    regression terms, the covariance structure of the social relations model
    (Warner, Kenny and Stoto (1979) <DOI:10.1037/0022-3514.37.10.1742>, 
    Wong (1982) <DOI:10.2307/2287296>), and multiplicative factor
    models (Hoff(2009) <DOI:10.1007/s10588-008-9040-4>). 
    Several different link functions accommodate different
    relational data structures, including binary/network data, normal
    relational data, zero-inflated positive outcomes using a tobit model, ordinal relational data and data from
    fixed-rank nomination schemes. Several of these link functions are
    discussed in Hoff, Fosdick, Volfovsky and Stovel (2013) 
    <DOI:10.1017/nws.2013.17>. Development of this
    software was supported in part by NIH grant R01HD067509.
Programmatic interface to the 'AmeriFlux' database
    (<https://ameriflux.lbl.gov/>). Provide query, download,
    and data summary tools.
A color palette generator inspired by American politics, with colors ranging from blue on the 
    left to gray in the middle and red on the right. A variety of palettes allow for a range of applications 
    from brief discrete scales (e.g., three colors for Democrats, Independents, and Republicans) to 
    continuous interpolated arrays including dozens of shades graded from blue (left) to red (right). This
    package greatly benefitted from building on the source code (with permission) from Ram and Wickham (2015).
Raw and processed versions of the data from De Cock (2011) <http://ww2.amstat.org/publications/jse> are included in the package.
A collection of lightweight functions that can be
    used to determine the computing environment in which your code is
    running. This includes operating systems, continuous integration (CI)
    environments, containers, and more.
Modern software often poorly support older file formats. This
    package intends to handle many file formats that were native to the
    antiquated Commodore Amiga machine. This package focuses on file types from
    the older Amiga operating systems (<= 3.0). It will read and write specific
    file formats and coerces them into more contemporary data.
Fast tool to calculate the Adjusted Market Inefficiency Measure following Tran & Leirvik (2019) <doi:10.1016/j.frl.2019.03.004>. This tool provides rolling window estimates of the Adjusted Market Inefficiency Measure for multiple instruments simultaneously.
Flexible multi-environment trials analysis via MCMC method for Additive Main Effects and Multiplicative Model (AMMI) for continuous data. 
 Biplot with the averages and regions of confidence can be generated. The chains run in parallel on Linux systems and run serially on Windows.
Computes various stability parameters from Additive Main Effects
    and Multiplicative Interaction (AMMI) analysis results such as  Modified
    AMMI Stability Value (MASV), Sums of the Absolute Value of the Interaction
    Principal Component Scores (SIPC), Sum Across Environments of
    Genotype-Environment Interaction Modelled by AMMI (AMGE), Sum Across 
    Environments of Absolute Value of Genotype-Environment Interaction Modelled
    by AMMI (AV_(AMGE)), AMMI Stability Index (ASI), Modified ASI (MASI), AMMI
    Based Stability Parameter (ASTAB), Annicchiarico's D Parameter (DA), Zhang's
    D Parameter (DZ), Averages of the Squared Eigenvector Values (EV), Stability
    Measure Based on Fitted AMMI Model (FA), Absolute Value of the Relative
    Contribution of IPCs to the Interaction (Za). Further calculates the
    Simultaneous Selection Index for Yield and Stability from the computed
    stability parameters. See the vignette for complete list of citations for
    the methods implemented.
Helps enable adaptive management by codifying knowledge in the
    form of models generated from numerous analyses and data sets. Facilitates
    this process by storing all models and data sets in a single object that can
    be updated and saved, thus tracking changes in knowledge through time. A shiny
    application called AM Model Manager (modelMgr()) enables the use of these
    functions via a GUI.
Provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values.
Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) <doi:10.1016/j.addbeh.2018.10.031>. This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential  item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment.
Fits tractable fully parametric odds-based regression models for survival data, including proportional odds (PO), accelerated failure time (AFT), accelerated odds (AO), and General Odds (GO) models in overall survival frameworks. Given at least an R function specifying the survivor, hazard rate and cumulative distribution functions, any user-defined parametric distribution can be fitted. We applied and evaluated a minimum of seventeen (17) various baseline distributions that can handle different failure rate shapes for each of the four different proposed odds-based regression models. For more information see Bennet et al., (1983) <doi:10.1002/sim.4780020223>, and Muse et al., (2022) <doi:10.1016/j.aej.2022.01.033>.
A testing framework for testing the multivariate point null hypothesis. 
    A testing framework described in Elder et al. (2022) <arXiv:2203.01897> to test the multivariate point null hypothesis.  After the user selects a parameter of interest and defines the assumed data generating mechanism, this information should be encoded in functions for the parameter estimator and its corresponding influence curve. Some parameter and data generating mechanism combinations have codings in this package, and are explained in detail in the article.
A method for automatic detection of peaks in noisy periodic and quasi-periodic signals. This method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima.
     For further information see  <doi:10.3390/a5040588>.
Predicts antimicrobial peptides using random forests trained on the
    n-gram encoded peptides. The implemented algorithm can be accessed from
    both the command line and shiny-based GUI. The AmpGram model is too large 
    for CRAN and it has to be downloaded separately from the repository:
    <https://github.com/michbur/AmpGramModel>.
A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale.
    It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated
    physico-chemical and compositional sequence properties described in Meher et al. (2017) <doi:10.1038/srep42362>.
    In order to support genome-wide analyses, these models are designed to accept any type of protein as input
    and calculation of compositional properties has been optimised for high-throughput use. For best results it is important to select the model that accurately 
    represents your sequence type: for full length proteins, it is recommended to use the default "precursor" model. The alternative, "mature", model is best suited
    for mature peptide sequences that represent the final antimicrobial peptide sequence after post-translational processing. For details see Fingerhut et al. (2020) <doi:10.1093/bioinformatics/btaa653>.
    The 'ampir' package is also available via a Shiny based GUI at <https://ampir.marine-omics.net/>.
Three Shiny apps are provided that introduce Harvest Control Rules (HCR) for fisheries management.
    'Introduction to HCRs' provides a simple overview to how HCRs work. Users are able to select their own HCR and
    step through its performance, year by year. Biological variability and estimation uncertainty are introduced.
    'Measuring performance' builds on the previous app and introduces the idea of using performance indicators
    to measure HCR performance.
    'Comparing performance' allows multiple HCRs to be created and tested, and their performance compared so that the
    preferred HCR can be selected.
Increasingly powerful techniques for high-throughput sequencing open the possibility to comprehensively characterize microbial communities, including rare species. However, a still unresolved issue are the substantial error rates in the experimental process generating these sequences. To overcome these limitations we propose an approach, where each sample is split and the same amplification and sequencing protocol is applied to both halves. This procedure should allow to detect likely PCR and sequencing artifacts, and true rare species by comparison of the results of both parts. The AmpliconDuo package, whereas amplicon duo from here on refers to the two amplicon data sets of a split sample, is intended to help interpret the obtained read frequency distribution across split samples, and to filter the false positive reads.
Functions to simplify and standardise antimicrobial resistance (AMR)
  data analysis and to work with microbial and antimicrobial properties by
  using evidence-based methods, as described in <doi:10.18637/jss.v104.i03>.
Automates regression testing of package 'allelematch'. Over
    2500 tests covers all functions in 'allelematch', reproduces the
    examples from the documentation and includes negative tests. The
    implementation is based on 'testthat'.
Manage and analyze animal movement data. The functionality of
    'amt' includes methods to calculate home ranges, track statistics
    (e.g. step lengths, speed, or turning angles), prepare data for
    fitting habitat selection analyses, and simulation of space-use from
    fitted step-selection functions.
Predicts amyloid proteins using random forests trained on the
    n-gram encoded peptides. The implemented algorithm can be accessed from
    both the command line and shiny-based GUI.
A free software for a fast and easy analysis of 1:1 molecular interaction studies. 
   This package is suitable for a high-throughput data analysis. 
   Both the online app and the package are completely open source. 
   You provide a table of sensogram, tell 'anabel' which method to use,
   and it takes care of all fitting details.
   The first two releases of 'anabel' were created and implemented as in 
   (<doi:10.1177/1177932218821383>, <doi:10.1093/database/baz101>).
Is a collection of models to analyze genome scale codon
        data using a Bayesian framework. Provides visualization
        routines and checkpointing for model fittings. Currently
        published models to analyze gene data for selection on codon
        usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist
        et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi
        (Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In
        addition 'AnaCoDa' contains three currently unpublished models.
        The FONSE (First order approximation On NonSense Error) model
        analyzes gene data for selection on codon usage against of
        nonsense error rates. The PA (PAusing time) and PANSE (PAusing
        time + NonSense Error) models use ribosome footprinting data to
        analyze estimate ribosome pausing times with and without
        nonsense error rate from ribosome footprinting data.
Performs simple and canonical CA (covariates on rows/columns) on a two-way frequency table (with missings) by means of SVD. Different scaling methods (standard, centroid, Benzecri, Goodman) as well as various plots including confidence ellipsoids are provided.
Provides a set of functions for interacting with the 'DigitalOcean'
    API <https://www.digitalocean.com/>, including creating images, destroying 
    them, rebooting, getting details on regions, and available images.
Fits Modern Analogue Technique and Weighted Averaging transfer
  	     function models for prediction of environmental data from species
	     data, and related methods used in palaeoecology.
A quick and effective data exploration toolkit. It provides essential features, including a descriptive statistics table for a quick overview of your dataset, interactive distribution plots to visualize variable patterns, Principal Component Analysis for dimensionality reduction and feature analysis, missing value imputation methods, and correlation analysis.
Easy data analysis and quality checks which are commonly used in data science. It combines the tabular and graphical visualization for easier usability. This package also creates an R Notebook with detailed data exploration with one function call. The notebook can be made interactive.
Enables gene regulatory network (GRN) analysis on single cell clusters,
    using the GRN analysis software 'ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>.
    Export data from 'Seurat' objects, for GRN analysis by 'ANANSE' 
    implemented in 'snakemake'. Finally, incorporate results for visualization 
    and interpretation.
Functions for normalisation, differentially analysis of microarray data and local False Discovery Rate.
Assigns genetic ancestry to an individual and
    studies relationships between local and global populations.
Performs AnchorRegression proposed by Rothenhäusler et al. 2020. 
    The code is adapted from the original paper repository. (<https://github.com/rothenhaeusler/anchor-regression>)
    The code was developed independently from the authors of the paper.
Construct language-aware lists. Make "and"-separated and
    "or"-separated lists that automatically conform to the user's language
    settings.
Visualisation of multidimensional data through different Andrews curves: 
   Andrews, D. F. (1972) Plots of High-Dimensional Data. Biometrics, 28(1), 125-136. <doi:10.2307/2528964>.
Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.
The goal of 'andurinha' is provide a fast and friendly way to 
    process spectroscopic data. It is intended for processing several spectra of 
    samples with similar composition (tens to hundreds of spectra). It compiles 
    spectroscopy data files, produces standardized and second derivative spectra, 
    finds peaks and allows to select the most significant ones based on the second 
    derivative/absorbance sum spectrum. It also provides functions for graphic 
    evaluation of the outputs.
Provides a comprehensive system for selecting
    variables and weighting data to match the specifications of the American
    National Election Studies. The package includes methods for identifying
    discrepant variables, raking data, and assessing the effects of the raking
    algorithm. It also allows automated re-raking if target variables fall
    outside identified bounds and allows greater user specification than other
    available raking algorithms. A variety of simple weighted statistics that
    were previously in this package (version .55 and earlier) have been moved to
    the package 'weights.'
Simulate an angler population, sample the simulated population with a user-specified survey times, and calculate metrics from a bus route-type creel survey.
Helper functions for working with Regional Ocean Modeling System 'ROMS' output. See
    <https://www.myroms.org/> for more information about 'ROMS'.
Provides: (1) Tools to infer dominance hierarchies based on calculating Elo scores, but with custom functions to improve estimates in animals with relatively stable dominance ranks. (2) Tools to plot the shape of the dominance hierarchy and estimate the uncertainty of a given data set.
Simple animated versions of basic R plots, using the 'animation'
    package. Includes animated versions of plot, barplot, persp, contour,
    filled.contour, hist, curve, points, lines, text, symbols, segments, and
    arrows.
Synthetic generation of 1-D and 2-D correlated random walks (CRWs) for animal movement with behavioral switching, and particle filter estimation of movement parameters from observed trajectories using Extended Kalman Filter (EKF) model. See Ackerman (2018) <https://digital.library.temple.edu/digital/collection/p245801coll10/id/499150>.
Functions for generating and visualising networks for characterising the physical attributes and spatial organisations of habitat components (i.e. habitat physical configurations). The network generating algorithm first determines the X and Y coordinates of N nodes within a rectangle with a side length of L and an area of A. Then it computes the pair-wise Euclidean distance Dij between node i and j, and then a complete network with 1/Dij as link weights is constructed. Then, the algorithm removes links from the complete network with the probability as shown in the function ahn_prob(). Such link removals can make the network disconnected whereas a connected network is wanted. In such cases, the algorithm rewires one network component to its spatially nearest neighbouring component and repeat doing this until the network is connected again. Finally, it outputs an undirected network (weighted or unweighted, connected or disconnected). This package came with a manuscript on modelling the physical configurations of animal habitats using networks (in preparation).
Implements a web-based graphics device for animated visualisations.
  Modelled on the 'base' syntax, it extends the 'base' graphics functions to
  support frame-by-frame animation and keyframes animation.
  The target use cases are real-time animated visualisations, including agent-based
  models, dynamical systems, and animated diagrams.
  The generated visualisations can be deployed as GIF images / MP4 videos, as
  'Shiny' apps (with interactivity) or as HTML documents through embedding into
  R Markdown documents.
Provides functions for animations in statistics, covering topics
    in probability theory, mathematical statistics, multivariate statistics,
    non-parametric statistics, sampling survey, linear models, time series,
    computational statistics, data mining and machine learning. These functions
    may be helpful in teaching statistics and data analysis. Also provided in this
    package are a series of functions to save animations to various formats, e.g.
    Flash, 'GIF', HTML pages, 'PDF' and videos. 'PDF' animations can be inserted
    into 'Sweave' / 'knitr' easily.
Create an interactive visualization to be used for communication purposes. Providing the function for preparing, plotting, and animating the data.
Functions are provided for defining animated,
 interactive data visualizations in R code, and rendering
 on a web page. The 2018 Journal of Computational and 
 Graphical Statistics paper,
 <doi:10.1080/10618600.2018.1513367>
 describes the concepts implemented.
Functions required to classify subjects within camera trap field data. The package can handle both images and videos. The authors recommend a two-step approach using Microsoft's 'MegaDector' model and then a second model trained on the classes of interest.
Animation of observed trajectories using spline-based interpolation (see for example, Buderman, F. E., Hooten, M. B., Ivan, J. S. and Shenk, T. M. (2016), <doi:10.1111/2041-210X.12465> "A functional model for characterizing long-distance movement behaviour". Methods Ecol Evol). Intended to be used exploratory data analysis, and perhaps for preparation of presentations.
Obtain network structures from animal GPS telemetry observations and 
    statistically analyse them to assess their adequacy for social network analysis. Methods include 
    pre-network data permutations, bootstrapping techniques to obtain confidence intervals 
    for global and node-level network metrics, and correlation and regression analysis of the local network metrics.
Animate Shiny and R Markdown content when it comes into view using 'animate-css' effects thanks to 'jQuery AniView'.
Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors.
Training of neural networks for classification and regression tasks
    using mini-batch gradient descent. Special features include a function for 
    training autoencoders, which can be used to detect anomalies, and some 
    related plotting functions. Multiple activation functions are supported, 
    including tanh, relu, step and ramp. For the use of the step and ramp 
    activation functions in detecting anomalies using autoencoders, see 
    Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore, 
    several loss functions are supported, including robust ones such as Huber 
    and pseudo-Huber loss, as well as L1 and L2 regularization. The possible 
    options for optimization algorithms are RMSprop, Adam and SGD with momentum.
    The package contains a vectorized C++ implementation that facilitates 
    fast training through mini-batch learning.
A 'reticulate' wrapper for the Python package 'anndata'.
    Provides a scalable way of keeping track of data and learned
    annotations.  Used to read from and write to the h5ad file format.
Implements persistent row and column annotations for R matrices. The annotations associated with rows and columns are preserved after subsetting, transposition, and various other matrix-specific operations. Intended use case is for storing and manipulating genomic datasets which typically consist of a matrix of measurements (like gene expression values) as well as annotations about rows (i.e. genomic locations) and annotations about columns (i.e. meta-data about collected samples). But 'annmatrix' objects are also expected to be useful in various other contexts.
We curated 147 of expression array, from 3 species(human,mouse,rat), 
              3 companies('Affymetrix','Illumina','Agilent'), 
              by aligning the 'Fasta' sequences of all probes of each platform to their corresponding reference genome, 
              and then annotate them to genes.
Provides non-invasive annotation of package load calls 
    such as \code{library()}, \code{p_load()}, and \code{require()} so that we can have an idea of what 
    the packages we are loading are meant for.
Extraction of subsequences into FASTA files from GenBank annotations where gene names may vary among accessions. Borstein & O'Meara (2018) <doi:10.7717/peerj.5179>.
Provides functions to create image annotations through polygon outlining. Annotator has the same function as 'graphics::locator()' but achieves its purpose through drawing, rather than multiple mouse clicks. It is based on the 'htmlwidgets' package and 'fabric.js' JavaScript library <http://fabricjs.com/>.
Annuity Random Interest Rates proposes different techniques for the approximation of the present and final value of a unitary annuity-due or annuity-immediate considering interest rate as a random variable. Cruz Rambaud et al. (2017) <doi:10.1007/978-3-319-54819-7_16>. Cruz Rambaud et al. (2015) <doi:10.23755/rm.v28i1.25>.
Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test
    to compare clustering structures with applications in functional magnetic resonance imaging
    data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups
    simultaneously and also to identify features that contribute to the differential clustering.
Analyses of frequencies can be performed using an alternative test based 
    on the G statistic. The test has similar type-I error rates and power as 
    the chi-square test. However, it is based on a total statistic that can be 
    decomposed in an additive fashion into interaction effects, main effects, simple 
    effects, contrast effects, etc., mimicking precisely the logic of ANOVA. We call 
    this set of tools 'ANOFA' (Analysis of Frequency data) to highlight its 
    similarities with ANOVA. This framework also renders plots of frequencies 
    along with confidence intervals. Finally, effect sizes and planning statistical 
    power are easily done under this framework. The ANOFA is a tool that assesses 
    the significance of effects instead of the significance of parameters; as such, 
    it is more intuitive to most researchers than alternative approaches based on 
    generalized linear models. 
    See Laurencelle and Cousineau (2023) <doi:10.20982/tqmp.19.2.p173>.
Analysis of means (ANOM) as used in technometrical computing. The package takes results from multiple comparisons with the grand mean (obtained with 'multcomp', 'SimComp', 'nparcomp', or 'MCPAN') or corresponding simultaneous confidence intervals as input and produces ANOM decision charts that illustrate which group means deviate significantly from the grand mean.
The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
    The main functions are time_decompose(), anomalize(), and time_recompose().
    When combined, it's quite simple to decompose time series, detect anomalies,
    and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). 
    Time series decomposition is used to remove trend and seasonal components via the time_decompose() function
    and methods include seasonal decomposition of time series by Loess ("stl") and 
    seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements
    two methods for anomaly detection of residuals including using an inner quartile range ("iqr")
    and generalized extreme studentized deviation ("gesd"). These methods are based on
    those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. 
    Refer to the associated functions for specific references for these methods.
Implements Collective And Point Anomaly (CAPA) Fisch, Eckley, and Fearnhead (2022) <doi:10.1002/sam.11586>, Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) <doi:10.1080/10618600.2021.1987257>, Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) <arXiv:1412.5565>. These methods are for the detection of anomalies in time series data.
Analyses of Proportions can be performed on the Anscombe (arcsine-related) transformed  
    data. The 'ANOPA' package can analyze proportions obtained from up to 
    four factors. The factors can be within-subject or between-subject or a mix 
    of within- and between-subject. The main, omnibus analysis can be followed by
    additive decompositions into interaction effects, main effects, simple 
    effects, contrast effects, etc., mimicking precisely the logic of ANOVA. For
    that reason, we call this set of tools 'ANOPA' (Analysis of Proportion
    using Anscombe transform) to highlight its similarities with ANOVA. 
    The 'ANOPA' framework also allows plots of proportions easy to obtain 
    along with confidence intervals. Finally, effect sizes and planning statistical 
    power are easily done under this framework. Only particularity, the 'ANOPA' computes  
    F statistics which have an infinite degree of freedom on the denominator. 
    See Laurencelle and Cousineau (2023) <doi:10.3389/fpsyg.2022.1045436>.
An interactive document on  the topic of one-way and two-way analysis of variance using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at <https://tinyurl.com/ANOVAStatsTool>.
An interactive document on  the topic of one-way and two-way analysis of variance using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/ANOVAShiny/>.
Epidemiological population dynamics models traditionally define 
    a pathogen's virulence as the increase in the per capita rate of mortality 
    of infected hosts due to infection. This package provides functions 
    allowing virulence to be estimated by maximum likelihood techniques. The 
    approach is based on the analysis of relative survival comparing survival 
    in matching cohorts of infected vs. uninfected hosts (Agnew 2019) 
    <doi:10.1101/530709>.
Anscombe's quartet are a set of four two-variable datasets that 
    have several common summary statistics but which have very different joint 
    distributions.  This becomes apparent when the data are plotted, which 
    illustrates the importance of using graphical displays in Statistics.  This
    package enables the creation of datasets that have identical marginal sample
    means and sample variances, sample correlation, least squares regression 
    coefficients and coefficient of determination.  The user supplies an initial 
    dataset, which is shifted, scaled and rotated in order to achieve target 
    summary statistics.  The general shape of the initial dataset is retained. 
    The target statistics can be supplied directly or calculated based on a 
    user-supplied dataset.  The 'datasauRus' package 
    <https://cran.r-project.org/package=datasauRus> provides further examples 
    of datasets that have markedly different scatter plots but share many 
    sample summary statistics.
Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. 'AntAngioCOOL' exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, 'AntAngioCOOL' reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset.
Edit an 'Antares' simulation before running it : create new areas, links, thermal
    clusters or binding constraints or edit existing ones. Update 'Antares' general & optimization settings.
    'Antares' is an open source power system generator, more information available here : <https://antares-simulator.org/>.
Process results generated by 'Antares', a powerful open source software developed by
    RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about
    'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>).
    This package provides functions to create new columns like net load, load factors, upward and
    downward margins or to compute aggregated statistics like economic surpluses
    of consumers, producers and sectors.
Import, manipulate and explore results generated by 'Antares', a 
    powerful open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems
    (more information about 'Antares' here : <https://antares-simulator.org/>).
Visualize results generated by Antares, a powerful open source software
    developed by RTE to simulate and study electric power systems
    (more information about 'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>).
    This package provides functions that create interactive charts to help
    'Antares' users visually explore the results of their simulations.
Provides WHO Child Growth Standards (z-scores) with
             confidence intervals and standard errors around the
             prevalence estimates, taking into account complex sample designs.
             More information on the methods is
             available online:
             <https://www.who.int/tools/child-growth-standards>.
Provides WHO 2007 References for School-age Children and
             Adolescents (5 to 19 years) (z-scores) with
             confidence intervals and standard errors around the
             prevalence estimates, taking into account complex sample designs.
             More information on the methods is
             available online:
             <https://www.who.int/tools/growth-reference-data-for-5to19-years>.
Offers a graphical user interface for the calculation of the mean measure of divergence, with facilities for trait selection and graphical representations <doi:10.1002/ajpa.23336>.
Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed 		at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the 		statistical concept of data depth, statistical shape analysis and archetypal analysis. Please see Vinue (2017) <doi:10.18637/jss.v077.i06>.
Visualization of antibody titer scores is valuable for examination of vaccination 
    effects. 'AntibodyTiters' visualizes antibody titers of all or selected patients. This 
    package also produces empty excel files in a specified format, in which users can fill in 
    experimental data for visualization. Excel files with toy data can also be produced, so that 
    users can see how it is visualized before obtaining real data. The data should contain 
    titer scores at pre-vaccination, after-1st shot, after-2nd shot, and at least one 
    additional sampling points. Patients with missing values can be included. The first two 
    sampling points (pre-vaccination and after-1st shot) will be plotted discretely, whereas 
    those following will be plotted on a continuous time scale that starts from the day of 
    second shot. Half-life of titer can also be calculated for each pair of sampling points.
A collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of mergers under different competitive regimes.
Wraps the 'AntiWord' utility to extract text from Microsoft Word 
    documents. The utility only supports the old 'doc' format, not the new xml 
    based 'docx' format. Use the 'xml2' package to read the latter.
Fits finite Bayesian mixture models with a random number of components. The MCMC algorithm implemented is based on point processes as proposed by Argiento and De Iorio (2019) <arXiv:1904.09733> and offers a more computationally efficient alternative to reversible jump. Different mixture kernels can be specified: univariate Gaussian, multivariate Gaussian, univariate Poisson, and multivariate Bernoulli (latent class analysis). For the parameters characterising the mixture kernel, we specify conjugate priors, with possibly user specified hyper-parameters. We allow for different choices for the prior on the number of components: shifted Poisson, negative binomial, and point masses (i.e. mixtures with fixed number of components).
Data from the anxiety and confinement study from Alvarado-Aravena et al. (2022) <doi:10.3390/bs12100398>.
You can use this package to create custom pipeline badges in a standard 'svg' format.
    This is useful for a company to use internally, where it may not be possible
    to create badges through external providers.
    This project was inspired by the 'anybadge' library in python.
Supplies a set of functions to query air travel data for user-
    specified years and airports. Datasets include on-time flights, airlines,
    airports, planes, and weather.
Made to make your life simpler with packages, by
    installing and loading a list of packages, whether they are on CRAN,
    Bioconductor or github. For github, if you do not have the full path, with 
    the maintainer name in it (e.g. "achateigner/topReviGO"), it will be able to
    load it but not to install it.
Convert input in any one of character, integer, numeric, factor,
 or ordered type into 'POSIXct' (or 'Date') objects, using one of a number of
 predefined formats, and relying on Boost facilities for date and time parsing.
Alternating optimization of (high-dimensional) functions is an 
    iterative procedure for optimizing jointly over all parameters by 
    alternately optimizing parameter subsets.
Provides a set of functions to analyse
        overdispersed counts or proportions. Most of the methods are
        already available elsewhere but are scattered in different
        packages. The proposed functions should be considered as
        complements to more sophisticated methods such as generalized
        estimating equations (GEE) or generalized linear mixed effect
        models (GLMM).
Provides functions to analyse overdispersed
        counts or proportions.  These functions should be considered as
        complements to more sophisticated methods such as generalized
        estimating equations (GEE) or generalized linear mixed effect
        models (GLMM). aods3 is an S3 re-implementation of the
        deprecated S4 package aod.
Another implementation of object-orientation in R. It provides
    syntactic sugar for the S4 class system and two alternative new
    implementations. One is an experimental version built around S4
    and the other one makes it more convenient to work with lists as objects.
Download data from the 'Access to Opportunities Project (AOP)'. The 
             'aopdata' package brings annual estimates of access to employment, 
             health, education and social assistance services by transport mode, 
             as well as data  on the spatial distribution of population, jobs, 
             health care, schools and social assistance facilities at a fine 
             spatial resolution for all cities included in the project. More 
             info on the 'AOP' website <https://www.ipea.gov.br/acessooportunidades/en/>.
A collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.
It can sometimes be difficult to ascertain when some events (such as property crime)
    occur because the victim is not present when the crime happens. As a result, police databases often
    record a 'start' (or 'from') date and time, and an 'end' (or 'to') date and time. The time span between
    these date/times can be minutes, hours, or sometimes days, hence the term 'Aoristic'. 
    Aoristic is one of the past tenses in Greek and represents an uncertain occurrence in time. 
    For events with a location describes with either a latitude/longitude, or X,Y coordinate pair, 
    and a start and end date/time, this package generates an aoristic data frame with aoristic weighted
    probability values for each hour of the week, for each observation. The coordinates are not 
    necessary for the program to calculate aoristic weights; however, they are part of this package 
    because a spatial component has been integral to aoristic analysis from the start. Dummy 
    coordinates can be introduced if the user only has temporal data. Outputs include an aoristic 
    data frame, as well as summary graphs and displays.   
        For more information see:
    Ratcliffe, JH (2002) Aoristic signatures and the temporal analysis of high volume crime patterns, 
    Journal of Quantitative Criminology. 18 (1): 23-43.
    Note: This package replaces an original 'aoristic' package (version 0.6) by George Kikuchi that 
    has been discontinued with his permission.
Fit, interpret, and compute predictions with oblique random
    forests. Includes support for partial dependence, variable importance,
    passing customized functions for variable importance and identification
    of linear combinations of features. Methods for the oblique random 
    survival forest are described in Jaeger et al., (2023)
    <DOI:10.1080/10618600.2023.2231048>.
Trigger animation effects on scroll on any HTML element 
    of 'shiny' and 'rmarkdown', such as any text or plot, thanks to 
    the 'AOS' Animate On Scroll jQuery library.
Provides functions to perform statistical inference in the balanced one-way ANOVA model with a random factor: confidence intervals, prediction interval, and Weerahandi generalized pivotal quantities. References: Burdick & Graybill (1992, ISBN-13: 978-0824786441); Weerahandi (1995) <doi:10.1007/978-1-4612-0825-9>; Lin & Liao (2008) <doi:10.1016/j.jspi.2008.01.001>.
It covers various approaches to analysis of variance, provides an assumption testing section in order to provide a decision diagram that allows selecting the most appropriate technique. It provides the classical analysis of variance, the nonparametric equivalent of Kruskal Wallis, and the Bayesian approach. These results are shown in an interactive shiny panel, which allows modifying the arguments of the tests, contains interactive graphics and presents automatic conclusions depending on the tests in order to contribute to the interpretation of these analyzes. 'AovBay' uses 'Stan' and 'FactorBayes' for Bayesian analysis and 'Highcharts' for interactive charts.
Formatter functions in the 'apa' package take the return value of a
    statistical test function, e.g. a call to chisq.test() and return a string
    formatted according to the guidelines of the APA (American Psychological
    Association).
R interface for 'Apache Sedona' based on 'sparklyr'
    (<https://sedona.apache.org>).
Provides capabilities to process Apache HTTPD Log files.The main functionalities are to extract data from access and error log files to data frames.
Visualize clonal expansion via circle-packing. 'APackOfTheClones' extends 'scRepertoire' to produce a publication-ready visualization of clonal expansion at a single cell resolution, by representing expanded clones as differently sized circles. The method was originally implemented by Murray Christian and Ben Murrell in the following immunology study: Ma et al. (2021) <doi:10.1126/sciimmunol.abg6356>.
A common task faced by researchers is the creation of APA style
    (i.e., American Psychological Association style) tables from statistical
    output. In R a large number of function calls are often needed to obtain all of
    the desired information for a single APA style table. As well, the process of
    manually creating APA style tables in a word processor is prone to transcription
    errors. This package creates Word files (.doc files) containing APA style tables
    for several types of analyses. Using this package minimizes transcription errors
    and reduces the number commands needed by the user.
Create APA style text from analyses for use within R Markdown documents. Descriptive statistics, confidence intervals, and cell sizes are reported.
Functions for age-period-cohort analysis. Aggregate data can be organised in matrices indexed by age-cohort, age-period or cohort-period. The data can include dose and response or just doses. The statistical model is a generalized linear model (GLM) allowing for 3,2,1 or 0 of the age-period-cohort factors. Individual-level data should have a row for each individual and columns for each of age, period, and cohort. The statistical model for repeated cross-section is a generalized linear model. The statistical model for panel data is ordinary least squares. The canonical parametrisation of Kuang, Nielsen and Nielsen (2008) <DOI:10.1093/biomet/asn026> is used. Thus, the analysis does not rely on ad hoc identification.
Analysis of  data from unreplicated orthogonal experiments such as 2-level factorial and fractional factorial designs and Plackett-Burman designs using the all possible comparisons (APC) methodology developed by Miller (2005) <doi:10.1198/004017004000000608>.
The adapted pair correlation function transfers the concept of the
  pair correlation function from point patterns to patterns of objects of
  finite size and irregular shape (e.g. lakes within a country).  The pair
  correlation function describes the spatial distribution of objects, e.g.
  random, aggregated or regularly spaced. This is a reimplementation of the
  method suggested by Nuske et al. (2009) <doi:10.1016/j.foreco.2009.09.050>
  using the library 'GEOS'.
It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.
Implements Affinity Propagation clustering introduced by Frey and
	Dueck (2007) <DOI:10.1126/science.1136800>. The algorithms are largely
        analogous to the 'Matlab' code published by Frey and Dueck.
        The package further provides leveraged affinity propagation and an
        algorithm for exemplar-based agglomerative clustering that can also be
        used to join clusters obtained from affinity propagation. Various
        plotting functions are available for analyzing clustering results.
In fields such as ecology, microbiology, and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis (PCoA) is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide 'aPCoA' as an easy-to-use tool to improve data visualization in this context, enabling enhanced presentation of the effects of interest. Details are described in Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson and Robert Jenq (2020) Bioinformatics, Volume 36, Issue 13, 4099-4101.
Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments.
    The 'APCtools' package offers visualization techniques and general routines to simplify the workflow of an APC analysis.
    Sophisticated functions are available both for descriptive and regression model-based analyses.
    For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization
    techniques building on the concept of Lexis diagrams.
    Model-based analyses build on the separation of the temporal dimensions based on generalized additive models,
    where a tensor product interaction surface (usually between age and period) is utilized
    to represent the third dimension (usually cohort) on its diagonal.
    Such tensor product surfaces can also be estimated while accounting for
    further covariates in the regression model.
    See Weigert et al. (2021) <doi:10.1177/1354816620987198> for methodological details.
An implementation of the additive polynomial (AP) design matrix. It
    constructs and appends an AP design matrix to a data frame for use with
    longitudinal data subject to seasonality.
Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.
Simplify pathway enrichment analysis results by detecting clusters
    of similar pathways and visualizing it as an enrichment network, where
    nodes and edges describe the pathways and similarity between them,
    respectively. This reduces the redundancy of the overlapping pathways and
    helps to notice the most important biological themes in the data
    (Kerseviciute and Gordevicius (2023) <doi:10.1101/2023.03.28.534514>).
The goal is to print an "aperçu", a short view of a vector, a
    matrix, a data.frame, a list or an array. By default, it prints the first 5
    elements of each dimension. By default, the number of columns is equal to
    the number of lines. If you want to control the selection of the elements,
    you can pass a list, with each element being a vector giving the selection
    for each dimension.
Toolkit for the analysis of multiple gene data (Jombart et al. 2017) <doi:10.1111/1755-0998.12567>. 
    'apex' implements the new S4 classes 'multidna', 'multiphyDat' and associated methods to handle aligned DNA sequences from multiple genes.
Provides an 'htmlwidgets' interface to 'apexcharts.js'. 
  'Apexcharts' is a modern JavaScript charting library to build interactive charts and visualizations with simple API.
  'Apexcharts' examples and documentation are available here: <https://apexcharts.com/>.
Implements a multiple testing approach to the
    choice of a threshold gamma on the p-values using the
    Average Power Function (APF) and Bayes False Discovery
    Rate (FDR) robust estimation. Function apf_fdr() 
    estimates both quantities from either raw data or
    p-values. Function apf_plot() produces smooth graphs 
    and tables of the relevant results. Details of the methods
    can be found in Quatto P, Margaritella N, et al. (2019) 
    <doi:10.1177/0962280219844288>.
Designed for the development and application of
    hidden Markov models and profile HMMs for biological sequence analysis. 
    Contains functions for multiple and pairwise sequence alignment, 
    model construction and parameter optimization, file import/export,
    implementation of the forward, backward and Viterbi algorithms for 
    conditional sequence probabilities, tree-based sequence weighting, 
    and sequence simulation. 
    Features a wide variety of potential applications including 
    database searching, gene-finding and annotation, phylogenetic 
    analysis and sequence classification.
    Based on the models and algorithms described in Durbin et 
    al (1998, ISBN: 9780521629713).
Implements a parsimonious evolutionary model to analyze and
  predict gene-functional annotations in phylogenetic trees as described in Vega
  Yon et al. (2021) <doi:10.1371/journal.pcbi.1007948>. Focusing on
  computational efficiency, 'aphylo' makes it possible to estimate pooled
  phylogenetic models, including thousands (hundreds) of annotations (trees) in
  the same run. The package also provides the tools for visualization of
  annotated phylogenies, calculation of posterior probabilities (prediction)
  and goodness-of-fit assessment featured in Vega Yon et al. (2021).
Simplifies aspects of linear regression analysis, particularly simultaneous inference. Additionally, supports "A Progressive Introduction to Linear Models" by Joshua French (<https://jfrench.github.io/LinearRegression/>).
Parentage assignment package.
    Parentage assignment is performed based on observed average Mendelian transmission probability distributions or Exclusion.
    The main functions of this package are the function APIS_2n(), APIS_3n and launch_APIShiny(), which perform parentage assignment.
API for using 'episensr', Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. See <https://cran.r-project.org/package=episensr>.
An unofficial companion to "Applied
    Logistic Regression" by D.W. Hosmer, S. Lemeshow and
    R.X. Sturdivant (3rd ed., 2013) containing the dataset used in the book.
For many times, we are not just aligning plots as what 'cowplot' and 'patchwork' did. Users would like to align associated information that requires axes to be exactly matched in subplots, e.g. hierarchical clustering with a heatmap. This package provides utilities to aligns associated subplots to a main plot at different sides (left, right, top and bottom) with axes exactly matched.
Many complex plots are actually composite plots, such as 'oncoplot', 'funkyheatmap', 'upsetplot', etc. We can produce subplots using 'ggplot2' and combine them to create composite plots using 'aplot'. In this way, it is easy to customize these complex plots, by adding, deleting or modifying subplots in the final plot. This package provides a set of utilities to help users to create subplots and complex plots.
Some functions for drawing some special plots:
   The function 'bagplot' plots a bagplot,
   'faces' plots chernoff faces,
   'iconplot' plots a representation of a frequency table or a data matrix,
   'plothulls' plots hulls of a bivariate data set,
   'plotsummary' plots a graphical summary of a data set,
   'puticon' adds icons to a plot,
   'skyline.hist' combines several histograms of a one dimensional data set in one plot,
   'slider' functions supports some interactive graphics,
   'spin3R' helps an inspection of a 3-dim point cloud,
   'stem.leaf' plots a stem and leaf plot,
   'stem.leaf.backback' plots back-to-back versions of stem and leaf plot.
Automated methods to assemble population PK (pharmacokinetic) and
    PKPD (pharmacodynamic) datasets for analysis in 'NONMEM' (non-linear mixed effects
    modeling) by Bauer (2019) <doi:10.1002/psp4.12404>. The package includes functions
    to build datasets from SDTM (study data tabulation module)
    <https://www.cdisc.org/standards/foundational/sdtm>, ADaM (analysis dataset
    module) <https://www.cdisc.org/standards/foundational/adam>, or other dataset
    formats. The package will combine population datasets, add covariates, and
    create documentation to support regulatory submission and internal communication.
Convert several png files into an animated png file.
  This package exports only a single function `apng'. Call the
  apng function with a vector of file names (which should be
  png files) to convert them to a single animated png file.
Finds, prioritizes and deletes erroneous taxa in a phylogenetic tree. This package calculates scores for taxa in a tree. Higher score means the taxon is more erroneous. If the score is zero for a taxon, the taxon is not erroneous. This package also can remove all erroneous taxa automatically by iterating score calculation and pruning taxa with the highest score.
Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool for the estimation and application 
    of choice models in R. Users are able to write their own 
    model functions or use a mix of already available ones. Random heterogeneity, 
    both continuous and discrete and at the level of individuals and 
    choices, can be incorporated for all models. There is support for both standalone 
    models and hybrid model structures.  Both classical 
    and Bayesian estimation is available, and multiple discrete 
    continuous models are covered in addition to discrete choice. 
    Multi-threading processing is supported for estimation and a large
    number of pre and post-estimation routines, including for computing posterior
    (individual-level) distributions are available. 
    For examples, a manual, and a support forum, visit 
    <http://www.ApolloChoiceModelling.com>. For more information on choice 
    models see Train, K. (2009) <isbn:978-0-521-74738-7> and Hess, 
    S. & Daly, A.J. (2014) <isbn:978-1-781-00314-5> for an overview 
    of the field.
Computation of the Apollonius diagram of given 2D points and
    its dual the Apollonius graph, also known as the additively weighted
    Voronoï diagram, and which is a generalization of the classical Voronoï 
    diagram. For references, see the bibliography in the CGAL documentation 
    at <https://doc.cgal.org/latest/Apollonius_graph_2/citelist.html>.
Programmatic interface to the NASA Application for Extracting and Exploring Analysis Ready Samples services (AppEEARS; <https://appeears.earthdatacloud.nasa.gov/>).
 The package provides easy access to analysis ready earth observation data in R.
Calculating predictive model performance measures adjusted for predictor distributions using density ratio method (Sugiyama et al., (2012, ISBN:9781139035613)). L1 and L2 error for continuous outcome and C-statistics for binomial outcome are computed.
Collect  your data on digital marketing campaigns from Apple Search Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Using 'Apple App Store' <https://www.apple.com/app-store/> web scraping and 'iTunes' API 
    <https://performance-partners.apple.com/search-api>
    to extract content information, app ratings and reviews.
A modeling package compiling applicability domain methods in
    R. It combines different methods to measure the amount of
    extrapolation new samples can have from the training set. See Gadaleta
    et al (2016) <doi:10.4018/IJQSPR.2016010102> for an overview of
    applicability domains.
A few functions and several data set for the Springer book 'Applied Predictive Modeling'.
Amyloid propensity prediction neural network (APPNN) is an amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation.
Convert populations into integer number of seats for legislative 
    bodies. Implements apportionment methods used historically and currently in the
    United States for reapportionment after the Census, as described in 
    <https://www.census.gov/history/www/reference/apportionment/methods_of_apportionment.html>.
With 'appRiori', users upload the research variables and the app guides them to the best set of comparisons fitting the hypotheses, for both main and interaction effects. Through a graphical explanation and empirical examples on reproducible data, it is shown that it is possible to understand both the logic behind the planned comparisons and the way to interpret them when a model is tested.
Performs Bayesian prediction of complex computer codes when fast approximations are available.  It uses a hierarchical version of the Gaussian process, originally proposed by Kennedy and O'Hagan (2000), Biometrika 87(1):1.
Tools for constructing a matched design with multiple comparison groups.
 Further specifications of refined covariate balance restriction and exact match on 
 covariate can be imposed. Matches are approximately optimal in  the sense that the 
 cost of the solution is at most twice the optimal cost, Crama and Spieksma (1992) 
 <doi:10.1016/0377-2217(92)90078-N>, Karmakar, Small and Rosenbaum (2019)
 <doi:10.1080/10618600.2019.1584900>.
R and C++ functions to perform exact and 
  approximate optimal transport. All C++ methods can be linked 
  to other R packages via their header files.
Collect  your data on digital marketing campaigns from 'Appsflyer' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Functionality to add, delete, read and update table
    records from your 'AppSheet' apps, using the official API <https://api.appsheet.com/>.
An unofficial companion to the textbook "Applied Regression
    Analysis" by N.R. Draper and H. Smith (3rd Ed., 1998) including all the
    accompanying datasets.
Assists the evaluation of whether and
    where to focus code optimization, using Amdahl's law and visual aids
    based on line profiling. Amdahl's profiler organizes profiling output
    files (including memory profiling) in a visually appealing way.
    It is meant to help to balance development
    vs. execution time by helping to identify the most promising sections
    of code to optimize and projecting potential gains. The package is
    an addition to R's standard profiling tools and is not a wrapper for them.
The functions in this package inspect, read, edit and run files for 'APSIM' "Next Generation" ('JSON')
             and 'APSIM' "Classic" ('XML'). The files with an 'apsim' extension correspond to
	     'APSIM' Classic (7.x) - Windows only - and the ones with an 'apsimx' extension correspond to 'APSIM' "Next Generation".
	     For more information about 'APSIM' see (<https://www.apsim.info/>) and for 'APSIM'
	     next generation (<https://apsimnextgeneration.netlify.app/>).
Asymmetric price transmission between two time series is assessed. Several functions are available for linear and nonlinear threshold cointegration, and furthermore, symmetric and asymmetric error correction model.
We provide tools to estimate two prediction accuracy metrics,
    the average positive predictive values (AP) as well as the well-known AUC
    (the area under the receiver operator characteristic curve) for risk scores. 
    The outcome of interest is either binary or censored event time.
    Note that for censored event time, our functions' estimates, the AP and the
    AUC, are time-dependent for pre-specified time interval(s). A function that
    compares the APs of two risk scores/markers is also included. Optional
    outputs include positive predictive values and true positive fractions at
    the specified marker cut-off values, and a plot of the time-dependent AP
    versus time (available for event time data).
Provides a quick method for visualizing non-aggregated line-list
    or aggregated census data stratified by age and one or two categorical
    variables (e.g. gender and health status) with any number of values. It
    returns a 'ggplot' object, allowing the user to further customize the
    output. This package is part of the 'R4Epis' project 
    <https://r4epis.netlify.app/>.
Developed for use by those tasked with the routine detection, characterisation
          and quantification of discrete changes in air quality time-series, such as
          identifying the impacts of air quality policy interventions. The main functions use
          signal isolation then break-point/segment (BP/S) methods based on 'strucchange' and
          'segmented' methods to detect and quantify change events (Ropkins & Tate, 2021,
          <doi:10.1016/j.scitotenv.2020.142374>).
Functions are included for recalling AQL (Acceptable Quality Level or Acceptance Quality Level) 
 Based single, double, and multiple attribute sampling plans from the Military Standard (MIL-STD-105E) - American 
 National Standards Institute/American Society for Quality (ANSI/ASQ Z1.4) tables and for retrieving variable
 sampling plans from Military Standard (MIL-STD-414) - American National Standards Institute/American Society
 for Quality (ANSI/ASQ Z1.9) tables. The sources for these tables are listed in the URL: field. Also included
 are functions for computing the OC (Operating Characteristic) and ASN (Average Sample Number) coordinates
 for the attribute plans it recalls, and functions for computing the estimated proportion nonconforming and
 the maximum allowable proportion nonconforming for variable sampling plans. The MIL-STD AQL Sampling schemes
 were the most used and copied set of standards in the world. They are intended to be used for sampling a stream
 of lots, and were used in contract agreements between supplier and customer companies. When the US military
 dropped support of MIL-STD 105E and 414, The American National Standards Institute (ANSI) and the International
 Standards Organization (ISO) adopted the standard with few changes or no changes to the central tables. This
 package is useful because its computer implementation of these tables duplicates that available in other
 commercial software and subscription online calculators.
The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.
Computes and integrates daily reference 'evapotranspiration' ('Eto') into a water balance model, 
             to estimate the calendar of wet-season (Onset, Cessation and Duration) based on 'agroclimatic' 
             approach, for further information please refer to Allen 'et al.' (1998, ISBN:92-5-104219-5),
             Allen (2005, ISBN:9780784408056), 'Doorenbos' and Pruitt (1975, ISBN:9251002797)
             'Guo et al.' (2016) <doi:10.1016/j.envsoft.2015.12.019>, Hargreaves and 'Samani' (1985) 
             <doi:10.13031/2013.26773>, Priestley and Taylor (1972) <https://journals.ametsoc.org/downloadpdf/journals/mwre/100/2/1520-0493_1972_100_0081_otaosh_2_3_co_2.pdf>.
Provides an automatic aggregation tool to manage point data privacy, 
    intended to be helpful for the production of official spatial data and for researchers.
    The package pursues the data accuracy at the smallest possible areas preventing 
    individual information disclosure. The methodology, based on hierarchical geographic 
    data structures performs aggregation and local suppression of point data to ensure privacy
    as described in Lagonigro, R., Oller, R., Martori J.C. (2017) <doi:10.2436/20.8080.02.55>.
    The data structures are created following the guidelines for grid datasets from the
    European Forum for Geography and Statistics.
Toolbox for the experimental aquatic chemist, focused on 
        acidification and CO2 air-water exchange. It contains all elements to
        model the pH, the related CO2 air-water exchange, and
        aquatic acid-base chemistry for an arbitrary marine,
        estuarine or freshwater system. It contains a suite of tools for 
        sensitivity analysis, visualisation, modelling of chemical batches, 
        and can be used to build dynamic models of aquatic systems. 
        As from version 1.0-4, it also contains functions to calculate 
        the buffer factors.
Estimate aquatic species life history using robust techniques.
    This package supports users undertaking two types of analysis: 1) Growth from
    length-at-age data, and 2) maturity analyses for length and/or age data.
    Maturity analyses are performed using generalised linear model approaches incorporating
    either a binomial or quasibinomial distribution.
    Growth modelling is performed using the multimodel approach presented by
    Smart et al. (2016) "Multimodel approaches in shark and ray growth studies:
    strengths, weaknesses and the future" <doi:10.1111/faf.12154>.
The Aquo Standard is the Dutch Standard for the exchange of 
    data in water management. With *aquodom* (short for aquo domaintables) 
    it is easy to exploit the API (<https://www.aquo.nl/index.php/Hoofdpagina>) to download domaintables 
    of the Aquo Standard and use them in R.
In mathematics, 'rejection sampling' is a basic technique used to generate observations from a distribution. It is also commonly called 'the Acceptance-Rejection method' or 'Accept-Reject algorithm' and is a type of Monte Carlo method. 'Acceptance-Rejection method' is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package 'AR' is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package 'AR' is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method.
Using sparse precision matricies and Choleski factorization simulates data that is auto-regressive.
Simple functions to convert given Arabic numerals to Kansuji  
    numerical figures that represent numbers written in Chinese characters.
Allows users to stem Arabic texts for text analysis.
Allows the user to connect with the World Spider Catalogue (WSC; <https://wsc.nmbe.ch/>) and the World Spider Trait (WST; <https://spidertraits.sci.muni.cz/>) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; <https://www.gbif.org/>), and mapping.
Processes noble gas mass spectrometer data to determine the isotopic composition of argon (comprised of Ar36, Ar37, Ar38, Ar39 and Ar40) released from neutron-irradiated potassium-bearing minerals. Then uses these compositions to calculate precise and accurate geochronological ages for multiple samples as well as the covariances between them. Error propagation is done in matrix form, which jointly treats all samples and all isotopes simultaneously at every step of the data reduction process. Includes methods for regression of the time-resolved mass spectrometer signals to t=0 ('time zero') for both single- and multi-collector instruments, blank correction, mass fractionation correction, detector intercalibration, decay corrections, interference corrections, interpolation of the irradiation parameter between neutron fluence monitors, and (weighted mean) age calculation. All operations are performed on the logs of the ratios between the different argon isotopes so as to properly treat them as 'compositional data', sensu Aitchison [1986, The Statistics of Compositional Data, Chapman and Hall].
Implements the Classification-based on
    Association Rules (CBA) algorithm for association rule classification.
    The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>,
    contains several convenience methods that allow to automatically
    set CBA parameters (minimum confidence, minimum support) and it also natively
    handles numeric attributes by integrating a pre-discretization step.
    The rule generation phase is handled by the 'arules' package. 
    To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 
    'qCBA' package is suggested.
It fits a univariate left, right, or interval censored linear regression model
    with autoregressive errors, considering the normal or the Student-t distribution for the 
    innovations. It provides estimates and standard errors of the parameters, predicts 
    future observations, and supports missing values on the dependent variable.
    References used for this package:
    Schumacher, F. L., Lachos, V. H., & Dey, D. K. (2017). Censored regression models with 
    autoregressive errors: A likelihood-based perspective. Canadian Journal of Statistics,
    45(4), 375-392 <doi:10.1002/cjs.11338>.
    Schumacher, F. L., Lachos, V. H., Vilca-Labra, F. E., & Castro, L. M. (2018). Influence 
    diagnostics for censored regression models with autoregressive errors. Australian & New 
    Zealand Journal of Statistics, 60(2), 209-229 <doi:10.1111/anzs.12229>.
    Valeriano, K. A., Schumacher, F. L., Galarza, C. E., & Matos, L. A. (2021). Censored 
    autoregressive regression models with Student-t innovations. arXiv preprint 
    <arXiv:2110.00224>.
Lite interface for finding locations of addresses or
    businesses around the world using the 'ArcGIS' REST API service
    <https://developers.arcgis.com/rest/geocode/api-reference/overview-world-geocoding-service.htm>.
    Address text can be converted to location candidates and a location
    can be converted into an address. No API key required.
Provides easy installation and loading of core ArcGIS
    location services packages 'arcgislayers' and 'arcgisutils'. Enabling
    developers to interact with spatial data and services from 'ArcGIS
    Online', 'ArcGIS Enterprise', and 'ArcGIS Platform'. Learn more about
    the 'arcgis' meta-package at <https://r.esri.com/r-bridge-site/>.
Enables users of 'ArcGIS Enterprise', 'ArcGIS Online', or
    'ArcGIS Platform' to read, write, publish, or manage vector and raster
    data via ArcGIS location services REST API endpoints
    <https://developers.arcgis.com/rest/>.
Developer oriented utility functions designed to be used as
    the building blocks of R packages that work with ArcGIS Location
    Services. It provides functionality for authorization, Esri JSON
    construction and parsing, as well as other utilities pertaining to
    geometry and Esri type conversions. To support 'ArcGIS Pro' users,
    authorization can be done via 'arcgisbinding'. Installation
    instructions for 'arcgisbinding' can be found at
    <https://r.esri.com/r-bridge-site/arcgisbinding/installing-arcgisbinding.html>.
Provides the data sets used to build the 'ArchaeoPhases' vignettes.  The data sets were formerly distributed with 'ArchaeoPhases', however they exceed current CRAN policy for package size.
The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in "Quantitative Methods in Archaeology Using R" by David L Carlson, one of the Cambridge Manuals in Archaeology.
Methods to analyse fragmented objects in archaeology using refitting relationships between fragments scattered in archaeological spatial units (e.g. stratigraphic layers). Graphs and graph theory are used to model archaeological observations. The package is mainly based on the 'igraph' package for graph analysis. Functions can: 1) create, manipulate, and simulate fragmentation graphs, 2) measure the cohesion and admixture of archaeological spatial units, and 3) characterise the topology of a specific set of refitting relationships. An empirical dataset is also provided as an example. Documentation about 'archeofrag' is provided by the vignette included in this package and by the accompanying scientific papers: Plutniak (2021, Journal of Archaeological Science, <doi:10.1016/j.jas.2021.105501>) and Plutniak (2022, Journal of Open Source Software, <doi:10.21105/joss.04335>).
An R 'Shiny' application for the visualisation, interactive exploration, and web communication of archaeological spatial data. It includes interactive 3D and 2D visualisations, generation of cross sections and maps of the remains, and display an interactive timeline of the work made in a site. Simple spatial statistics can be performed (convex hull, regression surfaces, 2D kernel density estimation), as well as exporting data to other online applications for more complex methods. 'archeoViz' can be used locally or deployed on a server, either with interactive input of data or with a static data set. Example is provided at <https://analytics.huma-num.fr/archeoviz/en>.
Performs archetypal analysis by using Principal Convex Hull Analysis 
    under a full control of all algorithmic parameters. 
    It contains a set of functions for determining the initial solution, the optimal 
    algorithmic parameters and the optimal number of archetypes.
    Post run tools are also available for the assessment of the derived solution. 
    Morup, M., Hansen, LK (2012) <doi:10.1016/j.neucom.2011.06.033>.
    Hochbaum, DS, Shmoys, DB (1985) <doi:10.1287/moor.10.2.180>.
    Eddy, WF (1977) <doi:10.1145/355759.355768>.
    Barber, CB, Dobkin, DP, Huhdanpaa, HT (1996) <doi:10.1145/235815.235821>.    
    Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076>.    
    Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., Sunde, U. (2018), <doi:10.1093/qje/qjy013>. 
    Christopoulos, DT (2015) <doi:10.1016/j.jastp.2015.03.009> .  
    Murari, A., Peluso, E., Cianfrani, Gaudio, F., Lungaroni, M., (2019), <doi:10.3390/e21040394>.
A project template to support the data science workflow.
The main function archetypes implements a
    framework for archetypal analysis supporting arbitrary
    problem solving mechanisms for the different conceptual
    parts of the algorithm.
Analysis of complex plant root system architectures (RSA) using the output files created by Data Analysis of Root Tracings (DART), an open-access software dedicated to the study of plant root architecture and development across time series (Le Bot et al (2010) "DART: a software to analyse root system architecture and development from captured images", Plant and Soil, <DOI:10.1007/s11104-009-0005-2>), and RSA data encoded with the Root System Markup Language (RSML) (Lobet et al (2015) "Root System Markup Language: toward a unified root architecture description language", Plant Physiology, <DOI:10.1104/pp.114.253625>). More information can be found in Delory et al (2016) "archiDART: an R package for the automated computation of plant root architectural traits", Plant and Soil, <DOI:10.1007/s11104-015-2673-4>.
Bindings to 'libarchive' <http://www.libarchive.org> the
    Multi-format archive and compression library. Offers R connections and
    direct extraction for many archive formats including 'tar', 'ZIP',
    '7-zip', 'RAR', 'CAB' and compression formats including 'gzip',
    'bzip2', 'compress', 'lzma' and 'xz'.
Scraping content from archived web pages stored in
    the 'Internet Archive' (<https://archive.org>) using a systematic
    workflow.  Get an overview of the mementos available from the
    respective homepage, retrieve the Urls and links of the page and
    finally scrape the content. The final output is stored in tibbles,
    which can be then easily used for further analysis.
Data exploration and modelling is a process in which a lot of data
    artifacts are produced. Artifacts like: subsets, data aggregates, plots,
    statistical models, different versions of data sets and different versions
    of results. The more projects we work with the more artifacts are produced
    and the harder it is to manage these artifacts. Archivist helps to store
    and manage artifacts created in R. Archivist allows you to store selected
    artifacts as a binary files together with their metadata and relations.
    Archivist allows to share artifacts with others, either through shared
    folder or github. Archivist allows to look for already created artifacts by
    using it's class, name, date of the creation or other properties. Makes it
    easy to restore such artifacts. Archivist allows to check if new artifact
    is the exact copy that was produced some time ago. That might be useful
    either for testing or caching.
The extension of the 'archivist' package integrating the archivist with GitHub via GitHub API, 'git2r' packages and 'httr' package.
Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) <DOI:10.2139/ssrn.2823687>.
For emulating multifidelity computer models. The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2020) <DOI:10.1137/19M1289893>. The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2019) <arXiv:1909.01836>.
Fast processing of ArcGIS FeatureCollection protocol buffers in R.
  It is designed to work seamlessly with 'httr2' and integrates with 'sf'.
Functions to efficiently query 'ArcGIS REST' APIs 
  <https://developers.arcgis.com/rest/>. 
  Both spatial and SQL queries can be used to retrieve data. 
  Simple Feature (sf) objects are utilized to perform spatial queries. 
  This package was neither produced nor is maintained by Esri.
An interface to the 'ArcGIS' 'arcpy' and 'arcgis' 'python' API
    <https://pro.arcgis.com/en/pro-app/latest/arcpy/get-started/arcgis-api-for-python.htm>.
    Provides various tools for installing and configuring a 'Conda' environment
    for accessing 'ArcGIS' geoprocessing functions. Helper functions for
    manipulating and converting 'ArcGIS' objects from R are also provided.
Provides functions to process minute level actigraphy-measured activity counts data and extract commonly used physical activity volume and fragmentation metrics.
Autoregressive-based decomposition of a time series based on the approach in West (1997). Particular cases include the extraction of trend and seasonal components.
A set of functions to access the 'ARDECO' (Annual Regional Database
    of the European Commission) data directly from the official ARDECO public
    repository through the exploitation of the 'ARDECO' APIs.
    The APIs are completely transparent to the user and the provided functions
    provide a direct access to the 'ARDECO' data.
    The 'ARDECO' database is a collection of variables related to demography,
    employment, labour market, domestic product, capital formation.
    Each variable can be exposed in one or more units of measure as well as
    refers to total values plus economic sectors.
    The description of the 'ARDECO' database can be found at the following URL
    <https://urban.jrc.ec.europa.eu/ardeco>.
Creates complex autoregressive distributed lag (ARDL) models and 
    constructs the underlying unrestricted and restricted error correction 
    model (ECM) automatically, just by providing the order. It also performs
    the bounds-test for cointegration as described in Pesaran et al. (2001) 
    <doi:10.1002/jae.616> and provides the multipliers and the cointegrating
    equation. The validity and the accuracy of this package have been verified 
    by successfully replicating the results of Pesaran et al. (2001) in 
    Natsiopoulos and Tzeremes (2022) <doi:10.1002/jae.2919>.
Estimate the linear and nonlinear autoregressive distributed lag (ARDL & NARDL) models and the corresponding error correction models, and test for longrun and short-run asymmetric. The general-to-specific approach is also available in estimating the ARDL and NARDL models. The Pesaran, Shin & Smith (2001) (<doi:10.1002/jae.616>) bounds test for level relationships is also provided. The 'ardl.nardl' package also performs short-run and longrun symmetric restrictions available at Shin et al. (2014) <doi:10.1007/978-1-4899-8008-3_9> and their corresponding tests.
Contains functions to help create an Analysis Results Dataset.
    The dataset follows industry recommended structure.  The dataset can be 
    created in multiple passes, using different data frames as input.  Analysis
    Results Datasets are used in the pharmaceutical and biotech industries
    to capture analysis in a common tabular data structure.
Calculate the area of triangles and polygons using the shoelace 
 formula. Area may be signed, taking into account path orientation, or unsigned, 
 ignoring path orientation. The shoelace formula is described at 
 <https://en.wikipedia.org/wiki/Shoelace_formula>.
Considering an (n x m) data matrix X, this package is based on the method proposed 
             by Gower, Groener, and Velden (2010) <doi:10.1198/jcgs.2010.07134>, and 
             utilize the  resulting matrices from the extended version of the NIPALS decomposition 
             to determine n triangles whose areas are used  to  visually  estimate the elements of
             a specific column of X. After a 90-degree rotation of the sample points, the triangles
             are drawn regarding the following points: 1.the origin of the axes; 2.the sample points;
             3. the vector endpoint representing some variable.
A pipeable, transparent implementation of areal weighted interpolation
    with support for interpolating multiple variables in a single function call.
    These tools provide a full-featured workflow for validation and estimation
    that fits into both modern data management (e.g. tidyverse) and spatial 
    data (e.g. sf) frameworks.
Many relevant applications in the environmental and socioeconomic 
    sciences use areal data, such as biodiversity checklists, agricultural statistics, 
    or socioeconomic surveys. For applications that surpass the spatial, temporal or 
    thematic scope of any single data source, data must be integrated from several 
    heterogeneous sources. Inconsistent concepts, definitions, or messy data tables 
    make this a tedious and error-prone process. 'arealDB' tackles those problems and 
    helps the user to integrate a harmonised databases of areal data. Read the paper
    at Ehrmann, Seppelt & Meyer (2020) <doi:10.1016/j.envsoft.2020.104799>.
Plot stacked areas and confidence bands as filled polygons, or add
  polygons to existing plots. A variety of input formats are supported,
  including vectors, matrices, data frames, formulas, etc.
Reads Arena <https://www.arenasimulation.com/> CSV output files and generates nice tables and plots. The package contains a Shiny App that can be used to interactively visualize Arena's results.
Generates data for challenging machine learning models in 'Arena'
    <https://arena.drwhy.ai> - an interactive web application. You can start
    the server with XAI (Explainable Artificial Intelligence) plots to be
    generated on-demand or precalculate and auto-upload data file beside
    shareable 'Arena' URL.
Adversarial random forests (ARFs) recursively partition data into 
    fully factorized leaves, where features are jointly independent. The 
    procedure is iterative, with alternating rounds of generation and 
    discrimination. Data becomes increasingly realistic at each round, until 
    original and synthetic samples can no longer be reliably distinguished. 
    This is useful for several unsupervised learning tasks, such as density
    estimation and data synthesis. Methods for both are implemented in this
    package. ARFs naturally handle unstructured data with mixed continuous and 
    categorical covariates. They inherit many of the benefits of random forests, 
    including speed, flexibility, and solid performance with default parameters. 
    For details, see Watson et al. (2022) <arXiv:2205.09435>.
Simulates, fits, and predicts long-memory and anti-persistent
	time series, possibly mixed with ARMA, regression, transfer-function
	components.
	Exact methods (MLE, forecasting, simulation) are used.
	Bug reports should be done via GitHub (at
	<https://github.com/JQVeenstra/arfima>), where the development version
	of this package lives; it can be installed using devtools.
Augmented Regression with General Online data (ARGO) for accurate estimation of influenza epidemics in United States on national level, regional level and state level. It replicates the method introduced in paper Yang, S., Santillana, M. and Kou, S.C. (2015) <doi:10.1073/pnas.1515373112>; Ning, S., Yang, S. and Kou, S.C. (2019) <doi:10.1038/s41598-019-41559-6>; Yang, S., Ning, S. and Kou, S.C. (2021) <doi:10.1038/s41598-021-83084-5>.
Supports the analysis of oceanographic data recorded by Argo autonomous drifting profiling floats. Functions are provided to (a) download and cache data files, (b) subset data in various ways, (c) handle quality-control flags and (d) plot the results according to oceanographic conventions. A shiny app is provided for easy exploration of datasets. The package is designed to work well with the 'oce' package, providing a wide range of processing capabilities that are particular to oceanographic analysis. See Kelley, Harbin, and Richards (2021) <doi:10.3389/fmars.2021.635922> for more on the scientific context and applications.
Create awesome 'Bootstrap 4' dashboards powered by 'Argon'.
   See more here <https://rinterface.github.io/argonDash/>.
R wrapper around the argon HTML library.
    More at <https://demos.creative-tim.com/argon-design-system/>.
Comprehensive set of tools for performing system identification of both linear and nonlinear dynamical systems directly from data. The Automatic Regression for Governing Equations (ARGOS) simplifies the complex task of constructing mathematical models of dynamical systems from observed input and output data, supporting various types of systems, including those described by ordinary differential equations. It employs optimal numerical derivatives for enhanced accuracy and employs formal variable selection techniques to help identify the most relevant variables, thereby enabling the development of predictive models for system behavior analysis.
Filters animal satellite tracking data obtained from the Argos system(<https://www.argos-system.org/>), following the algorithm described in Freitas et al (2008) <doi:10.1111/j.1748-7692.2007.00180.x>. It is especially indicated for telemetry studies of marine animals, where Argos locations are predominantly of low-quality.
A command line parser to
    be used with 'Rscript' to write "#!" shebang scripts that gracefully
    accept positional and optional arguments and automatically generate usage.
Cross-platform command-line argument parser written purely in R
    with no external dependencies. It is useful with the Rscript
    front-end and facilitates turning an R script into an executable script.
Random variate generation, density, CDF and quantile function 
  for the Argus distribution. Especially, it includes for random variate generation a
  flexible inversion method that is also fast in the varying parameter case.
  A Ratio-of-Uniforms method is provided as second alternative.
Perform the Adaptable Regularized Hotelling's T^2 test (ARHT) proposed by
    Li et al., (2016) <arXiv:1609.08725>. Both one-sample and two-sample mean test are available with
    various probabilistic alternative prior models. It contains a function to consistently
    estimate higher order moments of the population covariance spectral distribution using
    the spectral of the sample covariance matrix (Bai et al. (2010) <doi:10.1111/j.1467-842X.2010.00590.x>).
    In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately 
    with a given correlation matrix when the degrees of freedom are large.
Create videos from 'R Markdown' documents, or images and audio
    files. These images can come from image files or HTML slides, and the audio
    files can be provided by the user or computer voice narration can be created
    using 'Amazon Polly'. The purpose of this package is to allow users to create
    accessible, translatable, and reproducible lecture videos. See
    <https://aws.amazon.com/polly/> for more information.
It performs All-Resolutions Inference (ARI) on functional Magnetic Resonance Image (fMRI) data. As a main feature, 
 it estimates lower bounds for the proportion of active voxels in a set of clusters as, for example, given by a cluster-wise analysis. 
 The method is described in Rosenblatt, Finos, Weeda, Solari, Goeman (2018) <doi:10.1016/j.neuroimage.2018.07.060>.
Implements an efficient O(n) algorithm based on bucket-sorting for 
    fast computation of standard clustering comparison measures. Available measures
    include adjusted Rand index (ARI), normalized information distance (NID), 
    normalized mutual information (NMI), adjusted mutual information (AMI), 
    normalized variation information (NVI) and entropy, as described in Vinh et al (2009) 
    <doi:10.1145/1553374.1553511>. Include AMI (Adjusted Mutual Information) since version 0.1.2, 
    a modified version of ARI (MARI), as described in Sundqvist et al. <doi:10.1007/s00180-022-01230-7> 
    and simple Chi-square distance since version 1.0.0.
Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) <doi:10.1007/s41096-022-00128-3>.
Estimating and analyzing auto regressive integrated moving average
    (ARIMA) models. The primary function in this package is arima(), which fits 
    an ARIMA model to univariate time series data using a random restart
    algorithm. This approach frequently leads to models that have model 
    likelihood greater than or equal to that of the likelihood obtained by 
    fitting the same model using the arima() function from the 'stats' package. 
    This package enables proper optimization of model likelihoods, which is a 
    necessary condition for performing likelihood ratio tests. This package 
    relies heavily on the source code of the arima() function of the 'stats' 
    package. For more information, please see Jesse Wheeler and Edward L. 
    Ionides (2023) <arXiv:2310.01198>.
Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) <doi:10.1016/S0925-2312(01)00702-0>.
Flat text files provide a robust, compressible, and portable
  way to store tables from databases.  This package provides convenient
  functions for exporting tables from relational database connections
  into compressed text files and streaming those text files back into
  a database without requiring the whole table to fit in working memory.
A dependency-free collection of simple functions for cleaning
    rectangular data. This package allows to detect, count and replace
    values or discard rows/columns using a predicate function. In
    addition, it provides tools to check conditions and return informative
    error messages.
Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
Two steps variable selection procedure in a context of high-dimensional dependent data
  but few observations. First step is dedicated to eliminate dependence between variables (clustering
  of variables, followed by factor analysis inside each cluster).
  Second step is a variable selection using by aggregation of adapted methods.
  Bastien B., Chakir H., Gegout-Petit A., Muller-Gueudin A., Shi Y.
  A statistical methodology to select covariates in high-dimensional data under dependence.
  Application to the classification of genetic profiles associated with outcome of a non-small-cell
  lung cancer treatment. 2018. <https://hal.archives-ouvertes.fr/hal-01939694>.
The real-life time series data are hardly pure linear or nonlinear. Merging a linear time series model like the autoregressive moving average (ARMA) model with a nonlinear neural network model such as the Long Short-Term Memory (LSTM) model can be used as a hybrid model for more accurate modeling purposes. Both the autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models can be implemented. Details can be found in Box et al. (2015, ISBN: 978-1-118-67502-1) and Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>.
An efficient 'Rcpp' implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) <doi:10.2307/2986138>. This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density.
Arnold Schwarzenegger movie weekend box office records from
    1982-2014
Estimates the covariate-adjusted Receiver Operating Characteristic (AROC) curve and pooled (unadjusted) ROC curve by different methods. Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018) <arXiv:1806.00473>. NOTE: We have created a new package, 'ROCnReg', with more functionalities. It also implements all the methods included in 'AROC'. We, therefore, recommend using 'ROCnReg' ('AROC' will no longer be maintained).
A cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system.  The only parameter that limits the number of chips that can be processed is the amount of available disk space.  The Aroma Framework has successfully been used in studies to process tens of thousands of arrays.  This package has actively been used since 2006.
DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities.  Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets).  In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit.  This speeds up the read substantially.  This package is supporting the Aroma framework and should not be used elsewhere.)
Methods for analyzing DNA copy-number data.  Specifically,
  this package implements the multi-source copy-number normalization (MSCN)
  method for normalizing copy-number data obtained on various platforms and
  technologies.  It also implements the TumorBoost method for normalizing
  paired tumor-normal SNP data.
Core methods and classes used by higher-level 'aroma.*' packages
        part of the Aroma Project, e.g. 'aroma.affymetrix' and 'aroma.cn'.
Tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2021 <doi:10.1002/ajpa.24340>).
Contains functions for retrieving, managing and analysing air quality and weather data from Regione Lombardia open database (<https://www.dati.lombardia.it/>).
    Data are collected by ARPA Lombardia (Lombardia Environmental Protection Agency), Italy, through its ground monitoring network.
    See the webpage <https://www.arpalombardia.it/> for further information on ARPA Lombardia's activities and history.
    Data quality (e.g. missing values, exported values, graphical mapping) has been checked involving members of the ARPA Lombardia's office for air quality control.
    The package makes available observations since 1989 (for weather) and 1968 (for air quality) and are updated with daily frequency by the regional agency.
    Full description of the package can be retrieved in the companion paper Maranzano \& Algieri (2024), "ARPALData: an R package for retrieving and analyzing air quality 
    and weather data from ARPA Lombardia (Italy)", Environmental and Ecological Statistics, <doi:10.1007/s10651-024-00599-6>.
Left, right or interval censored mixed-effects linear model with autoregressive errors of order p or DEC correlation structure using the type-EM algorithm. The error distribution  can be Normal or t-Student. It provides the  parameter estimates, the standard errors  and prediction of future observations (available only for the normal case). Olivari et all (2021) <doi:10.1080/10543406.2020.1852246>.
Tools for simulating data generated by direct observation
    recording. Behavior streams are simulated based on an alternating renewal
    process, given specified distributions of event durations and interim
    times. Different procedures for recording data can then be applied to the
    simulated behavior streams. Functions are provided for the following
    recording methods: continuous duration recording, event counting, momentary
    time sampling, partial interval recording, whole interval recording, and
    augmented interval recording.
Provides convenience functions for programming with
    'magrittr' pipes. Conditional pipes, a string prefixer and a function
    to pipe the given object into a specific argument given by character
    name are currently supported. It is named after the dadaist Hans Arp,
    a friend of Rene Magritte.
Functions for Arps decline-curve analysis on oil and gas data. Includes exponential, hyperbolic, harmonic, and hyperbolic-to-exponential models as well as the preceding with initial curtailment or a period of linear rate buildup. Functions included for computing rate, cumulative production, instantaneous decline, EUR, time to economic limit, and performing least-squares best fits.
Fast generators and iterators for permutations, combinations,
    integer partitions and compositions. The arrangements are in
    lexicographical order and generated iteratively in a memory efficient manner. 
    It has been demonstrated that 'arrangements' outperforms most existing
    packages of similar kind. Benchmarks could be found at
    <https://randy3k.github.io/arrangements/articles/benchmark.html>.
High performance variant of apply() for a fixed set of functions.
  Considerable speedup of this implementation is a trade-off for universality: user defined
  functions cannot be used with this package. However, about 20 most currently employed
  functions are available for usage. They can be divided in three types:
  reducing functions (like mean(), sum() etc., giving a scalar when applied to a vector),
  mapping function (like normalise(), cumsum() etc., giving a vector of the same length
  as the input vector) and finally, vector reducing function (like diff() which produces
  result vector of a length different from the length of input vector).
  Optional or mandatory additional arguments required by some functions
  (e.g. norm type for norm()) can be
  passed as named arguments in '...'.
Some convenient functions to work with arrays.
'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language
    development platform for in-memory data. It specifies a standardized
    language-independent columnar memory format for flat and hierarchical data,
    organized for efficient analytic operations on modern hardware. This
    package provides an interface to the 'Arrow C++' library.
Adaptive Rejection Sampling, Original version.
A method for quantifying resilience after a stress event. A set of functions
    calculate the area of resilience that is created by the departure of baseline 
    'y' (i.e., robustness) and the time taken 'x' to return to baseline 
    (i.e., rapidity) after a stress event using the Cartesian coordinates of the 
    data. This package has the capability to calculate areas of resilience,
    growth, and cases in which resilience is not achieved 
    (e.g., diminished performance without return to baseline).
An Arsenal of 'R' functions for large-scale statistical summaries,
  which are streamlined to work within the latest reporting tools in 'R' and
  'RStudio' and which use formulas and versatile summary statistics for summary
  tables and models. The primary functions include tableby(), a Table-1-like
  summary of multiple variable types 'by' the levels of one or more categorical
  variables; paired(), a Table-1-like summary of multiple variable types paired across
  two time points; modelsum(), which performs simple model fits on one or more endpoints
  for many variables (univariate or adjusted for covariates);
  freqlist(), a powerful frequency table across many categorical variables;
  comparedf(), a function for comparing data.frames; and
  write2(), a function to output tables to a document.
An implementation of the Aligned Rank Transform technique for
    factorial analysis (see references below for details) including models with
    missing terms (unsaturated factorial models). The function first
    computes a separate aligned ranked response variable for each effect of the
    user-specified model, and then runs a classic ANOVA on each of the aligned
    ranked responses. For further details, see Higgins, J. J. and Tashtoush, S.
    (1994). An aligned rank transform test for interaction. Nonlinear World 1
    (2), pp. 201-211. Wobbrock, J.O., Findlater, L., Gergle, D. and
    Higgins,J.J. (2011). The Aligned Rank Transform for nonparametric factorial
    analyses using only ANOVA procedures. Proceedings of the ACM Conference on
    Human Factors in Computing Systems (CHI '11). New York: ACM Press, pp.
    143-146. <doi:10.1145/1978942.1978963>.
Fit and simulate ARTFIMA. Theoretical autocovariance function and spectral density function for stationary ARTFIMA.
For instructions, check <https://github.com/Hzhang-ouce/ARTofR>. This is a wrapper of 'bannerCommenter', for inserting neat comments, headers and dividers.
The aligned rank transform for nonparametric
    factorial ANOVAs as described by Wobbrock,
    Findlater, Gergle, and Higgins (2011) <doi:10.1145/1978942.1978963>. 
    Also supports aligned rank transform contrasts as described
    by Elkin, Kay, Higgins, and Wobbrock (2021)
    <doi:10.1145/3472749.3474784>.
Create data that displays generative art when mapped into a 'ggplot2' plot. Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using 'ggplot2' for generative art.
Provides algorithms for creating artworks in the 'ggplot2' language that incorporate some form of randomness.
Provides the infrastructure for representing, manipulating and analyzing 
  transaction data and patterns (frequent itemsets and association rules). 
  Also provides C implementations of the association mining algorithms Apriori and Eclat. 
  Hahsler, Gruen and Hornik (2005) <doi:10.18637/jss.v014.i15>.
Provides the infrastructure for association rule-based classification including the algorithms 
  CBA, CMAR, CPAR, C4.5, FOIL, PART, PRM, RCAR, and RIPPER to build associative classifiers. 
  Hahsler et al (2019) <doi:10.32614/RJ-2019-048>.
NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets and NB-precise rules. Michael Hahsler (2006) <doi:10.1007/s10618-005-0026-2>.
Add-on for arules to handle and mine frequent sequences.
    Provides interfaces to the C++ implementation of cSPADE by  
    Mohammed J. Zaki.
Extends package 'arules' with various visualization techniques for association rules and itemsets. The package also includes several interactive visualizations for rule exploration. Michael Hahsler (2017) <doi:10.32614/RJ-2017-047>.
Parse Autonomous Recording Unit (ARU) data and for sub-sampling recordings. 
    Extract Metadata from your recordings, select a subset of recordings for 
    interpretation, and prepare files for processing on the 
    'WildTrax' <https://wildtrax.ca/> platform. Read and process metadata 
    from recordings collected using the SongMeter and BAR-LT types of ARUs.
An interface to the API for 'arXiv',
    a repository of electronic preprints for
    computer science, mathematics, physics, quantitative biology,
    quantitative finance, and statistics.
Data sets are referred to in the text "Applied Survival Analysis Using R"
 by Dirk F. Moore, Springer, 2016, ISBN: 978-3-319-31243-9, <DOI:10.1007/978-3-319-31245-3>.
Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R.  CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.
Datasets to be used primarily in conjunction with Ascent
    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.
Coerce R object to 'asciidoc', 'txt2tags',
    'restructuredText', 'org', 'textile' or 'pandoc' syntax.
    Package comes with a set of drivers for 'Sweave'.
Record 'asciicast' screen casts from R scripts. Convert them
    to animated SVG images, to be used in 'README' files, or blog posts.
    Includes 'asciinema-player' as an 'HTML' widget, and an 'asciicast'
    'knitr' engine, to embed 'ascii' screen casts in 'Rmarkdown'
    documents.
Create ASCII line graphs of a time series directly on
    your terminal in an easy way. There are some configurations you
    can add to make the plot the way you like. This project was
    inspired by the original 'asciichart' package by Igor Kroitor.
An ASCII ruler is for measuring text and is especially useful for sequence analysis. Included in this package are methods to create ASCII rulers and associated GenBank sequence blocks, multi-column text displays that make it easy for viewers to locate nucleotides by position.
Lets you open a fixed-width ASCII file (.txt or
    .dat) that has an accompanying setup file (.sps or .sas). These file
    combinations are sometimes referred to as .txt+.sps, .txt+.sas,
    .dat+.sps, or .dat+.sas. This will only run in a txt-sps or txt-sas
    pair in which the setup file contains instructions to open that text
    file. It will NOT open other text files, .sav, .sas, or .por data
    files. Fixed-width ASCII files with setup files are common in older
    (pre-2000) government data.
A spatiotemporal model that simulates the spread of Ascochyta
    blight in chickpea fields based on location-specific weather conditions.
    This model is adapted from a model developed by Diggle et al. (2002)
   <doi:10.1094/PHYTO.2002.92.10.1110> for simulating the spread of anthracnose
   in a lupin field.
Package runs simulations for adaptive seamless designs with and without early outcomes 
                           for treatment selection and subpopulation type designs.
A simple driver that reads binary data created by the ASD Inc.
    portable spectrometer instruments, such as the FieldSpec (for more information,
    see <http://www.asdi.com/products/fieldspec-spectroradiometers>). Spectral data
    can be extracted from the ASD files as raw (DN), white reference, radiance, or
    reflectance. Additionally, the metadata information contained in the ASD file
    header can also be accessed.
The Australian Statistical Geography Standard ('ASGS') is 
  a set of shapefiles by the Australian Bureau of Statistics. This package
  provides an interface to those shapefiles, as well as methods for converting
  coordinates to shapefiles.
David Scott's ASH routines ported from S-PLUS to R.
Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The 'ashapesampler' package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al. (2024) <doi:10.1101/2024.01.09.574919>.
The R package 'ashr' implements an Empirical Bayes
    approach for large-scale hypothesis testing and false discovery
    rate (FDR) estimation based on the methods proposed in
    M. Stephens, 2016, "False discovery rates: a new deal",
    <DOI:10.1093/biostatistics/kxw041>. These methods can be applied
    whenever two sets of summary statistics---estimated effects and
    standard errors---are available, just as 'qvalue' can be applied
    to previously computed p-values. Two main interfaces are
    provided: ash(), which is more user-friendly; and ash.workhorse(),
    which has more options and is geared toward advanced users. The
    ash() and ash.workhorse() also provides a flexible modeling
    interface that can accommodate a variety of likelihoods (e.g.,
    normal, Poisson) and mixture priors (e.g., uniform, normal).
Gives some hypothesis test functions (sign test, median and other quantile tests, Wilcoxon signed rank test, coefficient of variation test, test of normal variance, test on weighted sums of Poisson [see Fay and Kim <doi:10.1002/bimj.201600111>], sample size for t-tests with different variances and non-equal n per arm, Behrens-Fisher test, nonparametric ABC intervals, Wilcoxon-Mann-Whitney test [with effect estimates and confidence intervals, see Fay and Malinovsky <doi:10.1002/sim.7890>], two-sample melding tests [see Fay, Proschan, and Brittain <doi:10.1111/biom.12231>], one-way ANOVA allowing var.equal=FALSE [see Brown and Forsythe, 1974, Biometrics]), prevalence confidence intervals that adjust for sensitivity and specificity [see Lang and Reiczigel, 2014 <doi:10.1016/j.prevetmed.2013.09.015>] or Bayer, Fay, and Graubard, 2023 <doi:10.48550/arXiv.2205.13494>). The focus is on hypothesis tests that have compatible confidence intervals, but some functions only have confidence intervals (e.g., prevSeSp).
'Asio' is a cross-platform C++ library for network and low-level
 I/O programming that provides developers with a consistent asynchronous model
 using a modern C++ approach. It is also included in Boost but requires linking
 when used with Boost. Standalone it can be used header-only (provided a recent
 compiler). 'Asio' is written and maintained by Christopher M. Kohlhoff, and
 released under the 'Boost Software License', Version 1.0.
A chat package connecting to API endpoints by 'OpenAI' 
  (<https://platform.openai.com/>) to answer questions (about R).
Cross-platform utilities for prompting the user for credentials or a 
    passphrase, for example to authenticate with a server or read a protected key.
    Includes native programs for MacOS and Windows, hence no 'tcltk' is required. 
    Password entry can be invoked in two different ways: directly from R via the 
    askpass() function, or indirectly as password-entry back-end for 'ssh-agent' 
    or 'git-credential' via the SSH_ASKPASS and GIT_ASKPASS environment variables.
    Thereby the user can be prompted for credentials or a passphrase if needed 
    when R calls out to git or ssh.
Provides an interface to the algorithm selection benchmark library
    at <http://www.aslib.net> and the 'LLAMA' package
    (<https://cran.r-project.org/package=llama>) for building
    algorithm selection models; see Bischl et al. (2016)
    <doi:10.1016/j.artint.2016.04.003>.
Performs linear regression with respect to a data-driven convex loss function that is chosen to minimize the asymptotic covariance of the resulting M-estimator. The convex loss function is estimated in 5 steps: (1) form an initial OLS (ordinary least squares) or LAD (least absolute deviation) estimate of the regression coefficients; (2) use the resulting residuals to obtain a kernel estimator of the error density; (3) estimate the score function of the errors by differentiating the logarithm of the kernel density estimate; (4) compute the L2 projection of the estimated score function onto the set of decreasing functions; (5) take a negative antiderivative of the projected score function estimate. Newton's method (with Hessian modification) is then used to minimize the convex empirical risk function. Further details of the method are given in Feng et al. (2024) <doi:10.48550/arXiv.2403.16688>.
Functions for Accurate and Speedy linkage map construction, manipulation and diagnosis of Doubled Haploid, Backcross and Recombinant Inbred 'R/qtl' objects. This includes extremely fast linkage map clustering and optimal marker ordering using 'MSTmap' (see Wu et al.,2008).
Adaptive Sparse Multi-block Partial Least Square, a supervised algorithm, is an extension of the Sparse Multi-block Partial Least Square, which allows different quantiles to be used in different blocks of different partial least square components to decide the proportion of features to be retained. The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome.
Implements several tools that are used in animal social network analysis, as described in Whitehead (2007) Analyzing Animal Societies <University of Chicago Press> and Farine & Whitehead (2015) <doi: 10.1111/1365-2656.12418>. In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data.
A collection of functions for computing centrographic
        statistics (e.g., standard distance, standard deviation
        ellipse, standard deviation box) for observations taken at
        point locations. Separate plotting functions have been
        developed for each measure. Users interested in writing results
        to ESRI shapefiles can do so by using results from 'aspace'
        functions as inputs to the convert.to.shapefile() and
        write.shapefile() functions in the 'shapefiles' library. We intend to
        provide 'terra' integration for geographic data in a future release.
        The 'aspace' package was originally conceived to aid in the analysis of
        spatial patterns of travel behaviour (see Buliung and Remmel 2008
        <doi:10.1007/s10109-008-0063-7>).
Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models.
Tools for the analysis and visualization of bilateral asymmetry in parasitic infections.
Perform one-dimensional spline regression with automatic knot selection.
      This package uses a penalized approach to select the most relevant knots.
      B-splines of any degree can be fitted. More details in 'Goepp et al. (2018)',
      "Spline Regression with Automatic Knot Selection", <arXiv:1808.01770>.
R codes for the (adaptive) Sum of Powered Score ('SPU' and 'aSPU')
    tests, inverse variance weighted Sum of Powered score ('SPUw' and 'aSPUw') tests
    and gene-based and some pathway based association tests (Pathway based Sum of
    Powered Score tests ('SPUpath'), adaptive 'SPUpath' ('aSPUpath') test, 'GEEaSPU'
    test for multiple traits - single 'SNP' (single nucleotide polymorphism)
    association in generalized estimation equations, 'MTaSPUs' test for multiple
    traits - single 'SNP' association with Genome Wide Association Studies ('GWAS')
    summary statistics, Gene-based Association Test that uses an extended 'Simes'
    procedure ('GATES'), Hybrid Set-based Test ('HYST') and extended version
    of 'GATES' test for pathway-based association testing ('GATES-Simes'). ).
    The tests can be used with genetic and other data sets with covariates. The
    response variable is binary or quantitative. Summary; (1) Single trait-'SNP' set
    association with individual-level data ('aSPU', 'aSPUw', 'aSPUr'), (2) Single trait-'SNP'
    set association with summary statistics ('aSPUs'), (3) Single trait-pathway
    association with individual-level data ('aSPUpath'), (4) Single trait-pathway
    association with summary statistics ('aSPUsPath'), (5) Multiple traits-single
    'SNP' association with individual-level data ('GEEaSPU'), (6) Multiple traits-
    single 'SNP' association with summary statistics ('MTaSPUs'), (7) Multiple traits-'SNP' set association with summary statistics('MTaSPUsSet'), (8) Multiple traits-pathway association with summary statistics('MTaSPUsSetPath').
Assists in automating the selection of terms to include in mixed models when  
  'asreml' is used to fit the models. Procedures are available for choosing models that 
  conform to the hierarchy or marginality principle, for fitting and choosing between 
  two-dimensional spatial models using correlation, natural cubic smoothing spline and 
  P-spline models. A history of the fitting of a sequence of models is kept in a data frame. 
  Also used to compute functions and contrasts of, to investigate differences between and 
  to plot predictions obtained using any model fitting function. The content  falls into 
  the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model 
  selection and description functions, (iv) Model diagnostics and simulation functions, 
  (v) Prediction production and presentation functions, (vi) Response transformation 
  functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions 
  (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a 
  computationally efficient algorithm for fitting a wide range of linear mixed models using 
  Residual Maximum Likelihood. It is a commercial package and a license for it can be 
  purchased from 'VSNi' <https://vsni.co.uk/> as 'asreml-R', who will supply a zip file 
  for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed 
  for functions that are methods for 'alldiffs'  and 'data.frame' objects. The package 
  'asremPlus' can also be installed from <http://chris.brien.name/rpackages/>.
Presents a series of molecular and genetic routines in the R
    environment with the aim of assisting in analytical pipelines before
    and after the use of 'asreml' or another library to perform analyses such
    as Genomic Selection or Genome-Wide Association Analyses. 
    Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) 
    <https://asreml.kb.vsni.co.uk/wp-content/uploads/sites/3/ASRgenomics_Manual.pdf>.
Functions to model and decompose time series into principal components using singular spectrum analysis (de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>; de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>).
Lightweight validation tool for checking function arguments and 
  validating data analysis scripts. This is an alternative to stopifnot() from 
  the 'base' package  and to assert_that() from the 'assertthat' package. It 
  provides more informative error messages and facilitates debugging.
Simple, flexible, assertions on data.frame or data.table objects with verbose output for vetting. While other assertion packages apply towards more general use-cases, assertable is tailored towards tabular data. It includes functions to check variable names and values, whether the dataset contains all combinations of a given set of unique identifiers, and whether it is a certain length. In addition, assertable includes utility functions to check the existence of target files and to efficiently import multiple tabular data files into one data.table.
Provides simple assertions with sensible defaults and customisable error messages. 
  It offers convenient assertion call wrappers and a general assert function that can handle any condition.
  Default error messages are user friendly and easily customized with inline code evaluation and styling powered by the 'cli' package.
Provides functionality to assert conditions
    that have to be met so that errors in data used in
    analysis pipelines can fail quickly. Similar to
    'stopifnot()' but more powerful, friendly, and easier
    for use in pipelines.
An extension to stopifnot() that makes it easy to declare 
    the pre and post conditions that you code should satisfy, while also 
    producing friendly error messages so that your users know what's gone
    wrong.
Provides assessment tools for regression models with discrete and semicontinuous outcomes proposed in Yang (2023) <doi:10.48550/arXiv.2308.15596>. It calculates the double probability integral transform (DPIT) residuals, constructs QQ plots of residuals and the ordered curve for assessing mean structures.
Easy and quick testing of customizable asset allocation strategies.
    Users can rely on their own data, or have the package automatically
    download data from Yahoo Finance (<https://finance.yahoo.com/>). Several 
    pre-loaded portfolios with data are available, including some which are 
    discussed in Faber (2015, ISBN:9780988679924).
Functions for the estimation of intra- and inter-cohort correlations in the Vasicek credit portfolio model. For intra-cohort correlations, the package covers the two method of moments estimators of Gordy (2000) <doi:10.1016/S0378-4266(99)00054-0>, the method of moments estimator of Lucas (1995) <https://jfi.pm-research.com/content/4/4/76> and a Binomial approximation extension of this approach. Moreover, the maximum likelihood estimators of Gordy and Heitfield (2010) <http://elsa.berkeley.edu/~mcfadden/e242_f03/heitfield.pdf> and Duellmann and Gehde-Trapp (2004) <http://hdl.handle.net/10419/19729> are implemented. For inter-cohort correlations, the method of moments estimator of Bluhm and Overbeck (2003) <doi:10.1007/978-3-642-59365-9_2>/Bams et al. (2016) <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2676595> is provided and the maximum likelihood estimators comprise the approaches of Gordy and Heitfield (2010)/Kalkbrener and Onwunta (2010) <ISBN: 978-1906348250> and Pfeuffer et al. (2020). Bootstrap and Jackknife procedures for bias correction are included as well as the method of moments estimator of Frei and Wunsch (2018) <doi:10.21314/JCR.2017.231> for auto-correlated time series.
Calculates the optimal price of assets (such as
	airline flight seats, hotel room bookings) whose value
	becomes zero after a fixed ``expiry date''.  Assumes
	potential customers arrive (possibly in groups) according
	to a known inhomogeneous Poisson process.  Also assumes a
	known time-varying elasticity of demand (price sensitivity)
	function.  Uses elementary techniques based on ordinary
	differential equations.  Uses the package deSolve to effect
	the solution of these differential equations.
Use Monte-Carlo and K-fold cross-validation coupled with machine-
    learning classification algorithms to perform population assignment, with
    functionalities of evaluating discriminatory power of independent training
    samples, identifying informative loci, reducing data dimensionality for genomic
    data, integrating genetic and non-genetic data, and visualizing results.
Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) <doi:10.1111/2041-210X.12229> as described in Ma, C. et al. (2020) <doi:10.1111/2041-210X.13426>.
Fit various smoothing spline models. Includes an ssr() function for smoothing 
    spline regression, an nnr() function for nonparametric nonlinear regression, an snr() 
    function for semiparametric nonlinear regression, an slm() function for semiparametric 
    linear mixed-effects models, and an snm() function for semiparametric nonlinear 
    mixed-effects models. See Wang (2011) <doi:10.1201/b10954> for an overview.
Clinical trial design for subgroup selection in three-stage group
             sequential trial as described in Lai, Lavori and Liao (2014,
	     <doi:10.1016/j.cct.2014.09.001>). Includes facilities for design,
	     exploration and analysis of such trials. An implementation of
	     the initial DEFUSE-3 trial is also provided as a vignette.
When doing association analysis one does not always have the 
   genotypes for the control population.  In such cases it may be 
   necessary to fall back on frequency based tests using well known sources 
   for the frequencies in the control population, for instance, from the 
   1000 Genomes Project.  The Allele Frequency Comparison ('AssocAFC') package 
   performs multiple rare variant association analyses in both population and 
   family-based GWAS (Genome-Wide Association Study) designs. It includes 
   three score tests that are based on the difference of the sum of allele 
   frequencies between cases and controls.  Two of these tests, Wcorrected() 
   and Wqls(), are collapsing-based tests and suffer from having protective 
   and risk variants. The third test, afcSKAT(), is a score test that 
   overcomes the mix of SNP (Single-Nucleotide Polymorphism) effect 
   directions. For more details see 
   Saad M and Wijsman EM (2017) <doi:10.1093/bib/bbx107>.
An iterative implementation of a recursive binary partitioning algorithm to measure pairwise dependence with a modular design that allows user specification of the splitting logic and stop criteria. Helper functions provide suggested versions of both and support visualization and the computation of summary statistics on final binnings. For a complete description of the functionality and algorithm, see Salahub and Oldford (2023) <arxiv:2311.08561>.
Implements several new association indices that can control for various types of errors. Also includes existing association indices and functions for simulating the effects of different rates of error on estimates of association strength between individuals using each method.
Some procedures including EIGENSTRAT (a procedure for detecting and correcting 
	for population stratification through searching for the eigenvectors in genetic association 
	studies), PCoC (a procedure for correcting for population stratification through calculating 
	the principal coordinates and the clustering of the subjects), Tracy-Widom test (a procedure 
	for detecting the significant eigenvalues of a matrix), distance regression (a procedure for 
	detecting the association between a distance matrix and some independent variants of interest), 
	single-marker test (a procedure for identifying the association between the genotype at a 
	biallelic marker and a trait using the Wald test or the Fisher's exact test), MAX3 (a procedure 
	for testing for the association between a single nucleotide polymorphism and a binary phenotype 
	using the maximum value of the three test statistics derived for the recessive, additive, and 
	dominant models), nonparametric trend test (a procedure for testing for the association between 
	a genetic variant and a non-normal distributed quantitative trait based on the nonparametric 
	risk), and nonparametric MAX3 (a procedure for testing for the association between a biallelic 
	single nucleotide polymorphism and a quantitative trait using the maximum value of the three 
	nonparametric trend tests derived for the recessive, additive, and dominant models), which are 
	commonly used in genetic association studies. To cite this package in publications use: 
	Lin Wang, Wei Zhang, and Qizhai Li. AssocTests: An R Package for Genetic Association Studies. 
	Journal of Statistical Software. 2020; 94(5): 1-26.
Functions to calculate the assortment of vertices in social networks.  This can be measured on both weighted and binary networks, with discrete or continuous vertex values.
Fits a model to adjust and consider additional variations in three dimensions of age groups, time, and space on residuals excluded from a prediction model that have residual such as: linear regression, mixed model and so on. Details are given in Foreman et al. (2015) <doi:10.1186/1478-7954-10-1>.
Enable translation of a tiny subset of R to C++. The user has to define a R function which gets translated. For a full list of possible functions check the documentation. After translation an R function is returned which is a shallow wrapper around the C++ code. Alternatively an external pointer to the C++ function is returned to the user. The intention of the package is to generate fast functions which can be used as ode-system or during optimization.
Aster models (Geyer, Wagenius, and Shaw, 2007,
    <doi:10.1093/biomet/asm030>; Shaw, Geyer, Wagenius, Hangelbroek, and
    Etterson, 2008, <doi:10.1086/588063>; Geyer, Ridley, Latta, Etterson,
    and Shaw, 2013, <doi:10.1214/13-AOAS653>) are exponential family
    regression models for life
    history analysis.  They are like generalized linear models except that
    elements of the response vector can have different families (e. g.,
    some Bernoulli, some Poisson, some zero-truncated Poisson, some normal)
    and can be dependent, the dependence indicated by a graphical structure.
    Discrete time survival analysis, life table analysis,
    zero-inflated Poisson regression, and
    generalized linear models that are exponential family (e. g., logistic
    regression and Poisson regression with log link) are special cases.
    Main use is for data in which there is survival over discrete time periods
    and there is additional data about what happens conditional on survival
    (e. g., number of offspring).  Uses the exponential family canonical
    parameterization (aster transform of usual parameterization).
    There are also random effects versions of these models.
Aster models are exponential family regression models for life
    history analysis.  They are like generalized linear models except that
    elements of the response vector can have different families (e. g.,
    some Bernoulli, some Poisson, some zero-truncated Poisson, some normal)
    and can be dependent, the dependence indicated by a graphical structure.
    Discrete time survival analysis, zero-inflated Poisson regression, and
    generalized linear models that are exponential family (e. g., logistic
    regression and Poisson regression with log link) are special cases.
    Main use is for data in which there is survival over discrete time periods
    and there is additional data about what happens conditional on survival
    (e. g., number of offspring).  Uses the exponential family canonical
    parameterization (aster transform of usual parameterization).
    Unlike the aster package, this package does dependence groups (nodes of
    the graph need not be conditionally independent given their predecessor
    node), including multinomial and two-parameter normal as families.  Thus
    this package also generalizes mark-capture-recapture analysis.
Provides basic functionalities to calculate the position of
    satellites given a known state vector. The package includes implementations
    of the SGP4 and SDP4 simplified perturbation models to propagate orbital
    state vectors, as well as utilities to read TLE files and convert coordinates
    between different frames of reference. Several of the functionalities of the
    package (including the high-precision numerical orbit propagator) require
    the coefficients and data included in the 'asteRiskData' package, available
    in a 'drat' repository. To install this data package, run 
    'install.packages("asteRiskData", repos="https://rafael-ayala.github.io/drat/")'.
    Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) <https://celestrak.org/NORAD/documentation/spacetrk.pdf>.
    David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) <doi:10.2514/6.2006-6753>.
    Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) <doi:10.2514/1.9161>.
Data sets and examples from National Health and Nutritional Examination Survey (NHANES).
Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis.
A collection of 19 datasets from contemporary astronomical research.  They are described the textbook `Modern Statistical Methods for Astronomy with R Applications' by Eric D. Feigelson and G. Jogesh Babu (Cambridge University Press, 2012, Appendix C) or on the website of Penn State's Center for Astrostatistics (http://astrostatistics.psu.edu/datasets).  These datasets can be used to exercise methodology involving: density estimation; heteroscedastic measurement errors; contingency tables; two-sample hypothesis tests; spatial point processes; nonlinear regression; mixture models; censoring and truncation; multivariate analysis; classification and clustering; inhomogeneous Poisson processes; periodic and stochastic time series analysis.
Miscellaneous astronomy functions, utilities, and data.
Contains data sets and scripts for analyzing time series in both the frequency and time domains including state space modeling as well as supporting the texts Time Series Analysis and Its Applications: With R Examples (5th ed coming), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2017, <DOI:10.1007/978-3-319-52452-8>, and Time Series: A Data Analysis Approach Using R. Chapman-Hall, 2019, <DOI:10.1201/9780429273285>.
Provides the ASUS procedure for estimating a high dimensional sparse parameter in the presence of auxiliary data that encode side information on sparsity. It is a robust data combination procedure in the sense that even when pooling non-informative auxiliary data ASUS would be at least as efficient as competing soft thresholding based methods that do not use auxiliary data. 
    For more information, please see the paper Adaptive Sparse Estimation with Side Information by Banerjee, Mukherjee and Sun (JASA 2020).
The efficient Markov chain Monte Carlo estimation of stochastic volatility models with and without leverage (asymmetric and symmetric stochastic volatility models). Further, it computes the logarithm of the likelihood given parameters using particle filters.
A collection of functions related to density estimation by using Chen's (2000) idea. Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull. For details see Chen (2000), Scaillet (2004) <doi:10.1080/10485250310001624819> and Khan and Akbar.
Data on Asylum and Resettlement for the UK,
    provided by the Home Office <https://www.gov.uk/government/statistical-data-sets/immigration-system-statistics-data-tables>.
Computes asymmetric LD measures (ALD) for multi-allelic genetic data. These measures are identical to the correlation measure (r) for bi-allelic data.
Response surface designs (RSDs) are widely used for Response Surface Methodology (RSM) based optimization studies, which aid in exploring the relationship between a group of explanatory variables and one or more response variable(s) (G.E.P. Box and K.B. Wilson (1951), "On the experimental attainment of optimum conditions" ; M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI: 10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts".). Second order rotatable designs are the most prominent and popular class of designs used for process and product optimization trials but it is suitable for situations when all the number of levels for each factor is the same. In many practical situations, RSDs with asymmetric levels (J.S. Mehta and M.N. Das (1968). "Asymmetric rotatable designs and orthogonal transformations" ; M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2020) <DOI: 10.1080/02664763.2020.1864817>. "Sequential asymmetric third order rotatable designs (SATORDs)" .) are more suitable as these designs explore more regions in the design space.This package contains functions named Asords() ,CCD_coded(), CCD_original(), SORD_coded() and SORD_original() for generating asymmetric/symmetric RSDs along with the randomized layout. It also contains another function named Pred.var() for generating the variance of predicted response as well as the moment matrix based on a second order model.
Multidimensional scaling models and methods for the visualization and analysis of asymmetric proximity data <doi:10.1111/j.2044-8317.1996.tb01078.x>. An asymmetric data matrix has the same number of rows and columns, and these rows and columns refer to the same set of objects. At least some elements in the upper-triangle are different from the corresponding elements in the lower triangle. An example of an asymmetric matrix is a  student migration table, where the rows correspond to the countries of origin of the students and the columns to the destination countries. This package provides algorithms for three multidimensional scaling models. These are the slide-vector model <doi:10.1007/BF02294474>, a scaling model with unique dimensions and the asymscal model for asymmetric multidimensional scaling. Furthermore, a heat map for skew-symmetric data, and the decomposition of asymmetry are provided for the exploratory analysis of asymmetric tables.
Provides functions and examples for the weak and strong density asymmetry measures in the articles: "A measure of asymmetry", Patil, Patil and Bagkavos (2012) <doi:10.1007/s00362-011-0401-6> and "A measure of asymmetry based on a new necessary and sufficient condition for symmetry", Patil, Bagkavos and Wood (2014) <doi:10.1007/s13171-013-0034-z>. The measures provided here are useful for quantifying the asymmetry of the shape of a density of a random variable. The package facilitates implementation of the measures which are applicable in a variety of fields including e.g. probability theory, statistics and economics.
One and two sample mean and variance
 tests (differences and ratios) are considered.
 The test statistics are all expressed in the same
 form as the Student t-test, which facilitates their
 presentation in the classroom. This contribution
 also fills the gap of a robust (to non-normality)
 alternative to the chi-square single variance test
 for large samples, since no such procedure is implemented
 in standard statistical software.
Estimate the lower and upper bound of asymptomatic cases in an 
    epidemic using the capture/recapture methods from Böhning et al. (2020) 
    <doi:10.1016/j.ijid.2020.06.009> and Rocchetti et al. (2020)
    <doi:10.1101/2020.07.14.20153445>. Note there is currently some discussion
    about the validity of the methods implemented in this package. You should
    read carefully the original articles, alongside this answer from Li et al.
    (2022) <doi:10.48550/arXiv.2209.11334> before using this package in your
    project.
Write sequential-looking code that pauses and resumes.
             gen() creates a generator, an iterator that returns a
             value and pauses each time it reaches a yield() call.
             async() creates a promise, which runs until it reaches
             a call to await(), then resumes when information is available.
             These work similarly to generator and async constructs
             from 'Python' or 'JavaScript'. Objects produced are
             compatible with the 'iterators' and 'promises' packages.
             Version 0.3 supports on.exit, single-step debugging,
             stream() for making asynchronous iterators, and
             delimited goto() in switch() calls.
Estimation of regression models for sparse asynchronous 
    longitudinal observations, where time-dependent response and covariates 
    are mismatched and observed intermittently within subjects. Kernel 
    weighted estimating equations are used for generalized linear models 
    with either time-invariant or time-dependent coefficients. Cao, H.,
    Li, J., and Fine, J. P. (2016) <doi:10.1214/16-EJS1141>. Cao, H., 
    Zeng, D., and Fine, J. P. (2015) <doi:10.1111/rssb.12086>.
Provides a collection of psychometric methods to process item metadata
 and use target assessment and measurement blueprint constraints to assemble a test form. Currently two automatic
 test assembly (ata) approaches are enabled. For example, the weighted (positive) deviations method, wdm(), proposed
 by Swanson and Stocking (1993) <doi:10.1177/014662169301700205> was implemented in its full specification allowing
 for both item selection as well as test form refinement. The linear constraint programming approach, atalp(), uses the 
 linear equation solver by Berkelaar et. al (2014) <http://lpsolve.sourceforge.net/5.5/>
 to enable a variety of approaches to select items.
Create Tables for Reporting Clinical Trials.
  Calculates descriptive statistics and hypothesis tests, 
  arranges the results in a table ready for reporting with LaTeX, HTML or Word.
The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>,
	Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the
	initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy
	implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend
	and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.
Point-scale variogram deconvolution from irregular/regular spatial support according to Goovaerts, P., (2008) <doi: 10.1007/s11004-007-9129-1>; ordinary area-to-area (co)Kriging and area-to-point (co)Kriging.
Estimation and inference methods for bounding average treatment effects (on the treated) that are valid under an unconfoundedness assumption. 
    The bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. 
    This robustness is achieved by only using limited "pooling" of information across observations. 
    For more details, see the paper by Lee and Weidner (2021), "Bounding Treatment Effects by Pooling Limited Information across Observations," <arXiv:2111.05243>.
A recent method proposed by Yi and Chen (2023) <doi:10.1177/09622802221146308> is used to estimate the average treatment effects using noisy data containing both measurement error and spurious variables. The package 'AteMeVs' contains a set of functions that provide a step-by-step estimation procedure, including the correction of the measurement error effects, variable selection for building the model used to estimate the propensity scores, and estimation of the average treatment effects. The functions contain multiple options for users to implement, including different ways to correct for the measurement error effects, distinct choices of penalty functions to do variable selection, and various regression models to characterize propensity scores.
Computing and visualizing comparative
 asymptotic timings of different algorithms and code versions.
 Also includes functionality for comparing empirical timings with
 expected references such as linear or quadratic,
 <https://en.wikipedia.org/wiki/Asymptotic_computational_complexity>
 Also includes functionality for measuring asymptotic memory and other
 quantities.
Stanford 'ATLAS' (Advanced Temporal Search Engine) is a powerful tool that allows constructing
             cohorts of patients extremely quickly and efficiently. This package is designed to interface directly
             with an instance of 'ATLAS' search engine and facilitates API queries and data dumps. Prerequisite
             is a good knowledge of the temporal language to be able to efficiently construct a query.
             More information available at <https://shahlab.stanford.edu/start>.
Simplify creating multiple, related 'leaflet' maps across tabs for a 'shiny' application. Users build lists of any polygons, points, and polylines needed for the project, use the map_server() function to assign built lists and other chosen aesthetics into each tab, and the package leverages modules to generate all map tabs.
Download air quality and meteorological information of Chile from  the National Air Quality System (S.I.N.C.A.)<https://sinca.mma.gob.cl/> dependent on the Ministry of the Environment and the Meteorological Directorate of Chile (D.M.C.)<http://www.meteochile.gob.cl/> dependent on the Directorate General of Civil Aeronautics.
Provides functions for implementing the Analysis-of-marginal-Tail-Means (ATM) method, a robust optimization method for discrete black-box optimization. Technical details can be found in Mak and Wu (2018+) <arXiv:1712.03589>. This work was supported by USARO grant W911NF-17-1-0007.
Implements the differential equations associated to different versions of Allometric Trophic Models (ATN) to estimate the temporal dynamics of species biomasses in food webs. It offers several features to generate synthetic food webs and to parametrise models as well as a wrapper to the ODE solver deSolve.
Provides tools to read/write/publish metadata based on the 'Atom' XML syndication format. This includes
 support of 'Dublin Core' XML implementation, and a client to API(s) implementing the 'AtomPub' 'SWORD' API specification.
ATPOL is a rectangular grid system used for botanical studies in Poland. The ATPOL grid was developed in Institute of Botany, Jagiellonian University, Krakow, Poland in '70. Since then it is widely used to represent distribution of plants in Poland. 
    'atpolR' provides functions to translate geographic coordinates to the grid and vice versa. It also allows to create a choreograph map.
Plot party trees in left-right orientation instead of the
  classical top-down layout.
The at-Risk (aR) approach is based on a two-step parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the aR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al. (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.
Wraps the 'AT' Protocol (Authenticated Transfer Protocol) behind 
    'Bluesky' <https://bsky.social>. Functions can be used for, among others, 
    retrieving posts and followers from the network or posting content.
Contains some tools for testing, analyzing time series data and
    fitting popular time series models such as ARIMA, Moving Average and Holt
    Winters, etc. Most functions also provide nice and clear outputs like SAS
    does, such as identify, estimate and forecast, which are the same statements
    in PROC ARIMA in SAS.
Manage dependencies during package development. This can
    retrieve all dependencies that are used in ".R" files in the "R/"
    directory, in ".Rmd" files in "vignettes/" directory and in 'roxygen2'
    documentation of functions. There is a function to update the
    "DESCRIPTION" file of your package with 'CRAN' packages or any other
    remote package.  All functions to retrieve dependencies of ".R"
    scripts and ".Rmd" or ".qmd" files can be used independently of a
    package development.
Tools for defensive programming, inspired by 'purrr' mappers and 
    based on 'rlang'.'attempt' extends and facilitates defensive programming by 
    providing a consistent grammar, and provides a set of easy to use functions 
    for common tests and conditions. 'attempt' only depends on 'rlang', and 
    focuses on speed, so it can be easily integrated in other functions and 
    used in data analysis.
Self-Attention algorithm helper functions and demonstration vignettes of increasing depth on how to construct the Self-Attention algorithm, this is based on Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>, Dan Jurafsky and James H. Martin (2022, ISBN:978-0131873216) <https://web.stanford.edu/~jurafsky/slp3/> "Speech and Language Processing (3rd ed.)" and Alex Graves (2020) <https://www.youtube.com/watch?v=AIiwuClvH6k> "Attention and Memory in Deep Learning".
Confidence curves, confidence intervals and p-values for 
   correlation coefficients corrected for attenuation due to measurement error.
   Implements the methods described in Moss (2019, <arxiv:1911.01576>).
Provides functions for estimating the attributable burden of disease due to risk factors. The posterior simulation is performed using arm::sim as described in Gelman, Hill (2012) <doi:10.1017/CBO9780511790942> and the attributable burden method is based on Nielsen, Krause, Molbak <doi:10.1111/irv.12564>.
Various functions to compute the area under the curve of selected measures: The area under the sensitivity curve (AUSEC), the area under the specificity curve (AUSPC), the area under the accuracy curve (AUACC), and the area under the receiver operating characteristic curve (AUROC). Support for visualization and partial areas is included.
Performs statistical testing to compare predictive 
	models based on multiple observations of the A' statistic (also known as 
	Area Under the Receiver Operating Characteristic Curve, or AUC). 
	Specifically, it implements a testing method based on the equivalence 
	between the A' statistic and the Wilcoxon statistic. 
	For more information, see Hanley and McNeil (1982) <doi:10.1148/radiology.143.1.7063747>.
Estimates a first-price auction model with conditionally independent
  private values as described in MacKay (2020) <doi:10.2139/ssrn.3096534>. The
  model allows for unobserved heterogeneity that is common to all bidders in
  addition to observable heterogeneity.
Interfaces to audio devices (mainly sample-based) from R to allow recording and playback of audio. Built-in devices include Windows MM, Mac OS X AudioUnits and PortAudio (the last one is very experimental).
Facilitates plotting audiometric data (mostly) by preparing the coordinate system according to standards, given e. g. in American Speech-Language-Hearing Association (2005), <doi:10.1044/policy.GL2005-00014>.
Find an upper bound for the total amount of overstatement
    of assets in a set of accounts, or estimate the amount of sales tax
    owed on a collection of transactions (Meeden and Sargent, 2007,
    <doi:10.1080/03610920701386802>).
Provides an easy to use unified interface for creating validation plots for any model. 
  The 'auditor' helps to avoid repetitive work consisting of writing code needed to create residual plots. 
  This visualizations allow to asses and compare the goodness of fit, performance, and similarity of models.
Dynamic regression for time series using Extreme Gradient Boosting with hyper-parameter tuning via Bayesian Optimization or Random Search.
A collection of Japanese text processing tools for filling
    Japanese iteration marks, Japanese character type conversions,
    segmentation by phrase, and text normalization which is based on rules
    for the 'Sudachi' morphological analyzer and the 'NEologd' (Neologism
    dictionary for 'MeCab').  These features are specific to Japanese and
    are not implemented in 'ICU' (International Components for Unicode).
Functions for analysis of data generated from experiments in
    augmented randomised complete block design according to Federer, W.T.
    (1961) <doi:10.2307/2527837>. Computes analysis of variance, adjusted
    means, descriptive statistics, genetic variability statistics etc.
    Further includes data visualization and report generation functions.
Implementation of the augmented
            Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. (2015) <doi:10.1080/01621459.2014.922777>
            for analyzing the data with mixed measurement error and misclassification. The main
            function provides a similar summary output as that of glm() function. Both parametric and
            empirical SIMEX are considered in the package.
Extract and process bird sightings records from
    eBird (<http://ebird.org>), an online tool for recording bird
    observations.  Public access to the full eBird database is via the
    eBird Basic Dataset (EBD; see <http://ebird.org/ebird/data/download>
    for access), a downloadable text file. This package is an interface to
    AWK for extracting data from the EBD based on taxonomic, spatial, or
    temporal filters, to produce a manageable file size that can be
    imported into R.
Standard template library sort is
 used to implement an efficient  
 algorithm <arXiv:2107.01285> for computing Area Under Minimum and
 directional derivatives.
Uses locality sensitive hashing and creates a neighbourhood graph for a data set and calculates the adjusted rank index value for the same. It uses Gaussian random planes to decide the nature of a given point. Datar, Mayur, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni(2004) <doi:10.1145/997817.997857>.
Estimate the AUC using a variety of methods as follows: 
             (1) frequentist nonparametric methods based on the Mann-Whitney statistic or kernel methods. 
             (2) frequentist parametric methods using the likelihood ratio test based on higher-order 
             asymptotic results, the signed log-likelihood ratio test, the Wald test, 
             or the approximate ''t'' solution to the Behrens-Fisher problem. 
             (3) Bayesian parametric MCMC methods.
Extraction, preparation, visualisation and analysis of TERN AusPlots ecosystem monitoring data. Direct access to plot-based data on vegetation and soils across Australia, including physical sample barcode numbers. Simple function calls extract the data and merge them into species occurrence matrices for downstream analysis, or calculate things like basal area and fractional cover. TERN AusPlots is a national field plot-based ecosystem surveillance monitoring method and dataset for Australia. The data have been collected across a national network of plots and transects by the Terrestrial Ecosystem Research Network (TERN - <https://www.tern.org.au>), an Australian Government NCRIS-enabled project, and its Ecosystem Surveillance platform (<https://www.tern.org.au/tern-land-observatory/ecosystem-surveillance-and-environmental-monitoring/>).
Provides access to biographical and political data about Australian 
    federal politicians who served between 1901 and 2021. This enhances how 
    reproducible research is that uses this data.
Performs approximate unconditional and permutation testing for
    2x2 contingency tables. Motivated by testing for disease association with rare
    genetic variants in case-control studies. When variants are extremely rare,
    these tests give better control of Type I error than standard tests.
Uses Auth0 API (see <https://auth0.com> for more 
    information) to use a simple authentication system. It provides 
    tools to log in and out a shiny application using social networks or a list
    of e-mails.
PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the <http://www.ijcem.org/papers032013/ijcem_032013_06.pdf> web page.
Calculations of the most common metrics of automated advertisement and plotting of them with trend and forecast. Calculations and description of metrics is taken from different RTB platforms support documentation. Plotting and forecasting is based on packages 'forecast', described in Rob J Hyndman and George Athanasopoulos (2021) "Forecasting: Principles and Practice" <https://otexts.com/fpp3/> and Rob J Hyndman et al "Documentation for 'forecast'" (2003) <https://pkg.robjhyndman.com/forecast/>, and 'ggplot2', described in Hadley Wickham et al "Documentation for 'ggplot2'" (2015) <https://ggplot2.tidyverse.org/>, and Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen (2015) "ggplot2: Elegant Graphics for Data Analysis" <https://ggplot2-book.org/>.
A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.
A modified version of the classification and regression tree (CART) 
    algorithm for modelling spatial data that features coordinate information.
    Coordinate information can be used to evaluate measures of spatial
    autocorrelation and spatial compactness during the splitting phase of the
    tree, leading to better predictions and more physically realistic predictions
    on these types of datasets. These methods are described in Ancell and Bean (2021)
    <arXiv:2101.08258>.
Automatically calculates cognostic groups for plot objects and list column plot objects.  Results are returned in a nested data frame.
Contains functions to implement automated covariate selection using methods described in the
             high-dimensional propensity score (HDPS) algorithm by Schneeweiss et.al. Covariate adjustment in real-world-observational-data (RWD) is important for
             for estimating adjusted outcomes and this can be done by using methods such as, but not limited to, propensity score 
             matching, propensity score weighting and regression analysis. While these methods strive to statistically adjust for 
             confounding, the major challenge is in selecting the potential covariates that can bias the outcomes comparison estimates 
             in observational RWD (Real-World-Data). This is where the utility of automated covariate selection comes in. 
             The functions in this package help to implement the three major steps of automated covariate selection as described by
             Schneeweiss et. al elsewhere. These three functions, in order of the steps required to execute automated covariate 
             selection are, get_candidate_covariates(), get_recurrence_covariates() and get_prioritised_covariates(). 
             In addition to these functions, a sample real-world-data from publicly available de-identified medical claims data is 
             also available for running examples and also for further exploration. The original article where the algorithm is described 
             by Schneeweiss et.al. (2009) <doi:10.1097/EDE.0b013e3181a663cc> .
An interface to the 'AutoDesk' 'API' Platform including the Authentication 
    'API' for obtaining authentication to the 'AutoDesk' Forge Platform, Data Management 
    'API' for managing data across the platform's cloud services, Design Automation 'API'
    for performing automated tasks on design files in the cloud, Model
    Derivative 'API' for translating design files into different formats, sending
    them to the viewer app, and extracting design data, and Viewer for rendering
    2D and 3D models (see <https://developer.autodesk.com> for more information).
An AutoML algorithm is developed to construct homogeneous or 
             heterogeneous stacked ensemble models using specified base-learners. 
             Various criteria are employed to identify optimal models, enhancing 
             diversity among them and resulting in more robust stacked ensembles. 
             The algorithm optimizes the model by incorporating an increasing 
             number of top-performing models to create a diverse combination. 
             Presently, only models from 'h2o.ai' are supported.
Automatic fixed rank kriging for (irregularly located)
    spatial data using a class of basis functions with multi-resolution features
    and ordered in terms of their resolutions. The model parameters are estimated
    by maximum likelihood (ML) and the number of basis functions is determined
    by Akaike's information criterion (AIC). For spatial data with either one
    realization or independent replicates, the ML estimates and AIC are efficiently
    computed using their closed-form expressions when no missing value occurs. Details 
    regarding the basis function construction, parameter estimation, and AIC calculation  
    can be found in Tzeng and Huang (2018) <doi:10.1080/00401706.2017.1345701>. For
    data with missing values, the ML estimates are obtained using the expectation-
    maximization algorithm. Apart from the number of basis functions, there are
    no other tuning parameters, making the method fully automatic. Users can also
    include a stationary structure in the spatial covariance, which utilizes
    'LatticeKrig' package.
Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the 'DESeq2' package described in Love et al. (2014) <doi:10.1186/s13059-014-0550-8>. The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce.
A customisable set of tools for assessing and grading 
    R or R-markdown scripts from students. It allows for checking correctness 
    of code output, runtime statistics and static code analysis. The latter 
    feature is made possible by representing R expressions using a tree
    structure.
Analysis of task-related functional magnetic resonance imaging (fMRI) activity at the level of individual participants is commonly based on general linear modelling (GLM) that allows us to estimate to what extent the blood oxygenation level dependent (BOLD) signal can be explained by task response predictors specified in the GLM model. The predictors are constructed by convolving the hypothesised timecourse of neural activity with an assumed hemodynamic response function (HRF). To get valid and precise estimates of task response, it is important to construct a model of neural activity that best matches actual neuronal activity. The construction of models is most often driven by predefined assumptions on the components of brain activity and their duration based on the task design and specific aims of the study. However, our assumptions about the onset and duration of component processes might be wrong and can also differ across brain regions. This can result in inappropriate or suboptimal models, bad fitting of the model to the actual data and invalid estimations of brain activity. Here we present an approach in which theoretically driven models of task response are used to define constraints based on which the final model is derived computationally using the actual data. Specifically, we developed 'autohrf' — a package for the 'R' programming language that allows for data-driven estimation of HRF models. The package uses genetic algorithms to efficiently search for models that fit the underlying data well. The package uses automated parameter search to find the onset and duration of task predictors which result in the highest fitness of the resulting GLM based on the fMRI signal under predefined restrictions. We evaluate the usefulness of the 'autohrf' package on publicly available datasets of task-related fMRI activity. Our results suggest that by using 'autohrf' users can find better task related brain activity models in a quick and efficient manner.
Functions for displaying multiple images  or scatterplots with a color 
    scale, i.e., heat maps, possibly with projected coordinates.  The
    package relies on the base graphics system, so graphics are
    rendered rapidly.
R Interface to 'AutoKeras' <https://autokeras.com/>.
    'AutoKeras' is an open source software library for Automated Machine
    Learning (AutoML). The ultimate goal of AutoML is to provide easily
    accessible deep learning tools to domain experts with limited data science
    or machine learning background. 'AutoKeras' provides functions to
    automatically search for architecture and hyperparameters of deep
    learning models.
Parse R code in a given directory for R packages and attempt to install them from CRAN or GitHub. Optionally use a dependencies file for tighter control over which package versions to install.
An automatic interpolation is done by automatically estimating the variogram and then calling gstat. An overview is given by Hiemstra et al (2008) <doi:10.1016/j.cageo.2008.10.011>.
Provides methods for fitting the Mixture of Factor Analyzers (MFA) model automatically. 
    The MFA model is a mixture model where each sub-population is assumed to follow the Factor Analysis model. The Factor Analysis (FA) model is a latent variable model which assumes that observations are normally distributed, but imposes constraints on their covariance matrix. The MFA model contains two hyperparameters; g (the number of components in the mixture) and q (the number of factors in each component Factor Analysis model). Usually, the Expectation-Maximisation algorithm would be used to fit the MFA model, but this requires g and q to be known. This package treats g and q as unknowns and provides several methods which infer these values with as little input from the user as possible.
Fits from simple regression to highly customizable deep neural networks 
    either with gradient descent or metaheuristic, using automatic hyper parameters 
    tuning and custom cost function.
    A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization.
A tool that improves the prediction performance of multilevel
    regression with post-stratification (MrP) by combining a number of machine
    learning methods. For information on the method, please refer to Broniecki, 
	Wüest, Leemann (2020) ''Improving Multilevel Regression with 
	Post-Stratification Through Machine Learning (autoMrP)'' in the 
	'Journal of Politics'. Final pre-print version: 
	<https://lucasleemann.files.wordpress.com/2020/07/automrp-r2pa.pdf>.
Automatically generate a changelog file (NEWS.md /
    CHANGELOG.md) from the git history using conventional commit messages
    (<https://www.conventionalcommits.org/en/v1.0.0/>).
An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses.
              We rely on the 'pamr' <http://www.bioconductor.org/packages//2.7/bioc/html/pamr.html> clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the 
              least significant genes according to the 'pamr' algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes.
Functionalities to automatically generate interactive visualizations for
             statistical results supported by 'ggfortify', such as time series, PCA,
             clustering and survival analysis, with 'plotly.js' <https://plotly.com/>  and
             'ggplot2' style. The generated visualizations can also be easily extended
             using 'ggplot2' and 'plotly' syntax while staying interactive.
The image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed.
Create beautiful and interactive visualizations in a single function call. The 'data.table' package is utilized to perform the data wrangling necessary to prepare your data for the plot types you wish to build, along with allowing fast processing for big data. There are two broad classes of plots available: standard plots and machine learning evaluation plots. There are lots of parameters available in each plot type function for customizing the plots (such as faceting) and data wrangling (such as variable transformations and aggregation).
Performs Rasch analysis (semi-)automatically, which has been shown to be comparable with the standard Rasch analysis (Feri Wijayanto et al. (2021) <doi:10.1111/bmsp.12218>, Feri Wijayanto et al. (2022) <doi:10.3758/s13428-022-01947-9>, Feri Wijayanto et al. (2022) <doi:10.1177/01466216221125178>).
Make summary tables for descriptive statistics and select explanatory variables 
    automatically in various regression models. Support linear models, generalized linear 
    models and cox-proportional hazard models. Generate publication-ready tables summarizing 
    result of regression analysis and plots. The tables and plots can be exported in "HTML", 
    "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents.
Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model.
A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The The original AutoScore structure is described in the research paper<doi:10.2196/21798>. A full tutorial can be found here<https://nliulab.github.io/AutoScore/>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.
Provides an efficient suite of R tools for scorecard modeling, analysis, and visualization. 
    Including equal frequency binning, equidistant binning, 
    K-means binning, chi-square binning, decision tree binning, data screening, manual parameter modeling, 
    fully automatic generation of scorecards, etc. 
    This package is designed to make scorecard development easier and faster.
    References include:
    1. <http://shichen.name/posts/>. 
    2. Dong-feng Li(Peking University),Class PPT.
    3. <https://zhuanlan.zhihu.com/p/389710022>. 
    4. <https://www.zhangshengrong.com/p/281oqR9JNw/>.
For researchers to quickly and comprehensively acquire disease genes, so as to understand the mechanism of disease,
  we developed this program to acquire disease-related genes.
  The data is integrated from three public databases. The three databases are 'eDGAR', 'DrugBank'
  and 'MalaCards'. The 'eDGAR' is a comprehensive database, containing data on the relationship between disease and genes.
  'DrugBank' contains information on 13443 drugs and 5157 targets. 'MalaCards' integrates human disease information, including disease-related genes.
Static code compilation of a 'shiny' app given an R function (into 'ui.R' and 'server.R' files or into a 'shiny' app object). See examples at <https://github.com/alekrutkowski/autoshiny>.
Randomly splits data into testing and training sets. Then, uses stepwise selection to fit numerous multiple regression models on the training data, and tests them on the test data. Returned for each model are plots comparing model Akaike Information Criterion (AIC), Pearson correlation coefficient (r) between the predicted and actual values, Mean Absolute Error (MAE), and R-Squared among the models. Each model is ranked relative to the other models by the model evaluation metrics (i.e., AIC, r, MAE, and R-Squared) and the model with the best mean ranking among the model evaluation metrics is returned. Model evaluation metric weights for AIC, r, MAE, and R-Squared are taken in as arguments as aic_wt, r_wt, mae_wt, and r_squ_wt, respectively. They are equally weighted as default but may be adjusted relative to each other if the user prefers one or more metrics to the others, Field, A. (2013, ISBN:978-1-4462-4918-5).
Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. 
  Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>.
  Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.
Algorithms for automatically finding appropriate thresholds
    for numerical data, with special functions for thresholding images.
    Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R
    users. See <https://imagej.net/plugins/auto-threshold> and Landini et
    al.  (2017) <DOI:10.1111/jmi.12474>.
A novel parametrization of log transformation and a shift parameter to automate the transformation process are proposed in R package 'AutoTransQF' based on Feng et al. (2016). Please read Feng et al. (2016) <doi:10.1002/sta4.104> for more details of the method.
Offers a set of functions to easily make predictions for univariate time series. 
             'autoTS' is a wrapper of existing functions of the 'forecast' and 'prophet' packages, 
             harmonising their outputs in tidy dataframes and using default values for each.
             The core function getBestModel() allows the user to effortlessly benchmark seven 
             algorithms along with a bagged estimator to identify which one performs the best 
             for a given time series.
Weather indices are formed from weather variables in this package. The users can input any number of weather variables recorded over any number of weeks. This package has no restriction on the number of weeks and weather variables to be taken as input.The details of the method can be seen (i)'Joint effects of weather variables on rice yields' by  R. Agrawal, R. C. Jain  and M. P. Jha in Mausam, vol. 34, pp. 189-194, 1983,<doi:10.54302/mausam.v34i2.2392>,(ii)'Improved weather indices based Bayesian regression model for forecasting crop yield' by  M. Yeasin, K. N. Singh, A. Lama and B. Gurung in Mausam, vol. 72, pp.879-886, 2021,<doi:10.54302/mausam.v72i4.670>.
Bindings to 'FFmpeg' <http://www.ffmpeg.org/> AV library for working with 
    audio and video in R. Generates high quality video from images or R graphics with 
    custom audio. Also offers high performance tools for reading raw audio, creating
    'spectrograms', and converting between countless audio / video formats. This package 
    interfaces directly to the C API and does not require any command line utilities.
Check if a given package name is available to use. It checks the
  name's validity. Checks if it is used on 'GitHub', 'CRAN' and 'Bioconductor'. Checks
  for unintended meanings by querying 'Wiktionary' and Wikipedia.
Implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) <doi:10.1109/LSP.2016.2541867>.
Average population attributable fractions are calculated for a set
    of risk factors (either binary or ordinal valued) for both prospective and case-
    control designs. Confidence intervals are found by Monte Carlo simulation. The
    method can be applied to either prospective or case control designs, provided an
    estimate of disease prevalence is provided. In addition to an exact calculation
    of AF, an approximate calculation, based on randomly sampling permutations has
    been implemented to ensure the calculation is computationally tractable when the
    number of risk factors is large.
Allows access to the data found in the species list featured in the renowned 'List of the Birds of Peru' Plenge, M. A. (2023) <https://sites.google.com/site/boletinunop/checklist>. This publication stands as one of Peru's most comprehensive reviews of bird diversity. The dataset incorporates detailed species accounts and has been meticulously structured for effortless utilization within the R environment.
We provide a stage-wise selection method using genetic algorithm which can perform fast interaction selection in high-dimensional linear regression models with two-way interaction effects under strong, weak, or no heredity condition. Ye, C.,and Yang,Y. (2019) <doi:10.1109/TIT.2019.2913417>.
Easy-to-use tools for performing complex queries on 'avidaDB', a
  semantic database that stores genomic and transcriptomic data of
  self-replicating computer programs (known as digital organisms) that mutate
  and evolve within a user-defined computational environment.
Tools to compute the center of gravity and moment of inertia tensor of any 
    flying bird. The tools function by modeling a bird as a composite structure 
    of simple geometric objects. This requires detailed morphological 
    measurements of bird specimens although those obtained for the associated 
    paper have been included in the package for use. Refer to the vignettes and 
    supplementary material for detailed information on the package function.
Research of subgroups in random clinical trials with binary outcome and two treatments groups. This is an adaptation of the Jared Foster method (<https://www.ncbi.nlm.nih.gov/pubmed/21815180>).
Provides a weekly summary of Hass Avocado sales for the 
    contiguous US from January 2017 to November 2020. 
    See the package website for more information, documentation,
    and examples. Data source: Haas Avocado Board 
    <https://hassavocadoboard.com/category-data/>.
Grafts the extinct bird species from the 'Avotrex' database (Sayol et al., in review) on to the 'BirdTree' phylogenies <https://birdtree.org>, using a set of different commands.
Which day a week starts depends heavily on the either the local or
  professional context. This package is designed to be a lightweight solution
  to easily switching between week-based date definitions.
Self-organizing maps (also known as SOM, see Kohonen (2001) <doi:10.1007/978-3-642-56927-2>) are a method for dimensionality reduction and clustering of continuous data. This package introduces interactive (html) graphics for easier analysis of SOM results. It also features an interactive interface, for push-button training and visualization of SOM on numeric, categorical or mixed data, as well as tools to evaluate the quality of SOM.
Collect  your data on digital marketing campaigns from 'Awin' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Make the compiled Java modules of the Amazon Web Services ('AWS') 'SDK' available to be used in downstream R packages interacting with 'AWS'. See <https://aws.amazon.com/sdk-for-java> for more information on the 'AWS' 'SDK' for Java.
Fetching data from Amazon 'Kinesis' Streams using the Java-based 'MultiLangDaemon' interacting with Amazon Web Services ('AWS') for easy stream processing from R. For more information on 'Kinesis', see <https://aws.amazon.com/kinesis>.
We provide a collection of R-functions implementing
    adaptive smoothing procedures in 1D, 2D and 3D. This includes the
    Propagation-Separation Approach to adaptive smoothing,
   the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter.
   The package is described in detail in Polzehl J, Papafitsoros K, Tabelow K (2020). 
   Patch-Wise Adaptive Weights Smoothing in R. Journal of Statistical Software, 95(6), 1-27. 
   <doi:10.18637/jss.v095.i06>,
    Usage of the package in MR imaging is illustrated in Polzehl and Tabelow (2023),
   Magnetic Resonance Brain Imaging, 2nd Ed. Appendix A, Springer, Use R! Series.
   <doi:10.1007/978-3-031-38949-8>.
Use the Amazon Alexa Web Information Services API to
    find information about domains, including the kind of content
    that they carry, how popular are they---rank and traffic history,
    sites linking to them, among other things. See <https://aws.amazon.com/awis/>
    for more information.
Client for 'AWS Comprehend' <https://aws.amazon.com/comprehend>, a cloud natural language processing service that can perform a number of quantitative text analyses, including language detection, sentiment analysis, and feature extraction.
Retrieve Amazon EC2 instance metadata from within the running instance.
Providing the functions for communicating with Amazon Web Services(AWS)
    Elastic Compute Cloud(EC2) and Elastic Container Service(ECS).
    The functions will have the prefix 'ecs_' or 'ec2_' depending on the class 
    of the API. The request will be sent via the REST API and the parameters are
    given by the function argument. The credentials can be set via 'aws_set_credentials'.
    The EC2 documentation can be found at <https://docs.aws.amazon.com/AWSEC2/latest/APIReference/Welcome.html>
    and ECS can be found at <https://docs.aws.amazon.com/AmazonECS/latest/APIReference/Welcome.html>.
A simple client for the Amazon Web Services ('AWS') Identity
    and Access Management ('IAM') 'API' <https://aws.amazon.com/iam/>.
Client package for the 'AWS Key Management Service' <https://aws.amazon.com/kms/>, a cloud service for managing encryption keys.
A simple client package for the Amazon Web Services ('AWS') Lambda 
    API <https://aws.amazon.com/lambda/>.
A client for AWS Polly <http://aws.amazon.com/documentation/polly>, a speech synthesis service.
A simple client package for the Amazon Web Services ('AWS') Simple
    Storage Service ('S3') 'REST' 'API' <https://aws.amazon.com/s3/>.
Generates version 2 and version 4 request signatures for Amazon Web Services ('AWS') <https://aws.amazon.com/> Application Programming Interfaces ('APIs') and provides a mechanism for retrieving credentials from environment variables, 'AWS' credentials files, and 'EC2' instance metadata. For use on 'EC2' instances, users will need to install the suggested package 'aws.ec2metadata' <https://cran.r-project.org/package=aws.ec2metadata>.
Client for 'AWS Transcribe' <https://aws.amazon.com/documentation/transcribe>, a cloud transcription service that can convert an audio media file in English and other languages into a text transcript.
A client for 'AWS Translate' <https://aws.amazon.com/documentation/translate>, a machine translation service that will convert a text input in one language into a text output in another language.
Exploration of Weather Research & Forecasting ('WRF') Model data
    of Servicio Meteorologico Nacional (SMN) from Amazon Web Services
    (<https://registry.opendata.aws/smn-ar-wrf-dataset/>) cloud. The package
    provides the possibility of data downloading, processing and correction
    methods. It also has map management and series exploration of available
    meteorological variables of 'WRF' forecast.
Defines the method extract and provides 'openMP' support as needed in several packages.
Simulates battles in the board game Axis and Allies Spring 1942, and calculates your probability of winning a battle. This speeds the game up significantly.
It extends the functionality of 'logger' package. Additional
    logging metadata can be configured to be collected. Logging messages are
    displayed on console and optionally they are sent to 'Azure Log Analytics'
    workspace in real-time.
Imports Azure Application Insights for web pages into Shiny apps
    via Microsoft's JavaScript snippet. 
    Allows app developers to submit page tracking and submit events.
Provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's 'Azure' cloud <https://azure.microsoft.com/>. Use this package to obtain 'OAuth' 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the 'rappdirs' package. The interface is based on the 'OAuth' framework in the 'httr' package, but customised and streamlined for Azure. Part of the 'AzureR' family of packages.
An interface to Azure Cognitive Services <https://docs.microsoft.com/en-us/azure/cognitive-services/>. Both an 'Azure Resource Manager' interface, for deploying Cognitive Services resources, and a client framework are supplied. While 'AzureCognitive' can be called by the end-user, it is meant to provide a foundation for other packages that will support specific services, like Computer Vision, Custom Vision, language translation, and so on. Part of the 'AzureR' family of packages.
An interface to container functionality in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/en-us/product-categories/containers/>. Manage 'Azure Container Instance' (ACI), 'Azure Container Registry' (ACR) and 'Azure Kubernetes Service' (AKS) resources, push and pull images, and deploy services. On the client side, lightweight shells to the 'docker', 'docker-compose', 'kubectl' and 'helm' commandline tools are provided. Part of the 'AzureR' family of packages.
An interface to 'Azure CosmosDB': <https://azure.microsoft.com/en-us/services/cosmos-db/>. On the admin side, 'AzureCosmosR' provides functionality to create and manage 'Cosmos DB' instances in Microsoft's 'Azure' cloud. On the client side, it provides an interface to the 'Cosmos DB' SQL API, letting the user store and query documents and attachments in 'Cosmos DB'. Part of the 'AzureR' family of packages.
A simple interface to the 'Microsoft Graph' API <https://learn.microsoft.com/en-us/graph/overview>. 'Graph' is a comprehensive framework for accessing data in various online Microsoft services. This package was originally intended to provide an R interface only to the 'Azure Active Directory' part, with a view to supporting interoperability of R and 'Azure': users, groups, registered apps and service principals. However it has since been expanded into a more general tool for interacting with Graph. Part of the 'AzureR' family of packages.
Manage keys, certificates, secrets, and storage accounts in Microsoft's 'Key Vault' service: <https://azure.microsoft.com/services/key-vault/>. Provides facilities to store and retrieve secrets, use keys to encrypt, decrypt, sign and verify data, and manage certificates. Integrates with the 'AzureAuth' package to enable authentication with a certificate, and with the 'openssl' package for importing and exporting cryptographic objects. Part of the 'AzureR' family of packages.
An interface to 'Azure Data Explorer', also known as 'Kusto', a fast, distributed data exploration service from Microsoft: <https://azure.microsoft.com/en-us/products/data-explorer/>. Includes 'DBI' and 'dplyr' interfaces, with the latter modelled after the 'dbplyr' package, whereby queries are translated from R into the native 'KQL' query language and executed lazily. On the admin side, the package extends the object framework provided by 'AzureRMR' to support creation and deletion of databases, and management of database principals. Part of the 'AzureR' family of packages.
Interface to the 'Azure Machine Learning' Software Development Kit
    ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and
    manage machine learning models on the 'Azure Machine Learning' service. To
    learn more about 'Azure Machine Learning' visit the website:
    <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.
An interface to 'Azure Queue Storage'. This is a cloud service for storing large numbers of messages, for example from automated sensors, that can be accessed remotely via authenticated calls using HTTP or HTTPS. Queue storage is often used to create a backlog of work to process asynchronously. Part of the 'AzureR' family of packages.
A lightweight but powerful R interface to the 'Azure Resource Manager' REST API. The package exposes a comprehensive class framework and related tools for creating, updating and deleting 'Azure' resource groups, resources and templates. While 'AzureRMR' can be used to manage any 'Azure' service, it can also be extended by other packages to provide extra functionality for specific services. Part of the 'AzureR' family of packages.
Manage storage in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/en-us/product-categories/storage/>. On the admin side, 'AzureStor' includes features to create, modify and delete storage accounts. On the client side, it includes an interface to blob storage, file storage, and 'Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the 'AzureR' family of packages.
An interface to the table storage service in 'Azure': <https://azure.microsoft.com/en-us/services/storage/tables/>. Supplies functionality for reading and writing data stored in tables, both as part of a storage account and from a 'CosmosDB' database with the table service API. Part of the 'AzureR' family of packages.
An interface to 'Azure Computer Vision' <https://docs.microsoft.com/azure/cognitive-services/Computer-vision/Home> and 'Azure Custom Vision' <https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/home>, building on the low-level functionality provided by the 'AzureCognitive' package. These services allow users to leverage the cloud to carry out visual recognition tasks using advanced image processing models, without needing powerful hardware of their own. Part of the 'AzureR' family of packages.
Functionality for working with virtual machines (VMs) in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/en-us/services/virtual-machines/>. Includes facilities to deploy, startup, shutdown, and cleanly delete VMs and VM clusters. Deployment configurations can be highly customised, and can make use of existing resources as well as creating new ones. A selection of predefined configurations is provided to allow easy deployment of commonly used Linux and Windows images, including Data Science Virtual Machines. With a running VM, execute scripts and install optional extensions. Part of the 'AzureR' family of packages.
A simple interface to the instance metadata for a virtual machine running in Microsoft's 'Azure' cloud. This provides information about the VM's configuration, such as its processors, memory, networking, storage, and so on. Part of the 'AzureR' family of packages.
Provides a fast, lightweight, and vectorized base 64 engine
    to encode and decode character and raw vectors as well as files stored
    on disk.  Common base 64 alphabets are supported out of the box
    including the standard, URL-safe, bcrypt, crypt, 'BinHex', and
    IMAP-modified UTF-7 alphabets. Custom engines can be created to
    support unique base 64 encoding and decoding needs.
Included here are babel routines for identifying unusual ribosome protected fragment counts given mRNA counts.
Genomic analysis of model organisms frequently requires the
    use of databases based on human data or making comparisons to
    patient-derived resources. This requires harmonization of gene names
    into the same gene space. The 'babelgene' R package converts between
    human and non-human gene orthologs/homologs. The package integrates
    orthology assertion predictions sourced from multiple databases as
    compiled by the HGNC Comparison of Orthology Predictions (HCOP)
    (Wright et al. 2005 <doi:10.1007/s00335-005-0103-2>, Eyre et al. 2007
    <doi:10.1093/bib/bbl030>, Seal et al. 2011 <doi:10.1093/nar/gkq892>).
Run other estimation and simulation software via the 'nlmixr2' (Fidler et al (2019)
  <doi:10.1002/psp4.12445>) interface including 'PKNCA', 'NONMEM' and 'Monolix'. While not required, you can
  get/install the 'lixoftConnectors' package in the 'Monolix' installation, as
  described at the following url
  <https://monolix.lixoft.com/monolix-api/lixoftconnectors_installation/>. When
  'lixoftConnectors' is available, 'Monolix' can be run directly instead of setting up
  command line usage.
Provides a unified interface to interact with
    'docker' and 'singularity' containers.  You can execute a command
    inside a container, mount a volume or copy a file.
US baby names provided by the SSA. This package contains all
    names used for at least 5 children of either sex.
Israeli baby names provided by Israel's Central Bureau of Statistics. The package contains only names used for at least 5 children in at least one gender and sector ("Jewish", "Muslim", "Christian", "Druze" and "Other"). Data was downloaded from: <https://www.cbs.gov.il/he/publications/LochutTlushim/2020/%D7%A9%D7%9E%D7%95%D7%AA-%D7%A4%D7%A8%D7%98%D7%99%D7%99%D7%9D.xlsx>.
The BACCO bundle of packages is replaced by the BACCO
 package, which provides a vignette that illustrates the constituent
 packages (emulator, approximator, calibrator) in use.
Implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling 'Just Another Gibbs Sampler' ('JAGS') software. In addition, the 'BACCT' package evaluates user-specified decision rules by computing the type-I error/power, or probability of correct go/no-go decision at interim look. The evaluation can be presented numerically or graphically. Users need to have 'JAGS' 4.0.0 or newer installed due to a compatibility issue with 'rjags' package. Currently, the package implements the BAC method for binary outcome only. Support for continuous and survival endpoints will be added in future releases. We would like to thank AbbVie's Statistical Innovation group and Clinical Statistics group for their support in developing the 'BACCT' package.
Provides the design of multi-group phase
    II clinical trials with binary outcomes using the hierarchical Bayesian
    classification and information sharing (BaCIS) model. Subgroups are classified
    into two clusters on the basis of their outcomes mimicking the hypothesis
    testing framework. Subsequently, information sharing takes place within
    subgroups in the same cluster, rather than across all subgroups. This method can
    be applied to the design and analysis of multi-group clinical trials with binary
    outcomes. Reference: Nan Chen and J. Jack Lee (2019) <doi:10.1002/bimj.201700275>.
An implementation of methods for extracting an unweighted unipartite
   graph (i.e. a backbone) from an unweighted unipartite graph, a weighted unipartite
   graph, the projection of an unweighted bipartite graph, or the projection
   of a weighted bipartite graph (Neal, 2022 <doi:10.1371/journal.pone.0269137>).
Provides a backward-pipe operator for 'magrittr' (%<%) or 
    'pipeR' (%<<%) that allows for a performing operations from right-to-left. 
    This allows writing more legible code where right-to-left ordering is 
    natural. This is common with hierarchies and nested structures such as 
    trees, directories or markup languages (e.g. HTML and XML). 
    The package also includes a R-Studio add-in that can be bound to a keyboard 
    shortcut.
Functions introduced or changed since R v3.0.0 are re-implemented in this
    package. The backports are conditionally exported in order to let R resolve
    the function name to either the implemented backport, or the respective base
    version, if available. Package developers can make use of new functions or
    arguments by selectively importing specific backports to
    support older installations.
Code for 'backShift', an algorithm to estimate the connectivity
    matrix of a directed (possibly cyclic) graph with hidden variables. The
    underlying system is required to be linear and we assume that observations
    under different shift interventions are available. For more details,
    see <arXiv:1506.02494>.
The backtest package provides facilities for exploring
        portfolio-based conjectures about financial instruments
        (stocks, bonds, swaps, options, et cetera).
Decomposition for differences-in-differences with variation in
    treatment timing from Goodman-Bacon (2018) <doi:10.3386/w25018>.
The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012)
 can be used to estimate the causal effect of a continuous exposure on a continuous outcome.
 This package provides an approximate sensitivity analysis of BAC with regards to the
 hyperparameter omega. BACprior also provides functions to guide the user in their choice
 of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).
Estimating the average causal effect based on the Bayesian Adjustment for Confounding (BAC) algorithm.
Bindings to 'badgen' <https://www.npmjs.com/package/badgen> to
    generate beautiful 'svg' badges in R without internet access. Images can
    be converted to 'png' using the 'rsvg' package as shown in examples.
Query information and generate badge for using in README and GitHub Pages.
Waffle plots are rectangular pie charts that represent a quantity or abundances using
    colored squares or other symbol. This makes them better at transmitting information as the
    discrete number of squares is easier to read than the circular area of pie charts.
    While the original waffle charts were rectangular with 10 rows and columns, with a single square
    representing 1%, they are nowadays popular in various infographics to visualize any
    proportional ratios.
Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.
Running and comparing meta-analyses of data with hierarchical 
    Bayesian models in Stan, including convenience functions for formatting
    data, plotting and pooling measures specific to meta-analysis. This implements many models
    from Meager (2019) <doi:10.1257/app.20170299>.
The BAGofT assesses the goodness-of-fit of binary classifiers. Details can be found in Zhang, Ding and Yang (2021) <arXiv:1911.03063v2>.
Tree- and rule-based models can be bagged
    (<doi:10.1007/BF00058655>) using this package and their predictions
    equations are stored in an efficient format to reduce the model
    objects size and speed.
The Philippines frequently experiences tropical cyclones (called
    'bagyo' in the Filipino language) because of its geographical position.
    These cyclones typically bring heavy rainfall, leading to widespread
    flooding, as well as strong winds that cause significant damage to human
    life, crops, and property. Data on cyclones are collected and curated by the
    Philippine Atmospheric, Geophysical, and Astronomical Services
    Administration or 'PAGASA' and made available through its website
    <https://bagong.pagasa.dost.gov.ph/tropical-cyclone/publications/annual-report>.
    This package contains Philippine tropical cyclones data in a
    machine-readable format. It is hoped that this data package provides an
    interesting and unique dataset for data exploration and visualisation.
A method to filter correlation and covariance matrices by averaging
     bootstrapped filtered hierarchical clustering and boosting. See Ch. Bongiorno and D. Challet,
     Covariance matrix filtering with bootstrapped hierarchies (2020) <arXiv:2003.05807> and
     Ch. Bongiorno and D. Challet, Reactive Global Minimum Variance Portfolios with k-BAHC covariance cleaning
     (2020) <arXiv:2005.08703>.
Computes approximated adjusted fractional Bayes factors for
    equality, inequality, and about equality constrained hypotheses.
    For a tutorial on this method, see Hoijtink, Mulder, van Lissa, & Gu,
    (2019) <doi:10.31234/osf.io/v3shc>. For applications in structural equation
    modeling, see: Van Lissa, Gu, Mulder, Rosseel, Van Zundert, &
    Hoijtink, (2021) <doi:10.1080/10705511.2020.1745644>. For the statistical
    underpinnings, see Gu, Mulder, and Hoijtink (2018) <doi:10.1111/bmsp.12110>;
    Hoijtink, Gu, & Mulder, J. (2019) <doi:10.1111/bmsp.12145>; Hoijtink, Gu, 
    Mulder, & Rosseel, (2019) <doi:10.31234/osf.io/q6h5w>.
In ancient Chinese mythology, Bai Ze is a divine creature that knows the needs of everything.
    'baizer' provides data processing functions frequently used by the author.
    Hope this package also knows what you want!
Data about the bakers, challenges, and ratings 
    for "The Great British Bake Off", 
    from Wikipedia <https://en.wikipedia.org/wiki/The_Great_British_Bake_Off>.
Provides functions to specify, fit and visualize
    nested partially-latent class models (
    Wu, Deloria-Knoll, Hammitt, and Zeger (2016) <doi:10.1111/rssc.12101>; 
    Wu, Deloria-Knoll, and Zeger (2017) <doi:10.1093/biostatistics/kxw037>;
    Wu and Chen (2021) <doi:10.1002/sim.8804>) for 
    inference of population disease etiology and individual diagnosis. In the motivating
    Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities
    of interest sum to one hundred percent, the PERCH scientists frequently refer to
    them as population etiology pie and individual etiology pie, hence the name of the package.
Several implementations of a novel Bayesian hierarchical statistical model of nucleotide
	recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.)
	for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) <doi:10.1261/rna.079451.122>).
	NR-seq is a powerful extension of RNA-seq that provides information about the kinetics
	of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking
	in standard RNA-seq data. The statistical model makes maximal use of these high-throughput
	datasets by sharing information across transcripts to significantly improve
	uncertainty quantification and increase statistical power. 'bakR' includes a maximally
	efficient implementation of this model for conservative initial investigations of datasets. 'bakR'
	also provides more highly powered implementations using the probabilistic programming language
	'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test
	adjusted statistical inference with the output of these model implementations to
	help biologists separate signal from background. Methods to automatically visualize key
	results and detect batch effects are also provided.
Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.
Select balanced and spatially balanced probability samples in multi-dimensional spaces
    with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of
    the included sampling methods. The local pivotal method by Grafström, Lundström and Schelin (2012)
    <doi:10.1111/j.1541-0420.2011.01699.x> and spatially correlated Poisson sampling by Grafström (2012)
    <doi:10.1016/j.jspi.2011.07.003> are included. Also the cube method (for balanced sampling) and
    the local cube method (for doubly balanced sampling) are included, see Grafström and Tillé (2013)
    <doi:10.1002/env.2194>.
Statistical decision in proteomics data using a hierarchical
    Bayesian model. There are two regression models for describing the 
    mean-variance trend, a gamma regression or a latent gamma mixture
    regression. The regression model is then used as an Empirical Bayes
    estimator for the prior on the variance in a peptide. Further, it assumes
    that each measurement has an uncertainty (increased variance) associated
    with it that is also inferred. Finally, it tries to estimate the posterior
    distribution (by Hamiltonian Monte Carlo) for the differences in means for
    each peptide in the data. Once the posterior is inferred, it integrates the
    tails to estimate the probability of error from which a statistical decision
    can be made.
    See Berg and Popescu for details (<doi:10.1101/2023.05.11.540411>).
Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence <doi:10.1214/17-AOS1579>, ball covariance <doi:10.1080/01621459.2018.1543600>, and ball correlation <doi:10.1080/01621459.2018.1462709>, are developed to analyze complex data in metric spaces, e.g, shape, directional, compositional and symmetric positive definite matrix data. The ball divergence and ball covariance based distribution-free tests are implemented to detecting distribution difference and association in metric spaces <doi:10.18637/jss.v097.i06>. Furthermore, several generic non-parametric feature selection procedures based on ball correlation, BCor-SIS and all of its variants, are implemented to tackle the challenge in the context of ultra high dimensional data. A fast implementation for large-scale multiple K-sample testing with ball divergence <doi: 10.1002/gepi.22423> is supported, which is particularly helpful for genome-wide association study.
Analysis of gene expression RNA-seq data using Bartlett-Adjusted Likelihood-based LInear model (BALLI). Based on likelihood ratio test, it provides comparisons for effect of one or more variables. See Kyungtaek Park (2018) <doi:10.1101/344929> for more information.
The core algorithm is described in "Ball mapper: a shape summary for topological data analysis" by Pawel Dlotko, (2019) <arXiv:1901.07410>. Please consult the following youtube video <https://www.youtube.com/watch?v=M9Dm1nl_zSQfor> the idea of functionality. Ball Mapper provide a topologically accurate summary of a data in a form of an abstract graph. To create it, please provide the coordinates of points (in the points array), values of a function of interest at those points (can be initialized randomly if you do not have it) and the value epsilon which is the radius of the ball in the Ball Mapper construction. It can be understood as the minimal resolution on which we use to create the model of the data.
Functions and datasets for Jeff Gill: "Bayesian Methods: A Social and Behavioral Sciences Approach". First, Second, and Third Edition. Published by Chapman and Hall/CRC (2002, 2007, 2014) <doi:10.1201/b17888>.
Perform mediation analysis in the presence of high-dimensional
    mediators based on the potential outcome framework. Bayesian Mediation
    Analysis (BAMA), developed by Song et al (2019) <doi:10.1111/biom.13189> and
    Song et al (2020) <arXiv:2009.11409>,
    relies on two Bayesian sparse linear mixed models to simultaneously analyze
    a relatively large number of mediators for a continuous exposure and outcome
    assuming a small number of mediators are truly active. This sparsity
    assumption also allows the extension of univariate mediator analysis by
    casting the identification of active mediators as a variable selection
    problem and applying Bayesian methods with continuous shrinkage priors on
    the effects.
Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.
Enables a user to consume the 'BambooHR' API endpoints using R. The
  actual URL of the API will depend on your company domain, and will be handled
  by the package automatically once you setup the config file. The API documentation
  can be found here <https://documentation.bamboohr.com/docs>.
Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).
Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework.
  The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
  on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model.
  The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019)
  <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021)
  <doi:10.18637/jss.v100.i04>.
Species Distribution Modeling (SDM) is a practical methodology 
 that aims to estimate the area of distribution of a species. However, 
 most of the work has focused on estimating static expressions of the 
 correlation between environmental variables. The outputs of
 correlative species distribution models can be interpreted 
 as maps of the suitable environment for a species but not generally as maps
 of its actual distribution. 
 Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4> presented
 the BAM scheme, a heuristic framework that states that the occupied area of 
 a species occurs on sites that have been accessible through dispersal (M) and 
 have both favorable biotic (B) and abiotic conditions (A). 
 The 'bamm' package implements classes and functions to operate on each element 
 of the BAM and by using a cellular automata model where the occupied area 
 of a species at time t is estimated by the multiplication of three 
 binary matrices: one matrix represents movements (M), another 
 abiotic -niche- tolerances (A), and a third, biotic interactions (B). 
 The theoretical background of the package can be found in 
 Soberón and Osorio-Olvera (2022) <arXiv:2212.06308>.
Provides functions for analyzing and visualizing complex
    macroevolutionary dynamics on phylogenetic trees. It is a companion
    package to the command line program BAMM (Bayesian Analysis of
    Macroevolutionary Mixtures) and is entirely oriented towards the analysis,
    interpretation, and visualization of evolutionary rates. Functionality
    includes visualization of rate shifts on phylogenies, estimating
    evolutionary rates through time, comparing posterior distributions of
    evolutionary rates across clades, comparing diversification models using
    Bayes factors, and more.
Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R.
A light-weight object-oriented system with 'python'-like syntax which supports multiple inheritances and incorporates a 'python'-like method resolution order.
A set of functions for doing analysis of A/B split test data and web metrics in general.
Interface to a high-performance implementation of k-medoids clustering described in Tiwari, Zhang, Mayclin, Thrun, Piech and Shomorony (2020) "BanditPAM: Almost Linear Time k-medoids Clustering via Multi-Armed Bandits" <https://proceedings.neurips.cc/paper/2020/file/73b817090081cef1bca77232f4532c5d-Paper.pdf>.
Implements functions that calculate upper prediction 
  bounds on the false discovery proportion (FDP) in the list of discoveries 
  returned by competition-based setups, implementing Ebadi et al. (2022)
  <arXiv:2302.11837>. Such setups include target-decoy competition (TDC) 
  in computational mass spectrometry and the knockoff construction in linear 
  regression (note this package typically uses the terminology of TDC). Included 
  is the standardized (TDC-SB) and uniform (TDC-UB) bound on TDC's FDP, and the 
  simultaneous standardized and uniform bands. Requires 
  pre-computed Monte Carlo statistics available at 
  <https://github.com/uni-Arya/fdpbandsdata>. This data can be downloaded by
  running the command 'devtools::install_github("uni-Arya/fdpbandsdata")' in R
  and restarting R after installation. The size of this data is roughly 81Mb.
Provides functions for the Bayesian analysis of some simple 
    commonly-used models, without using Markov Chain Monte Carlo (MCMC) 
    methods such as Gibbs sampling.  The 'rust' package
    <https://cran.r-project.org/package=rust> is used to simulate a random 
    sample from the required posterior distribution, using the generalized 
    ratio-of-uniforms method.  See Wakefield, Gelfand and Smith (1991) 
    <DOI:10.1007/BF01889987> for details. At the moment three conjugate 
    hierarchical models are available: beta-binomial, gamma-Poisson and a 1-way 
    analysis of variance (ANOVA).
Usually, it is difficult to plot choropleth maps for Bangladesh in 'R'.
    The 'bangladesh' package provides ready-to-use shapefiles for different administrative
    regions of Bangladesh (e.g., Division, District, Upazila, and Union).
    This package helps users to draw thematic maps of administrative regions
    of Bangladesh easily as it comes with the 'sf' objects for the boundaries.
    It also provides functions allowing users to efficiently get specific area
    maps and center coordinates for regions. Users can also search for
    a specific area and calculate the centroids of those areas.
A convenience package for use while drafting code.
    It facilitates making stand-out comment lines decorated with
    bands of characters.  The input text strings are converted into
    R comment lines, suitably formatted. These are then displayed in
    a console window and, if possible, automatically transferred to a
    clipboard ready for pasting into an R script.  Designed to save
    time when drafting R scripts that will need to be navigated and
    maintained by other programmers.
It covers several Bayesian Analysis of Variance (BANOVA) models used in analysis of experimental designs in which both within- and between- subjects factors are manipulated. They can be applied to data that are common in the behavioral and social sciences. The package includes: Hierarchical Bayes ANOVA models with normal response, t response, Binomial (Bernoulli) response, Poisson response, ordered multinomial response and multinomial response variables. All models accommodate unobserved heterogeneity by including a normal distribution of the parameters across individuals. Outputs of the package include tables of sums of squares, effect sizes and p-values, and tables of predictions, which are easily interpretable for behavioral and social researchers. The floodlight analysis and mediation analysis based on these models are also provided. BANOVA uses 'Stan' and 'JAGS' as the computational platform. References: Dong and Wedel (2017) <doi:10.18637/jss.v081.i09>; Wedel and Dong (2020) <doi:10.1002/jcpy.1111>.
Create a hierarchical acoustic event species classifier out of  
  multiple call type detectors as described in 
  Rankin et al (2017) <doi:10.1111/mms.12381>.
Provides functions to scrape IQY calls to Bank of Mexico,
    downloading and ordering the data conveniently.
Various tools dealing with batch effects, in particular enabling the 
  removal of discrepancies between training and test sets in prediction scenarios.
  Moreover, addon quantile normalization and addon RMA normalization (Kostka & Spang, 
  2008) is implemented to enable integrating the quantile normalization step into 
  prediction rules. The following batch effect removal methods are implemented: 
  FAbatch, ComBat, (f)SVA, mean-centering, standardization, Ratio-A and Ratio-G. 
  For each of these we provide  an additional function which enables a posteriori 
  ('addon') batch effect removal in independent batches ('test data'). Here, the
  (already batch effect adjusted) training data is not altered. For evaluating the
  success of batch effect adjustment several metrics are provided. Moreover, the 
  package implements a plot for the visualization of batch effects using principal 
  component analysis. The main functions of the package for batch effect adjustment 
  are ba() and baaddon() which enable batch effect removal and addon batch effect 
  removal, respectively, with one of the seven methods mentioned above. Another 
  important function here is bametric() which is a wrapper function for all implemented
  methods for evaluating the success of batch effect removal. For (addon) quantile 
  normalization and (addon) RMA normalization the functions qunormtrain(), 
  qunormaddon(), rmatrain() and rmaaddon() can be used.
Bayesian adaptive randomization is also called outcome adaptive randomization, which is increasingly used in clinical trials.
Tool to find where a function has its lowest value(minimum). The
    functions can be any dimensions. Recommended use is with eps=10^-10, but can be
    run with 10^-20, although this depends on the function. Two more methods are in
    this package, simple gradient method (Gradmod) and Powell method (Powell). These
    are not recommended for use, their purpose are purely for comparison.
The function \code{barcode()}
        produces a histogram-like plot of a distribution that shows
        granularity in the data.
Tools to generate unique identifier codes and
    printable barcoded labels for the management of biological samples.
    The creation of unique ID codes and printable PDF files can be
    initiated by standard commands, user prompts, or through a GUI addin
    for R Studio. Biologically informative codes can be included for
    hierarchically structured sampling designs.
To perform species identification using DNA barcodes.
Provides R data structures for Shakespeare's complete works, 
  as provided by Project Gutenberg <https:www.gutenberg.org/ebooks/100>.
Bayesian Additive Regression Kernels (BARK) provides
	an implementation for non-parametric function estimation using Levy
	Random Field priors for functions that may be represented as a
	sum of additive multivariate kernels.  Kernels are located at
	every data point as in Support Vector Machines, however, coefficients 
	may be heavily shrunk to zero under the Cauchy process prior, or even, 
	set to zero.  The number of active features is controlled by priors on
	precision parameters within the kernels, permitting feature selection. For 
	more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels",
	Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A, 
	and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy
	Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962 
	<doi:10.1214/11-AOS889>.
Barnard's unconditional test for 2x2 contingency tables.
Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes.  For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.
Contains a variety of methods to generate typical causal inference estimates using Bayesian Additive Regression Trees (BART) as the underlying regression model (Hill (2012) <doi:10.1198/jcgs.2010.08162>).
Fit Bayesian Regression Additive Trees (BART) models to
    select true confounders from a large set of potential confounders and
    to estimate average treatment effect. For more information, see Kim et
    al. (2023) <doi:10.1111/biom.13833>.
An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.
These are bartMachine's Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine.
Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) <doi:10.1214/09-AOAS285> model fits.  We construct conventional plots to analyze a model’s performance and stability  as well as create new tree-based plots to analyze variable importance, interaction, and tree structure.  We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance  and interactions jointly using colour scale to represent posterior uncertainty.  Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 <doi:10.18637/jss.v097.i01>,  'dbarts'  (Vincent Dorie 2023) <https://CRAN.R-project.org/package=dbarts>,  and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.
Intended to facilitate acoustic analysis of (animal) sound transmission experiments, which typically aim to quantify changes in signal structure when transmitted in a given habitat by broadcasting and re-recording animal sounds at increasing distances. The package offers a workflow with functions to prepare the data set for analysis as well as to calculate and visualize several degradation metrics, including blur ratio, signal-to-noise ratio, excess attenuation and envelope correlation among others (Dabelsteen et al 1993 <doi:10.1121/1.406682>).
Package for Bayesian Variable Selection and  Model Averaging 
    in linear models and generalized linear models using stochastic or
    deterministic sampling without replacement from posterior
    distributions.  Prior distributions on coefficients are
    from Zellner's g-prior or mixtures of g-priors
    corresponding to the Zellner-Siow Cauchy Priors or the
    mixture of g-priors from Liang et al (2008)
    <DOI:10.1198/016214507000001337>
    for linear models or mixtures of g-priors from  Li and Clyde
    (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models.
    Other model selection criteria include AIC, BIC and Empirical Bayes 
    estimates of g. Sampling probabilities may be updated based on the sampled
    models using sampling w/out replacement or an efficient MCMC algorithm which
    samples models using a tree structure of the model space 
    as an efficient hash table.  See  Clyde, Ghosh and Littman (2010) 
    <DOI:10.1198/jcgs.2010.09049> for  details on the sampling algorithms.
    Uniform priors over all models or beta-binomial prior distributions on
    model size are allowed, and for large p truncated priors on the model
    space may be used to enforce sampling models that are full rank.  
    The user may force variables to always be included in addition to imposing
    constraints that higher order interactions are included only if their 
    parents are included in the model.
    This material is based upon work supported by the National Science
    Foundation under Division of Mathematical Sciences grant 1106891.
    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
    National Science Foundation.
Provides a Bayesian variable selection approach using continuous spike and slab prior distributions. The prior choices here are motivated by the shrinking and diffusing priors studied in Narisetty & He (2014) <DOI:10.1214/14-AOS1207>.
We perform linear, logistic, and cox regression using the base functions lm(), 
    glm(), and coxph() in the R software and the 'survival' package. Likewise, we 
    can use ols(), lrm() and cph() from the 'rms' package for the same 
    functionality. Each of these two sets of commands has a different focus.
  In many cases, we need to use both sets of commands in the same situation, 
    e.g. we need to filter the full subset model using AIC, and we need to build 
    a visualization graph for the final model. 'base.rms' package can help you to switch 
    between the two sets of commands easily.
Compatibility wrapper to replace the orphaned package. New 
    applications should use base64 encoders from 'jsonlite' or 'openssl' or
    'base64enc'.
This package provides tools for handling base64 encoding. It is more flexible than the orphaned base64 package.
In contrast to RFC3548, the 62nd character ("+") is replaced with
    "-", the 63rd character ("/") is replaced with "_". Furthermore, the encoder
    does not fill the string with trailing "=". The resulting encoded strings
    comply to the regular expression pattern "[A-Za-z0-9_-]" and thus are
    safe to use in URLs or for file names.
    The package also comes with a simple base32 encoder/decoder suited for
    case insensitive file systems.
Provides numerous utilities for acquiring and analyzing
    baseball data from online sources such as 'Baseball Reference' <https://www.baseball-reference.com/>,
    'FanGraphs' <https://www.fangraphs.com/>, and the 'MLB Stats' API <https://www.mlb.com/>.
Provides functions streamlining the data analysis workflow: 
  Outsourcing data import, renaming and type casting to a *.csv.
  Manipulating imputed datasets and fitting models on them. Summarizing models.
An R interface to the 'Base dos Dados' API <https:basedosdados.github.io/mais/py_reference_api/>). Authenticate your project, query our tables, save data to disk and memory, all from R.
Some very simple infrastructure for basis functions.
Collection of baseline correction algorithms, along with a framework and a Tcl/Tk enabled GUI for optimising baseline algorithm parameters. Typical use of the package is for removing background effects from spectra originating from various types of spectroscopy and spectrometry, possibly optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more.
A lightweight package to access spatial basemaps from open sources such as 'OpenStreetMap', 'Carto', 'Mapbox' and others in R.
Providing equivalent functions for the dummy
    classifier and regressor used in 'Python' 'scikit-learn' library. Our goal
    is to allow R users to easily identify baseline performance for their
    classification and regression problems. Our baseline models use no
    predictors, and are useful in cases of class imbalance, multiclass
    classification, and when users want to quickly identify how much
    improvement their statistical and machine learning models are over several
    baseline models. We use a "better" default (proportional guessing) for
    the dummy classifier than the 'Python' implementation ("prior", which is
    the most frequent class in the training set). The functions in the
    package can be used on their own, or introduce methods named
    'dummy_regressor' or 'dummy_classifier' that can be used within the
    caret package pipeline.
Primarily created as an easy and understanding way to do basic sequences surrounding the central dogma of molecular biology.
Implements a class and methods to work with sets,
    doing intersection, union, complementary sets, power sets, cartesian
    product and other set operations in a "tidy" way. These set operations
    are available for both classical sets and fuzzy sets. Import sets from
    several formats or from other several data structures.
All the seeds do not germinate at a single point in time due to physiological mechanisms determined by temperature which vary among individual seeds in the population. Seeds germinate by following accumulation of thermal time in degree days/hours, quantified by multiplying the time of germination with excess of base temperature required by each seed for its germination, which follows log-normal distribution. The theoretical germination course can be obtained by regressing the rate of germination at various fractions against temperature (Garcia et al., 1982), where the fraction-wise regression lines intersect the temperature axis at base temperature and the methodology of determining optimum base temperature has been described by Ellis et al. (1987). This package helps to find the base temperature of seed germination using algorithms of Garcia et al. (1982) and Ellis et al. (1982) <doi:10.1093/JXB/38.6.1033> <doi:10.1093/jxb/33.2.288>.
Functions to create and select graphical themes for the base plotting system. Contains: 1) several custom pre-made themes 2) mechanism for creating new themes by making persistent changes to the graphical parameters of base plots.
Resurrects the standard plot for shapes established by the
 'base' and 'graphics' packages. This is suited to workflows that require
 plotting using the established and traditional idioms of plotting spatially
 coincident data where it belongs. This package depends on 'sf' and only replaces 
 the plot method.
Evaluate, fit, and analyze Hill dose response models (Goutelle et 
    al., 2008 <doi:10.1111/j.1472-8206.2008.00633.x>), also sometimes referred
    to as four-parameter log-logistic models.  Includes tools to invert Hill 
    models,  select models based on the Akaike information criterion
    (Akaike, 1974 <doi:10.1109/TAC.1974.1100705>) or Bayesian information 
    criterion (Schwarz, 1978 <https://www.jstor.org/stable/2958889>), and 
    construct bootstrapped confidence intervals both
    on the Hill model parameters and values derived from the Hill model
    parameters.
Provides methods for examining posterior MCMC samples
    from a single chain using trace plots and density plots, and from
    multiple chains by comparing posterior medians and credible intervals
    from each chain.  These plotting functions have a variety of options,
    such as figure sizes, legends, parameters to plot, and saving plots to file.
    Functions interface with the NIMBLE software package, see
    de Valpine, Turek, Paciorek, Anderson-Bergman, Temple Lang and Bodik (2017)
    <doi:10.1080/10618600.2016.1172487>.
Easily create tables from data 
    frames/matrices.  Create/manipulate tables 
    row-by-row, column-by-column or cell-by-cell.  
    Use common formatting/styling to output 
    rich tables as 'HTML', 'HTML widgets' or to 
    'Excel'.
Data sets of the Spanish National Forest Inventory <https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/> are processed to compute tree metrics and statistics. Function metrics2Vol() controls most of the routines.
It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are four data present in the 'BASiNET' package, "sequences", "sequences2", "sequences-predict" and "sequences2-predict" with 11, 10, 11 and 11 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples. The BASiNET was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA, Isaque; VICENTE, Fábio; PEREIRA, Felipe; LOPES, Fabrício, 2018) <doi:10.1093/nar/gky462>.
It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of maximum entropy for the purpose of making a classification between the classes of the sequences. There are two data present in the 'BASiNET' package, "mRNA", and "ncRNA" with 10 sequences. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples.
Tool to perform Bayesian inference of carcass processing/transport strategy and bone attrition from archaeofaunal skeletal profiles characterized by percentages of MAU (Minimum Anatomical Units). The approach is based on a generative model for skeletal profiles that replicates the two phases of formation of any faunal assemblage: initial accumulation as a function of human transport strategies and subsequent attrition.Two parameters define this model: 1) the transport preference (alpha), which can take any value between - 1 (mostly axial contribution) and 1 (mostly appendicular contribution) following strategies constructed as a function of butchering efficiency of different anatomical elements and the results of ethnographic studies, and 2) degree of attrition (beta), which can vary between 0 (no attrition) and 10 (maximum attrition) and relates the survivorship of bone elements to their maximum bone density. Starting from uniform prior probability distribution functions of alpha and beta, a Monte Carlo Markov Chain sampling based on a random walk Metropolis-Hasting algorithm is adopted to derive the posterior probability distribution functions, which are then available for interpretation. During this process, the likelihood of obtaining the observed percentages of MAU given a pair of parameter values is estimated by the inverse of the Chi2 statistic, multiplied by the proportion of elements within a 1 percent of the observed value. See Ana B. Marin-Arroyo, David Ocio (2018).<doi:10.1080/08912963.2017.1336620>.
Implementation of multisource exchangeability models for Bayesian analyses of prespecified subgroups arising in the context of basket trial design and monitoring.  The R 'basket' package facilitates implementation of the binary, symmetric multi-source exchangeability model (MEM) with posterior inference arising from both exact computation and Markov chain Monte Carlo sampling. Analysis output includes full posterior samples as well as posterior probabilities, highest posterior density (HPD) interval boundaries, effective sample sizes (ESS), mean and median estimations, posterior exchangeability probability matrices, and maximum a posteriori MEMs. In addition to providing "basketwise" analyses, the package includes similar calculations for "clusterwise" analyses for which subgroups are combined into meta-baskets, or clusters, using graphical clustering algorithms that treat the posterior exchangeability probabilities as edge weights. In addition plotting tools are provided to visualize basket and cluster densities as well as their exchangeability.  References include Hyman, D.M., Puzanov, I., Subbiah, V., Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L., Hollebecque, A. and Gervais, R (2015) <doi:10.1056/NEJMoa1502309>; Hobbs, B.P. and Landin, R. (2018) <doi:10.1002/sim.7893>; Hobbs, B.P., Kane, M.J., Hong, D.S. and Landin, R. (2018) <doi:10.1093/annonc/mdy457>; and Kaizer, A.M., Koopmeiners, J.S. and Hobbs, B.P. (2017) <doi:10.1093/biostatistics/kxx031>.
Contains data and code to accompany  the book 
             P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799.
Analytically calculates the operating characteristics of
    single-stage and two-stage basket trials with equal sample sizes using the
	power prior design by Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988>
	and the design by Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>.
Provides a unified syntax for the simulation-based comparison of different single-stage basket trial
	designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by 
	Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988>, Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>,
	Berry et al. (2020) <doi:10.1177/1740774513497539>, Neuenschwander et al. (2016) <doi:10.1002/pst.1730> and
	Psioda et al. (2021) <doi:10.1093/biostatistics/kxz014>. For the latter three designs, the functions are mostly 
	wrappers for functions provided by the packages 'bhmbasket' and 'bmabasket'.
Bayesian fitting and sensitivity analysis methods for adaptive
    spline surfaces described in <doi:10.18637/jss.v094.i08>. Built to handle continuous and categorical inputs as well as
    functional or scalar output. An extension of the methodology in Denison, Mallick
    and Smith (1998) <doi:10.1023/A:1008824606259>.
Includes algorithms to assess alpha and beta diversity
    in all their dimensions (taxonomic, phylogenetic and functional).
    It allows performing a number of analyses based on species
    identities/abundances, phylogenetic/functional distances, trees,
    convex-hulls or kernel density n-dimensional hypervolumes
    depicting species relationships.
    Cardoso et al. (2015) <doi:10.1111/2041-210X.12310>.
Allows the user to manage easily R packages removal and installation. It offers many functions to display installed packages according to
    specific dates and removes them if needed. The user is always prompted when running the removal functions in order to confirm
    the required action. It also provides functions that will install 'Github' starred R packages whether available on 'CRAN' or not.
Functions to allow you to easily pass command-line arguments into R, and functions to aid in submitting your R code in parallel on a cluster and joining the results afterward (e.g. multiple parameter values for simulations running in parallel, splitting up a permutation test in parallel, etc.). See `parseCommandArgs(...)' for the main example of how to use this package.
Extends the BatchJobs package to run statistical experiments on
    batch computing clusters. For further details see the project web page.
Makes it easy to download financial data from Yahoo Finance <https://finance.yahoo.com/>.
Provides Map, Reduce and Filter variants to generate jobs on batch
    computing systems like PBS/Torque, LSF, SLURM and Sun Grid Engine.
    Multicore and SSH systems are also supported. For further details see the
    project web page.
Provides consistent batch means estimation of Monte
    Carlo standard errors.
Semi-supervised and unsupervised Bayesian mixture models that
  simultaneously infer the cluster/class structure and a batch correction.
  Densities available are the multivariate normal and the multivariate t.
  The model sampler is implemented in C++. This package is aimed at analysis of
  low-dimensional data generated across several batches. See Coleman et al.
  (2022) <doi:10.1101/2022.01.14.476352> for details of the model.
Handy frameworks, such as error handling and log generation, for batch scripts.
	Use case: in scripts running in remote servers, set error handling mechanism for downloading and uploading and record operation log.
Different adjustment methods for batch effects in biomarker data,
  such as from tissue microarrays. Some methods attempt to retain differences 
  between batches that may be due to between-batch differences in "biological"
  factors that influence biomarker values.
As a successor of the packages 'BatchJobs' and 'BatchExperiments',
    this package provides a parallel implementation of the Map function for high
    performance computing systems managed by schedulers 'IBM Spectrum LSF'
    (<https://www.ibm.com/products/hpc-workload-management>),
    'OpenLava' (<https://www.openlava.org/>), 'Univa Grid Engine'/'Oracle Grid
    Engine' (<https://www.univa.com/>), 'Slurm' (<https://slurm.schedmd.com/>),
    'TORQUE/PBS'
    (<https://adaptivecomputing.com/cherry-services/torque-resource-manager/>),
    or 'Docker Swarm' (<https://docs.docker.com/engine/swarm/>).
    A multicore and socket mode allow the parallelization on a local machines,
    and multiple machines can be hooked up via SSH to create a makeshift
    cluster. Moreover, the package provides an abstraction mechanism to define
    large-scale computer experiments in a well-organized and reproducible way.
Compute bounds for the treatment effect 
    after adjusting for the presence of omitted variables in linear  
    econometric models, according to the method of Basu (2022) <arXiv:2203.12431>. 
    You supply the data, identify the outcome and 
    treatment variables and additional regressors. The main functions
    will compute bounds for the bias-adjusted treatment effect. Many
    plot functions allow easy visualization of results.
Survey systems and other third-party data sources commonly use non-standard representations of logical values when
    it comes to qualitative data - "Yes", "No" and "N/A", say. batman is a package designed to seamlessly convert these into logicals.
    It is highly localised, and contains equivalents to boolean values in languages including German, French, Spanish, Italian,
    Turkish, Chinese and Polish.
Real-time quantitative polymerase chain reaction (qPCR) data
    sets by Batsch et al. (2008) <doi:10.1186/1471-2105-9-95>. This package
    provides five data sets, one for each PCR target: (i) rat SLC6A14,
    (ii) human SLC22A13, (iii) pig EMT, (iv) chicken ETT, and (v) human
    GAPDH. Each data set comprises a five-point, four-fold dilution series.
    For each concentration there are three replicates. Each amplification curve
    is 45 cycles long. Original raw data file:
    <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-95/MediaObjects/12859_2007_2080_MOESM5_ESM.xls>.
Battery reduction is a method used in data reduction. It uses Gram-Schmidt orthogonal rotations to find out a subset of variables best representing the original set of variables.
Collection of tools to work with European basketball data. Functions available are related to friendly 
	web scraping, data management and visualization. Data were obtained from <https://www.euroleaguebasketball.net/euroleague/>, 
	<https://www.euroleaguebasketball.net/eurocup/> and <https://www.acb.com/>, following the instructions 
        of their respectives robots.txt files, when available. Box score data are available for the three leagues. 
	Play-by-play data are also available for the Spanish league. Methods for analysis include a population pyramid, 
	2D plots, circular plots of players' percentiles, plots of players' monthly/yearly stats, team heatmaps, 
	team shooting plots, team four factors plots, cross-tables with the results of regular season games,
	maps of nationalities, combinations of lineups, possessions-related variables, timeouts,
	performance by periods, personal fouls and offensive rebounds. 
	Please see Vinue (2020) <doi:10.1089/big.2018.0124>.
Simulating synthetic clumped isotope dataset, fitting
    linear regression models under Bayesian and non-Bayesian frameworks,
    and generating temperature reconstructions for the same two approaches.
    Please note that models implemented in this package are described
    in Roman-Palacios et al. (2021) <doi:10.1002/essoar.10507995.1>.
A Bayesian hybrid approach for inferring Directed Acyclic Graphs
    (DAGs) for continuous, discrete, and mixed data. The algorithm can use the 
    graph inferred by another more efficient graph inference method as input;
    the input graph may contain false edges or undirected edges but can help
    reduce the search space to a more manageable size. A Bayesian Markov chain
    Monte Carlo algorithm is then used to infer the probability of direction and
    absence for the edges in the network.
    References:
    Martin and Fu (2019) <arXiv:1909.10678>.
Implements the Bayesian FDR control described by 
    Newton et al. (2004), <doi:10.1093/biostatistics/5.2.155>.
    Allows optimisation and visualisation of expected error rates based on
    tail posterior probability tests.
    Based on code written by Catalina Vallejos for BASiCS, see
    Beyond comparisons of means: understanding changes in gene expression at the
    single-cell level Vallejos et al. (2016) <doi:10.1186/s13059-016-0930-3>.
As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty for quantile regression in genetic analysis. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in 'C++' and R.
Contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 <doi:10.18637/jss.v076.i01>).
A suite of functions that allow the user to analyze A/B test
    data in a Bayesian framework. Intended to be a drop-in replacement for
    common frequentist hypothesis test such as the t-test and chi-sq test.
Performs Bayesian estimation of the additive main effects and multiplicative interaction (AMMI) model. The method is explained in Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius, P.L. (2011) (<doi:10.2135/cropsci2010.06.0343>).
Provides a Bayesian version of the analysis of variance based on a three-component Gaussian mixture for which a Gibbs sampler produces posterior draws. For details about the Bayesian ANOVA based on Gaussian mixtures, see Kelter (2019) <arXiv:1906.07524>.
The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX by Bierens (1987) <doi:10.1016/0304-4076(87)90086-8>. In this package we estimate the ARIMAX model using Bayesian framework.
Computes Bayesian assurance under various 
    settings characterized by different assumptions and objectives, including 
    precision-based conditions, credible intervals, and goal functions. 
    All simulation-based functions included in this package rely on a two-stage 
    Bayesian method that assigns two distinct priors to evaluate the 
    probability of observing a positive outcome, which addresses subtle 
    limitations that take place when using the standard single-prior approach. 
    For more information, please refer to Pan and Banerjee (2021)
    <arXiv:2112.03509>.
The Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models are used for modelling the volatile multivariate data sets. In this package a variant of MGARCH called BEKK (Baba, Engle, Kraft, Kroner) proposed by Engle and Kroner (1995) <http://www.jstor.org/stable/3532933> has been used to estimate the bivariate time series data using Bayesian technique.
Fully Bayesian inference for estimating the number of clusters and related parameters to heterogeneous binary data.
A hodgepodge of hopefully helpful functions. Two of these perform
    shrinkage estimation: one using a simple weighted method where the user can
    specify the degree of shrinkage required, and one using James-Stein shrinkage
    estimation for the case of unequal variances.
Functions for performing the Bayesian bootstrap as introduced by
    Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the result.
    The implementation can handle both summary statistics that works on a
    weighted version of the data and summary statistics that works on a
    resampled data set.
Smoothed lexis diagrams with Bayesian method specifically tailored to cancer 
             incidence data. Providing to calculating slope and constructing credible interval.
             LC Chien et al. (2015) <doi:10.1080/01621459.2015.1042106>. 
             LH Chien et al. (2017) <doi:10.1002/cam4.1102>.
Performs CACE (Complier Average Causal Effect analysis) on either a single study or meta-analysis of datasets with binary outcomes, using either complete or incomplete noncompliance information. Our package implements the Bayesian methods proposed in Zhou et al. (2019) <doi:10.1111/biom.13028>, which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) <doi:10.1080/01621459.2021.1900859>, with an application example on Epidural Analgesia.
Combine diverse evidence across multiple studies to test a high level scientific theory. The methods can also be used as an alternative to a standard meta-analysis.
Bayesian multivariate binary (probit) regression
    models for analysis of ecological communities.
Tools for Bayesian copula generalized linear models (GLMs). 
             The sampling scheme is based on Pitt, Chan, and Kohn (2006) <doi:10.1093/biomet/93.3.537>. 
             Regression parameters (including coefficients and dispersion parameters) are
             estimated via the adaptive random walk Metropolis approach developed by
             Haario, Saksman, and Tamminen (1999) <doi:10.1007/s001800050022>.
             The prior for the correlation matrix is based on Hoff (2007) <doi:10.1214/07-AOAS107>.
A set of functions to allow analysis of count data (such
        as faecal egg count data) using Bayesian MCMC methods.  Returns
        information on the possible values for mean count, coefficient
        of variation and zero inflation (true prevalence) present in
        the data.  A complete faecal egg count reduction test (FECRT)
        model is implemented, which returns inference on the true
        efficacy of the drug from the pre- and post-treatment data
        provided, using non-parametric bootstrapping as well as using
        Bayesian MCMC.  Functions to perform power analyses for faecal
        egg counts (including FECRT) are also provided.
Propose a parametric fit for censored linear regression models based on SMSN distributions, from a Bayesian perspective. Also, generates SMSN random variables.
Simulation and analysis of Bayesian adaptive clinical trials for binomial, Gaussian, and time-to-event data types, 
      incorporates historical data and allows early stopping for futility or early success. The package uses novel 
      and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, 
      and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data 
      into the analysis via the power prior or non-informative priors.
Algorithms for computing and generating plots with and without error bars for Bayesian cluster validity index (BCVI) (N. Wiroonsri, O. Preedasawakul (2024) <arXiv:2402.02162>) based on several underlying cluster validity indexes (CVIs) including Calinski-Harabasz, Chou-Su-Lai, Davies-Bouldin, Dunn,  Pakhira-Bandyopadhyay-Maulik, Point biserial correlation, the score function, Starczewski, and Wiroonsri indices for hard clustering, and Correlation Cluster Validity, the generalized C, HF, KWON, KWON2, Modified Pakhira-Bandyopadhyay-Maulik, Pakhira-Bandyopadhyay-Maulik, Tang, Wiroonsri-Preedasawakul, Wu-Li, and Xie-Beni indices for soft clustering. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). Though BCVI is compatible with any underlying existing CVIs, we recommend users to use either WI or WP as the underlying CVI.
Functions for Bayesian Data Analysis, with datasets from
        the book "Bayesian data Analysis (second edition)" by Gelman,
        Carlin, Stern and Rubin. Not all datasets yet, hopefully
        completed soon.
Bayesian estimation of dynamic conditional correlation GARCH model for multivariate time series volatility (Fioruci, J.A., Ehlers, R.S. and Andrade-Filho, M.G., (2014). <doi:10.1080/02664763.2013.839635>.
The proposed event-driven approach for Bayesian two-stage single-arm phase II trial design is a novel clinical trial design and can be regarded as an extension of the Simon’s two-stage design with the time-to-event endpoint. This design is motivated by cancer clinical trials with immunotherapy and molecularly targeted therapy, in which time-to-event endpoint is often a desired endpoint.
Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic 
    factor analysis is a dimension reduction tool for multivariate time series.
    'bayesdfa' extends conventional dynamic factor models in several ways. 
    First, extreme events may be estimated in the latent trend by modeling
    process error with a student-t distribution. Second, alternative constraints
    (including proportions are allowed). Third, the estimated
    dynamic factors can be analyzed with hidden Markov models to evaluate
    support for latent regimes.
Provide early termination phase II trial designs with a
    decreasingly informative prior (DIP) or a regular Bayesian prior
    chosen by the user. The program can determine the minimum planned
    sample size necessary to achieve the user-specified admissible
    designs. The program can also perform power and expected sample size
    calculations for the tests in early termination Phase II trials.
    See Wang C and Sabo RT (2022) <doi:10.18203/2349-3259.ijct20221110>;
    Sabo RT (2014) <doi:10.1080/10543406.2014.888441>.
Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) <doi:10.1111/rssc.12535>. However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time.
Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.
The 'BayesDLMfMRI' package performs statistical analysis for task-based functional magnetic resonance imaging (fMRI) data at both individual and group levels. The analysis to detect brain activation at the individual level is based on modeling the fMRI signal using Matrix-Variate Dynamic Linear Models (MDLM). The analysis for the group stage is based on posterior distributions of the state parameter obtained from the modeling at the individual level. In this way, this package offers several R functions with different algorithms to perform inference on the state parameter to assess brain activation for both individual and group stages. Those functions allow for parallel computation when the analysis is performed for the entire brain as well as analysis at specific voxels when it is required.
             References: Cardona-Jiménez (2021) <doi:10.1016/j.csda.2021.107297>;
             Cardona-Jiménez (2021) <arXiv:2111.01318>.
Functions for data augmentation using the Bayesian discount prior 
    method for single arm and two-arm clinical trials, as described in Haddad 
    et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior 
    methodology was developed in collaboration with the The Medical Device 
    Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.
Determines effective sample size of a parametric prior distribution
    in Bayesian models. For a web-based Shiny application related to this package, see <https://implement.shinyapps.io/bayesess/>.
A suite of functions for computing
    various Bayes factors for simple designs, including contingency tables,
    one- and two-sample designs, one-way designs, general ANOVA designs, and
    linear regression.
Allows Bayesian borrowing from a historical dataset for time-to-
    event data. A flexible baseline hazard function is achieved via a piecewise
    exponential likelihood with time varying split points and smoothing prior on the
    historic baseline hazards. The method is described in Scott and Lewin (2024) 
    <arXiv:2401.06082>.
Implementation of 'BayesFlux.jl' for R; It extends the famous 
             'Flux.jl' machine learning library to Bayesian Neural Networks. 
             The goal is not to have the fastest production ready 
             library, but rather to allow more people to be able 
             to use and research on Bayesian Neural Networks.
Collection of procedures to perform Bayesian analysis on a variety
    of factor models. Currently, it includes: "Bayesian Exploratory Factor 
    Analysis" (befa) from G. Conti, S. Frühwirth-Schnatter, J.J. Heckman, 
    R. Piatek (2014) <doi:10.1016/j.jeconom.2014.06.008>, an approach to 
    dedicated factor analysis with stochastic search on the structure of the 
    factor loading matrix. The number of latent factors, as well as the 
    allocation of the manifest variables to the factors, are not fixed a priori 
    but determined during MCMC sampling.
Performs a spatial Bayesian general linear model (GLM) for task 
    functional magnetic resonance imaging (fMRI) data on the cortical surface. 
    Additional models include group analysis and inference to detect thresholded
    areas of activation. Includes direct support for the 'CIFTI' neuroimaging 
    file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. 
    Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D. 
    Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) 
    <doi:10.1016/j.neuroimage.2022.118908>.
Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range 
  of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic 
  Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic 
  volatility models for univariate time series.  Prior specifications are flexible and explicitly encourage 
  users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed 
  and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes 
  factor and leave-one-out cross-validation methods. References: Hyndman (2017)
    <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
The 'bayesGAM' package is designed to provide a user friendly option to fit univariate and multivariate response Generalized Additive Models (GAM) using Hamiltonian Monte Carlo (HMC) with few technical burdens.  The functions in this package use 'rstan' (Stan Development Team 2020)  to call 'Stan' routines that run the HMC simulations. The 'Stan' code for these models is already pre-compiled for the user. The programming formulation for models in 'bayesGAM' is designed to be familiar to analysts who fit statistical models in 'R'.
    Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of statistical software, 76(1). 
    Stan Development Team. 2018. RStan: the R interface to Stan. R package version 2.17.3.   <https://mc-stan.org/>
    Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418.
    Betancourt, Michael, and Mark Girolami. "Hamiltonian Monte Carlo for hierarchical models." Current trends in Bayesian methodology with applications 79.30 (2015): 2-4.
    Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>,
    Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, 
    Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, 
    Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.
    Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric regression (No. 12). Cambridge university press. ISBN: 978-0521785167.
Provides the bayesGARCH() function which performs the
    Bayesian estimation of the GARCH(1,1) model with Student's t innovations as described in Ardia (2008) <doi:10.1007/978-3-540-78657-3>.
Performs parametric mediation analysis using the Bayesian g-formula approach for binary and continuous outcomes. The methodology is based on Comment (2018) <doi:10.5281/zenodo.1285275> and a demonstration of its application can be found at Yimer et al. (2022) <doi:10.48550/arXiv.2210.08499>.
A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (<https://www.nature.com/articles/s41598-018-28130-5 >).
Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach.
Estimate fish length-at-age models using MCMC analysis with 
    'rstan' models. This package allows a multimodel approach to growth fitting
    to be applied to length-at-age data and is supported by further analyses to 
    determine model selection and result presentation. The core methods of this 
    package are presented in Smart and Grammer (2021) "Modernising fish and 
    shark growth curves with Bayesian length-at-age models". PLOS ONE 16(2):
    e0246734 <doi:10.1371/journal.pone.0246734>.
Fits Bayesian grouped weighted quantile sum (BGWQS) regressions for one or more chemical groups with binary outcomes. Wheeler DC et al. (2019) <doi:10.1016/j.sste.2019.100286>.
Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels'
    via 'bayesian' <doi:10.5281/zenodo.4426836>. 'tidymodels' is a collection of
    packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
    The technical details of 'brms' and 'Stan' are described in Bürkner (2017)
    <doi:10.18637/jss.v080.i01>, Bürkner (2018) <doi:10.32614/RJ-2018-017>,
    and Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
The Epidemic Type Aftershock Sequence  (ETAS) model is one of the best-performing methods for modeling and forecasting earthquake occurrences. This package implements Bayesian estimation routines to draw samples from the full posterior distribution of the model parameters, given an earthquake catalog. The paper on which this package is based is Gordon J. Ross - Bayesian Estimation of the ETAS Model for Earthquake Occurrences (2016), available from the below URL.
Contains the functions to use the econometric methods in the paper Bryzgalova, Huang, and Julliard (2023) <doi:10.1111/jofi.13197>. In this package, we provide a novel Bayesian framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems. For a stand-alone model, we provide functions including BayesianFM() and BayesianSDF() to deliver reliable price of risk estimates for both tradable and nontradable factors. For competing factors and possibly nonnested models, we provide functions including continuous_ss_sdf(), continuous_ss_sdf_v2(), and dirac_ss_sdf_pvalue() to analyze high-dimensional models. If you use this package, please cite the paper. We are thankful to Yunan Ding and Jingtong Zhang for their research assistance. Any errors or omissions are the responsibility of the authors.
Adjust the Gamma regression models from a Bayesian perspective described by Cepeda and Urdinola (2012) <doi:10.1080/03610918.2011.600500>, modeling the parameters of mean and shape and using different link functions for the parameter associated to the mean. And calculates different adjustment statistics such as the Akaike information criterion and Bayesian information criterion.
Implements a data-augmented block Gibbs sampler for simulating the posterior distribution of concentration matrices for specifying the topology and parameterization of a Gaussian Graphical Model (GGM). This sampler was originally proposed in Wang (2012) <doi:10.1214/12-BA729>.
Functional differences between the cerebral hemispheres 
    are a fundamental characteristic of the human brain. Researchers 
    interested in studying these differences often infer underlying 
    hemispheric dominance for a certain function (e.g., language) from 
    laterality indices calculated from observed performance or brain 
    activation measures . However, any inference from observed measures 
    to latent (unobserved) classes has to consider the prior probability 
    of class membership in the population. The provided functions 
    implement a Bayesian model for predicting hemispheric dominance from
    observed laterality indices (Sorensen and Westerhausen, Laterality: 
    Asymmetries of Body, Brain and Cognition, 2020, <doi:10.1080/1357650X.2020.1769124>).
Bayesian MCPMod (Fleischer et al. (2022)
    <doi:10.1002/pst.2193>) is an innovative method that improves the
    traditional MCPMod by systematically incorporating historical data,
    such as previous placebo group data. This R package offers functions
    for simulating, analyzing, and evaluating Bayesian MCPMod trials with
    normally distributed endpoints.  It enables the assessment of trial
    designs incorporating historical data across various true
    dose-response relationships and sample sizes. Robust mixture prior
    distributions, such as those derived with the Meta-Analytic-Predictive
    approach (Schmidli et al. (2014) <doi:10.1111/biom.12242>), can be
    specified for each dose group.  Resulting mixture posterior
    distributions are used in the Bayesian Multiple Comparison Procedure
    and modeling steps.  The modeling step also includes a weighted model
    averaging approach (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).
    Estimated dose-response relationships can be bootstrapped and
    visualized.
We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results.
A "Shiny"" web application for creating interactive Bayesian Network models,
    learning the structure and parameters of Bayesian networks, and utilities for classic
    network analysis.
Simulating the sequential multi-arm multi-stage or platform trial with Bayesian approach using the 'rstan' package, which provides the R interface for the Stan. 
    This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. 
    Additionally, it allows for the study of time trend problems in platform trials. 
    There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening.
    The Bayesian adaptive randomisation approaches are described in:
    Trippa et al. (2012) <doi:10.1200/JCO.2011.39.8420> and
    Wathen et al. (2017) <doi:10.1177/1740774517692302>.
    The randomisation algorithm is described in: 
    Zhao W <doi:10.1016/j.cct.2015.06.008>.
    The analysis methods of time trend effect in platform trial are described in:
    Saville et al. (2022) <doi:10.1177/17407745221112013> and
    Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>.
A collection of methods to determine the required sample size for
    the evaluation of inequality constrained hypotheses by means of a Bayes 
    factor. Alternatively, for a given sample size, the unconditional error 
    probabilities or the expected conditional error probabilities can be
    determined. Additional material on the methods in this package is 
    available in Klaassen, F., Hoijtink, H. & Gu, X. (2019) 
    <doi:10.31219/osf.io/d5kf3>.
Functions to plot and help understand positive and negative
    predictive values (PPV and NPV), and their relationship with
    sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007)
    <doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of
    the technical concepts and Navarrete et al. (2015) for a practical
    explanation about the importance of their understanding
    <doi:10.3389/fpsyg.2015.01327>.
General-purpose MCMC and SMC samplers, as well as plot and
    diagnostic functions for Bayesian statistics, with a particular focus on
    calibrating complex system models. Implemented samplers include various
    Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the
    T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential
    Monte Carlo (SMC) particle filter.
Performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated.
Efficient Markov Chain Monte Carlo (MCMC) algorithms for the
    fully Bayesian estimation of vectorautoregressions (VARs) featuring
    stochastic volatility (SV). Implements state-of-the-art shrinkage
    priors following Gruber & Kastner (2023) <arXiv:2206.04902>.
    Efficient equation-per-equation estimation following Kastner & Huber
    (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021)
    <doi:10.1016/j.jeconom.2021.11.010>.
Various algorithms for segmentation of 2D and 3D images, such
    as computed tomography and satellite remote sensing. This package implements
    Bayesian image analysis using the hidden Potts model with external field
    prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>.
    Latent labels are sampled using chequerboard updating or Swendsen-Wang.
    Algorithms for the smoothing parameter include pseudolikelihood, path sampling,
    the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC),
    and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to
    <doi:10.1007/978-3-030-42553-1_6> for an overview and also to <doi:10.1007/s11222-014-9525-6>
    and <doi:10.1214/18-BA1130> for further details of specific algorithms.
Bayesian Latent Class Analysis using several different
        methods.
Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-012-0193-x>. Subnational projections are also supported.
Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn, He and Lopes (2018) <arXiv:1806.05738>.
Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.
Tools for sampling from the PolyaGamma distribution based on Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>.  Useful for logistic regression.
Adjusts longitudinal regression models using Bayesian methodology 
            for covariance structures of composite symmetry (SC), 
            autoregressive ones of order 1 AR (1) and 
            autoregressive moving average of order (1,1) ARMA (1,1).
Covers many important models used
  in marketing and micro-econometrics applications. 
  The package includes:
  Bayes Regression (univariate or multivariate dep var),
  Bayes Seemingly Unrelated Regression (SUR),
  Binary and Ordinal Probit,
  Multinomial Logit (MNL) and Multinomial Probit (MNP),
  Multivariate Probit,
  Negative Binomial (Poisson) Regression,
  Multivariate Mixtures of Normals (including clustering),
  Dirichlet Process Prior Density Estimation with normal base,
  Hierarchical Linear Models with normal prior and covariates,
  Hierarchical Linear Models with a mixture of normals prior and covariates,
  Hierarchical Multinomial Logits with a mixture of normals prior
     and covariates,
  Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates,
  Hierarchical Negative Binomial Regression Models,
  Bayesian analysis of choice-based conjoint data,
  Bayesian treatment of linear instrumental variables models,
  Analysis of Multivariate Ordinal survey data with scale
     usage heterogeneity (as in Rossi et al, JASA (01)),
  Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see
  Jiang, Manchanda, Rossi 2009)
  For further reference, consult our book, Bayesian Statistics and
  Marketing by Rossi, Allenby and McCulloch (Wiley first edition 2005 and second forthcoming) and Bayesian Non- and Semi-Parametric
  Methods and Applications (Princeton U Press 2014).
An implementation of the Bayesian version of the Mallows rank model
    (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>;
    Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>;
    Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>;
    Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings
    and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule,
    Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be
    analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well
    as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the
    posterior distributions of parameters are provided. The package also provides functions for estimating
    the partition function (normalizing constant) of the Mallows rank model, both with the importance
    sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm
    (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).
Bayesian tools that can be used to reconcile, or mass balance, mass flow rate data collected from chemical or particulate separation processes aided by constraints governed by the conservation of mass.
    Functions included in the package aid the user in organizing and constraining data, using Markov chain Monte Carlo methods to obtain samples from Bayesian models, and in computation of the marginal likelihood of the data, given a particular model, for model selection.  Marginal likelihood is approximated by methods in Chib S (1995) <doi:10.2307/2291521>.
Computes Bayesian posterior distributions of predictions, marginal effects, and differences of marginal effects for various generalized linear models. Importantly, the posteriors are on the mean (response) scale, allowing for more natural interpretation than summaries on the link scale. Also, predictions and marginal effects of the count probabilities for Poisson and negative binomial models can be computed.
A collection of functions allowing to derive the posterior distribution of the model parameters in random-effects meta-analysis or meta-regression, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.; For more details, see also Roever C (2020) <doi:10.18637/jss.v093.i06>, or Roever C and Friede T (2022) <doi:10.1016/j.cmpb.2022.107303>.
Contains a split population survival estimator that models the 
    misclassification probability of failure versus right-censored events.  
    The split population survival estimator is described in 
    Bagozzi et al. (2019) <doi:10.1017/pan.2019.6>.
Producing probabilistic projections of net migration rate for all countries of the world
    or for subnational units using a Bayesian hierarchical model by Azose an Raftery (2015) <doi:10.1007/s13524-015-0415-0>.
Fits finite mixture models of univariate Gaussian distributions using JAGS within a Bayesian framework.
Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with Lasso Shrinkage and
        Stratification. As a Bayesian dynamic survival model, it relaxes the proportional-hazard assumption. Lasso shrinkage controls
        overfitting, given the increase in the number of free parameters in the model due to presence of two Weibull components
        in the hazard function.
Create life tables with a Bayesian approach, which can be very useful for modelling a complex health process when considering multiple predisposing factors and multiple coexisting health conditions. Details for this method can be found in: Lynch, Scott, et al., (2022) <doi:10.1177/00811750221112398>; Zang, Emma, et al., (2022) <doi:10.1093/geronb/gbab149>.
Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in Heligman, L., & Pollard, J. H. (1980) <doi:10.1017/S0020268100040257> and Dellaportas, Petros, et al. (2001) <doi:10.1111/1467-985X.00202>, and dynamic linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009) <doi:10.1007/b135794_2>). While Heligman-Pollard has parameters with a straightforward interpretation yielding some rich analysis, the dynamic linear model provides a very flexible adjustment of the mortality curves by controlling the discount factor value. Closing methods for both Heligman-Pollard and dynamic linear model were also implemented according to Dodd, Erengul, et al. (2018) <https://www.jstor.org/stable/48547511>. The Bayesian Lee-Carter model is also implemented to fit historical mortality tables time series to predict the mortality in the following years and to do improvement analysis, as seen in Lee, R. D., & Carter, L. R. (1992) <doi:10.1080/01621459.1992.10475265> and Pedroza, C. (2006) <doi:10.1093/biostatistics/kxj024>.
Methods for assessing animal movement from telemetry and biologging
    data using non-parametric Bayesian methods. This includes features for pre-
    processing and analysis of data, as well as the visualization of results
    from the models. This framework does not rely on standard parametric density
    functions, which provides flexibility during model fitting. Further details 
    regarding part of this framework can be found in Cullen et al. (2021) <doi:10.1101/2020.11.05.369702>.
Bayesian analysis of multivariate receptor modeling. The package consists of implementations of the methods of Park and Oh (2015) <doi:10.1016/j.chemolab.2015.08.021>.The package uses 'JAGS'(Just Another Gibbs Sampler) to generate Markov chain Monte Carlo samples of parameters.
A two-step Bayesian approach for mode inference following 
      Cross, Hoogerheide, Labonne and van Dijk (2024) <doi:10.1016/j.econlet.2024.111579>).
      First, a mixture distribution is fitted on the data using a sparse finite
      mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of
      mixture components does not have to be known; the size of the mixture is
      estimated endogenously through the SFM approach. Second, the modes of the
      estimated mixture at each MCMC draw are retrieved using algorithms
      specifically tailored for mode detection. These estimates are then used to
      construct posterior probabilities for the number of modes, their locations
      and uncertainties, providing a powerful tool for mode inference.
Objective Bayesian inference procedures for the parameters of the
    multivariate random effects model with application to multivariate
    meta-analysis. The posterior for the model parameters, namely the overall
    mean vector and the between-study covariance matrix, are assessed by
    constructing Markov chains based on the Metropolis-Hastings algorithms as
    developed in Bodnar and Bodnar (2021) (<arXiv:2104.02105>). The
    Metropolis-Hastings algorithm is designed under the assumption of the
    normal distribution and the t-distribution when the Berger and Bernardo
    reference prior and the Jeffreys prior are assigned to the model parameters.
    Convergence properties of the generated Markov chains are investigated by
    the rank plots and the split hat-R estimate based on the rank normalization,
    which are proposed in Vehtari et al. (2021) (<DOI:10.1214/20-BA1221>).
Implementation of No-Effect-Concentration estimation that uses 'brms' (see Burkner (2017)<doi:10.18637/jss.v080.i01>; Burkner (2018)<doi:10.32614/RJ-2018-017>; Carpenter 'et al.' (2017)<doi:10.18637/jss.v076.i01> to fit concentration(dose)-response data using Bayesian methods for the purpose of estimating 'ECx' values, but more particularly 'NEC' (see Fox (2010)<doi:10.1016/j.ecoenv.2009.09.012>), 'NSEC' (see Fisher and Fox (2023)<doi:10.1002/etc.5610>), and 'N(S)EC (see Fisher et al. 2023<doi:10.1002/ieam.4809>). This package expands and supersedes an original version implemented in R2jags, see Fisher, Ricardo and Fox (2020)<doi:10.5281/ZENODO.3966864>.
Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks <https://www.jmlr.org/papers/v6/cowell05a.html>. For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks <doi:10.18637/jss.v094.i03>. The optional 'cyjShiny' package for running the Shiny app is available at <https://github.com/cytoscape/cyjShiny>. Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub.
Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <arXiv:1702.00434v2>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the 'nimble' package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.
The proposed group-sequential trial design is based on Bayesian methods for ordinal endpoints, 
             including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, 
             and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with 
             various scenarios.
             Richard J. Barker, William A. Link (2013) <doi:10.1080/00031305.2013.791644>.
             Thomas A. Murray, Ying Yuan, Peter F. Thall, Joan H. Elizondo, Wayne L.Hofstetter (2018) <doi:10.1111/biom.12842>.
             Chengxue Zhong, Haitao Pan, Hongyu Miao (2021) <arXiv:2108.06568>.
Fits a piecewise exponential hazard to survival data using a
    Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive
    formulation for the spatial dependency in the hazard rates for each piece.
    This function uses Metropolis- Hastings-Green MCMC to allow the number of split
    points to vary and also uses Stochastic Search Variable Selection to determine
    what covariates drive the risk of the event. This function outputs trace plots
    depicting the number of split points in the hazard and the number of variables
    included in the hazard. The function saves all posterior quantities to the
    desired path.
A lightweight modelling syntax for defining likelihoods and priors
    and for computing Bayes factors for simple one parameter models. It includes
    functionality for computing and plotting priors, likelihoods, and model
    predictions. Additional functionality is included for computing and plotting
    posteriors.
Plotting functions for posterior analysis, MCMC diagnostics,
    prior and posterior predictive checks, and other visualizations
    to support the applied Bayesian workflow advocated in
    Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>.
    The package is designed not only to provide convenient functionality
    for users, but also a common set of functions that can be easily used by
    developers working on a variety of R packages for Bayesian modeling,
    particularly (but not exclusively) packages interfacing with 'Stan'.
The R-package bayespm implements Bayesian Statistical Process Control and Monitoring (SPC/M) methodology. These methods utilize available prior information and/or historical data, providing efficient online quality monitoring of a process, in terms of identifying moderate/large transient shifts (i.e., outliers) or persistent shifts of medium/small size in the process. These self-starting, sequentially updated tools can also run under complete absence of any prior information. The Predictive Control Charts (PCC) are introduced for the quality monitoring of data from any discrete or continuous distribution that is a member of the regular exponential family. The Predictive Ratio CUSUMs (PRC) are introduced for the Binomial, Poisson and Normal data (a later version of the library will cover all the remaining distributions from the regular exponential family). The PCC targets transient process shifts of typically large size (a.k.a. outliers), while PRC is focused in detecting persistent (structural) shifts that might be of medium or even small size. Apart from monitoring, both PCC and PRC provide the sequentially updated posterior inference for the monitored parameter. Bourazas K., Kiagias D. and Tsiamyrtzis P. (2022) "Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs" <doi:10.1080/00224065.2021.1916413>, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs" <doi:10.1080/00224065.2022.2161434>, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Design and properties of the predictive ratio cusum (PRC) control charts" <doi:10.1080/00224065.2022.2161435>.
Presence-Only data is best modelled with a Point Process Model.
    The work of Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569> provides
    a way to use exact Bayesian inference to model this type of data, which is
    implemented in this package.
Generating population projections for all countries of the world using several probabilistic components, such as total fertility rate and life expectancy (Raftery et al., 2012 <doi:10.1073/pnas.1211452109>).
An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 <doi:10.2139/ssrn.2765419>), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) <doi:10.1111/j.1540-5907.2012.00602.x>, the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>). This package can be used with MCMC output generated by any Bayesian estimation tool including 'JAGS', 'BUGS', 'MCMCpack', and 'Stan'.
Bayesian power/type I error calculation and model fitting using 
  the power prior and the normalized power prior for generalized linear models.
  Detailed examples of applying the package are available at <doi:10.32614/RJ-2023-016>. 
  The Bayesian clinical trial design methodology is described in Chen et al. (2011) 
  <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) 
  <doi:10.1093/biostatistics/kxy009>. The normalized power prior is described in Duan et al. (2006) 
  <doi:10.1002/env.752> and Ibrahim et al. (2015) <doi:10.1002/sim.6728>.
Bayesian power/type I error calculation and model fitting using 
  the power prior and the normalized power prior for proportional hazards models
  with piecewise constant hazard. The methodology and examples of 
  applying the package are detailed in <doi:10.48550/arXiv.2404.05118>.
  The Bayesian clinical trial design methodology is described in 
  Chen et al. (2011) <doi:10.1111/j.1541-0420.2011.01561.x>, 
  and Psioda and Ibrahim (2019) <doi:10.1093/biostatistics/kxy009>. 
  The proportional hazards model with piecewise constant hazard is detailed in 
  Ibrahim et al. (2001) <doi:10.1007/978-1-4757-3447-8>.
Implementations in 'cpp' of the BayesProject algorithm (see G. Hahn, P. Fearnhead, I.A. Eckley (2020) <doi:10.1007/s11222-020-09966-2>) which implements a fast approach to compute a projection direction for multivariate changepoint detection, as well as the sum-cusum and max-cusum methods, and a wild binary segmentation wrapper for all algorithms.
Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.
Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) <doi:10.1007/978-3-030-67664-3_13>, MCMC reconciliation of count time series (Corani et al., 2022) <doi:10.48550/arXiv.2207.09322>, Bottom-Up Importance Sampling (Zambon et al., 2022) <doi:10.48550/arXiv.2210.02286>.
Fits linear or generalized linear regression models using Bayesian global-local shrinkage prior hierarchies as described in Polson and Scott (2010) <doi:10.1093/acprof:oso/9780199694587.003.0017>. Provides an efficient implementation of ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets using the algorithms summarized in Makalic and Schmidt (2016) <arXiv:1611.06649>.
Functionality for reliability estimates. For 'unidimensional' tests:
    Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower
    bound and coefficient omega_u ('unidimensional') in a Bayesian and a frequentist version. 
    For multidimensional tests: omega_t (total) and omega_h (hierarchical). 
    The results include confidence and credible intervals, the 
    probability of a coefficient being larger than a cutoff, 
    and a check for the factor models, necessary for the omega coefficients. 
    The method for the Bayesian 'unidimensional' estimates, except for omega_u, 
    is sampling from the posterior inverse 'Wishart' for the 
    covariance matrix based measures (see 'Murphy', 2007, 
    <https://groups.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>. 
    The Bayesian omegas (u, t, and h) are obtained by 
    'Gibbs' sampling from the conditional posterior distributions of 
    (1) the single factor model, (2) the second-order factor model, (3) the bi-factor model, 
    (4) the correlated factor model
    ('Lee', 2007, <doi:10.1002/9780470024737>).
Provides tools for the analysis of replication studies using Bayes factors (Pawel and Held, 2022) <doi:10.1111/rssb.12491>.
Provides functionality for determining the sample size of replication studies using Bayesian design approaches in the normal-normal hierarchical model (Pawel et al., 2022) <doi:10.48550/arXiv.2211.02552>.
Contains posterior samplers for the Bayesian piecewise linear log-hazard and piecewise exponential hazard models, including Cox models. Posterior mean restricted survival times are also computed for non-Cox an Cox models with only treatment indicators. The ApproxMean() function can be used to estimate restricted posterior mean survival times given a vector of patient covariates in the Cox model. Functions included to return the posterior mean hazard and survival functions for the piecewise exponential and piecewise linear log-hazard models. Chapple, AG, Peak, T, Hemal, A (2020). Under Revision.
To perform model estimation using MCMC algorithms with Bayesian methods for incomplete longitudinal studies on binary and ordinal outcomes that are measured repeatedly on subjects over time with drop-outs. Details about the method can be found in the vignette or <https://sites.google.com/view/kuojunglee/r-packages/bayesrgmm>.
Computation and visualization of Bayesian Regions of Evidence
    to systematically evaluate the sensitivity of a superiority or
    non-inferiority claim against any prior assumption of its assessors.
    Methodological details are elaborated by Hoefler and Miller
    (<https://osf.io/jxnsv>).  Besides generic functions, the package also
    provides an intuitive 'Shiny' application, that can be run in local R
    environments.
Runs hierarchical linear Bayesian models. Samples from the posterior
    distributions of model parameters in JAGS (Just Another Gibbs Sampler;
	Plummer, 2017, <http://mcmc-jags.sourceforge.net>). Computes Bayes factors for group
	parameters of interest with the Savage-Dickey density ratio (Wetzels,
	Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).
Provides datasets and functions used for analysis 
  and visualizations in the Bayes Rules! book (<https://www.bayesrulesbook.com>). 
  The package contains a set of functions that summarize and plot Bayesian models from some conjugate families 
  and another set of functions for evaluation of some Bayesian models.
Allows the reenactment of the R programs used in 
        the book Bayesian Essentials with R without further programming. 
        R code being available as well, they can be modified by the user
        to conduct one's own simulations. 
	    Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.
In p >> n settings, full posterior sampling using existing Markov chain Monte
    Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical
    perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.
Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and
    the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary
    information) - BLE_Ratio() - and to categorical data - BLE_Categorical().
    The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg()
    and it is also possible to achieve the design based estimators using vague prior distributions.    
    Based on Gonçalves, K.C.M, Moura, F.A.S and  Migon, H.S.(2014) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.
Generates different posterior distributions of adjusted odds ratio under different priors of sensitivity and specificity, and plots the models for comparison. It also provides estimations for the specifications of the models using diagnostics of exposure status with a non-linear mixed effects model. It implements the methods that are first proposed in <doi:10.1016/j.annepidem.2006.04.001> and <doi:10.1177/0272989X09353452>.
Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.
Contains Bayesian implementations of the Mixed-Effects Accelerated Failure Time (MEAFT) models
             for censored data. Those can be not only right-censored but also interval-censored,
	     doubly-interval-censored or misclassified interval-censored. The methods implemented in the package
	     have been published in Komárek and Lesaffre (2006, Stat. Modelling) <doi:10.1191/1471082X06st107oa>,
	     Komárek, Lesaffre and Legrand (2007, Stat. in Medicine) <doi:10.1002/sim.3083>, Komárek and Lesaffre (2007, Stat. Sinica) <https://www3.stat.sinica.edu.tw/statistica/oldpdf/A17n27.pdf>, Komárek and Lesaffre (2008, JASA) <doi:10.1198/016214507000000563>,
	     García-Zattera, Jara and Komárek (2016, Biometrics) <doi:10.1111/biom.12424>.
Performs unadjusted Bayesian survival analysis for right censored time-to-event data. The main function, BayesSurv(), computes the posterior mean and a credible band for the survival function and for the cumulative hazard, as well as the posterior mean for the hazard, starting from a piecewise exponential (histogram) prior with Gamma distributed heights that are either independent, or have a Markovian dependence structure.
    A function, PlotBayesSurv(), is provided to easily create plots of the posterior means of the hazard, cumulative hazard and survival function, with a credible band accompanying the latter two.
    The priors and samplers are described in more detail in Castillo and Van der Pas (2020) "Multiscale Bayesian survival analysis" <arXiv:2005.02889>. In that paper it is also shown that the credible bands for the survival function and the cumulative hazard can be considered confidence bands (under mild conditions) and thus offer reliable uncertainty quantification.
An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database.
Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.
Provides utilities to describe posterior
    distributions and Bayesian models. It includes point-estimates such as
    Maximum A Posteriori (MAP), measures of dispersion (Highest Density
    Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and
    indices used for null-hypothesis testing (such as ROPE percentage, pd
    and Bayes factors). References: Makowski et al. (2021) <doi:10.21105/joss.01541>.
Making probabilistic projections of total fertility rate for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-011-0040-5> <doi:10.18637/jss.v106.i08>. Subnational probabilistic projections are also supported <doi:10.4054/DemRes.2018.38.60>.
Provides tools for conducting Bayesian analyses and Bayesian model averaging 
    (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, 
    Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains 
    functions for creating a wide range of prior distribution objects, mixing posterior 
    samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc...
    The tools for working with prior distribution span from visualization, generating 'JAGS' 
    and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.
This is an implementation of BART:Bayesian Additive Regression Trees,
        by Chipman, George, McCulloch (2010).
Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) <DOI:10.2307/2669832>. The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) <DOI:10.2307/2669832> is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016).
Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) <doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014) <doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016) <doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016) <doi:10.1016/j.lindif.2017.01.018>.
Bayes factors and posterior probabilities in Linear models, 
    aimed at provide a formal Bayesian answer to testing and variable 
    selection problems.
Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate 'Stan' code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) <doi:10.31219/osf.io/w5dx6> The 'bayesvl' R package. Open Science Framework (May 18).
Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities.  In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point.  Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point.  For further details, see: Alexander C. Murph et al. (2023) <arXiv:2310.02940>.
Functions for exploring and visualising estimation results
  obtained with BayesX, a free software for estimating structured additive
  regression models (<https://www.uni-goettingen.de/de/bayesx/550513.html>). In addition,
  functions that allow to read, write and manipulate map objects that are required in spatial
  analyses performed with BayesX.
'BayesX' performs Bayesian inference in structured additive regression (STAR) models.
	The R package BayesXsrc provides the 'BayesX' command line tool for easy installation.
	A convenient R interface is provided in package R2BayesX.
Fits a Bayesian zero-inflated Bernoulli regression model handling (potentially) different covariates for the zero-inflated
    and non zero-inflated parts. See Moriña D, Puig P, Navarro A. (2021) <doi:10.1186/s12874-021-01427-2>.
A Bayesian, global planktic foraminifera core top calibration to 
    modern sea-surface temperatures. Includes four calibration models, 
    considering species-specific calibration parameters and seasonality.
This R package offers block Gibbs samplers for the Bayesian (adaptive) graphical lasso, ridge, and naive elastic net priors. These samplers facilitate the simulation of the posterior distribution of precision matrices for Gaussian distributed data and were originally proposed by: Wang (2012) <doi:10.1214/12-BA729>; Smith et al. (2022) <doi:10.48550/arXiv.2210.16290> and Smith et al. (2023) <doi:10.48550/arXiv.2306.14199>, respectively.
Bayesian approach to multidimensional scaling. The package consists of implementations of the methods of Oh and Raftery (2001)  <doi:10.1198/016214501753208690>.
BAYesian inference for MEDical designs in R. Functions for the 
    computation of Bayes factors for common biomedical research designs. 
    Implemented are functions to test the equivalence (equiv_bf), 
    non-inferiority (infer_bf), and superiority (super_bf) of an experimental 
    group compared to a control group on a continuous outcome measure. Bayes 
    factors for these three tests can be computed based on raw data (x, y) or 
    summary statistics (n_x, n_y, mean_x, mean_y, sd_x, sd_y [or ci_margin 
    and ci_level]).
Performs general Bayesian estimation method of linear–bilinear models for genotype × environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011) (<doi:10.1007/s13253-011-0063-9>).
Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods.
Enable users to evaluate long-term trends using a Generalized 
    Additive Modeling (GAM) approach. The model development includes selecting a 
    GAM structure to describe nonlinear seasonally-varying changes over time, 
    incorporation of hydrologic variability via either a river flow or salinity, 
    the use of an intervention to deal with method or laboratory changes 
    suspected to impact data values, and representation of left- and 
    interval-censored data. The approach has been applied to water quality data 
    in the Chesapeake Bay, a major estuary on the east coast of the United 
    States to provide insights to a range of management- and research-focused 
    questions.  Methodology described in Murphy (2019) 
    <doi:10.1016/j.envsoft.2019.03.027>.
A collection of miscellaneous functions for 
    copying objects to the clipboard ('Copy');
    manipulating strings ('concat', 'mgsub', 'trim', 'verlan'); 
    loading or showing packages ('library_with_dep', 'require_with_dep', 
    'sessionPackages'); 
    creating or testing for named lists ('nlist', 'as.nlist', 'is.nlist'), 
    formulas ('is.formula'), empty objects ('as.empty', 'is.empty'), 
    whole numbers ('as.wholenumber', 'is.wholenumber'); 
    testing for equality ('almost.equal', 'almost.zero') and computing 
    uniqueness ('almost.unique'); 
    getting modified versions of usual functions ('rle2', 'sumNA'); 
    making a pause or a stop ('pause', 'stopif'); 
    converting into a function ('as.fun'); 
    providing a C like ternary operator ('condition %?% true %:% false'); 
    finding packages and functions ('get_all_pkgs', 'get_all_funs');
    and others ('erase', '%nin%', 'unwhich', 'top', 'bot', 'normalize').
Barzilai-Borwein spectral methods for solving nonlinear
        system of equations, and for optimizing nonlinear objective
        functions subject to simple constraints. A tutorial style
        introduction to this package is available in a vignette on the
        CRAN download page or, when the package is loaded in an R
        session, with vignette("BB").
Implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan and Sossounov (2002, <doi:10.1002/jae.664>) and the algorithm of Lunde and Timmermann (2004, <doi:10.1198/073500104000000136>).  
       The package also contains functions for printing out the dating of the Bull and Bear states of the market, the descriptive statistics of the states, and functions for plotting the results. 
       For the sake of convenience, the package includes the monthly and daily data on the prices (not adjusted for dividends) of the S&P 500 stock market index.
We implemented a Bayesian-statistics approach for 
        subtraction of incoherent scattering from neutron total-scattering data. 
        In this approach, the estimated background signal associated with 
        incoherent scattering maximizes the posterior probability, which combines 
        the likelihood of this signal in reciprocal and real spaces with the prior 
        that favors smooth lines. The description of the corresponding approach 
        could be found at Gagin and Levin (2014) <DOI:10.1107/S1600576714023796>.
Set of functions to calculate Benthic Biotic Indices from
	composition data, obtained whether from morphotaxonomic inventories or
	sequencing data. Based on reference ecological weights publicly available for
	a set of commonly used marine biotic indices, such as AMBI (A Marine Biotic Index, Borja et al., 2000) <doi:10.1016/S0025-326X(00)00061-8>
	NSI (Norwegian Sensitivity Index) and ISI (Indicator Species Index) (Rygg 2013, <ISBN:978-82-577-6210-0>). It provides the ecological quality status of the samples based on each BBI as well as the normalized Ecological Quality Ratio.
Download data and metadata from the 'Bundesbank SDMX Web
    Service API' found at
    <https://www.bundesbank.de/en/statistics/time-series-databases/help-for-sdmx-web-service/web-service-interface-data>.
A fast and intuitive batch effect removal tool for single-cell data. BBKNN is originally used in the 'scanpy' python package, and now can be used with 'Seurat' seamlessly.
Supervised learning using Boltzmann Bayes model inference, 
    which extends naive Bayes model to include interactions. Enables 
    classification of data into multiple response groups based on a large 
    number of discrete predictors that can take factor values of 
    heterogeneous levels. Either pseudo-likelihood or mean field 
    inference can be used with L2 regularization, cross-validation, and 
    prediction on new data. 
    <doi:10.18637/jss.v101.i05>.
Miscellaneous helper functions for and from B. Bischl and
    some other guys, mainly for package development.
The method models RNA-seq reads using a mixture of 3 beta-binomial distributions to generate posterior probabilities for genotyping bi-allelic single nucleotide polymorphisms. Elena Vigorito, Anne Barton, Costantino Pitzalis, Myles J. Lewis and Chris Wallace (2023) <doi:10.1093/bioinformatics/btad393> "BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing."
Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package.
Features highly configurable search spaces via the 'paradox'
    package and optimizes every user-defined objective function. The package
    includes several optimization algorithms e.g. Random Search, Iterated
    Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in
    'mlr3hyperband'). bbotk is the base package of 'mlr3tuning', 'mlr3fselect'
    and 'miesmuschel'.
Functions to fit, via Expectation-Maximization (EM) algorithm, the Bessel and Beta regressions to a data set with a bounded continuous response variable. The Bessel regression is a new and robust approach proposed in the literature. The EM version for the well known Beta regression is another major contribution of this package. See details in the references Barreto-Souza, Mayrink and Simas (2022) <doi:10.1111/anzs.12354> and Barreto-Souza, Mayrink and Simas (2020) <arXiv:2003.05157>.
Posterior sampling for Spike-and-Slab LASSO prior in linear models from Nie and Rockova <arXiv:2011.14279>.
The blocked weighted bootstrap (BBW) is an estimation technique 
    for use with data from two-stage cluster sampled surveys in which either 
    prior weighting (e.g. population-proportional sampling or PPS as used in 
    Standardized Monitoring and Assessment of Relief and Transitions or SMART 
    surveys) or posterior weighting (e.g. as used in rapid assessment method or
    RAM and simple spatial sampling method or S3M surveys) is implemented. See 
    Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of 
    bootstrap to cluster samples. See Aaron et al (2016) 
    <doi:10.1371/journal.pone.0163176> and Aaron et al (2016) 
    <doi:10.1371/journal.pone.0162462> for application of the blocked weighted 
    bootstrap to estimate indicators from two-stage cluster sampled surveys.
Implementation of the BC3NET algorithm for gene regulatory network inference (de Matos Simoes and Frank Emmert-Streib, Bagging Statistical Network Inference from Large-Scale Gene Expression Data, PLoS ONE 7(3): e33624, <doi:10.1371/journal.pone.0033624>).
Implementing the Block Coordinate Ascent with One-Step Generalized Rosen (BCA1SG) algorithm on the semiparametric models for panel count data, interval-censored survival data, and degradation data. A comprehensive description of the BCA1SG algorithm can be found in Wang et al. (2020) <https://github.com/yudongstat/BCA1SG/blob/master/BCA1SG.pdf>. For details of the semiparametric models for panel count data, interval-censored survival data, and degradation data, please see Wellner and Zhang (2007) <doi:10.1214/009053607000000181>, Huang and Wellner (1997) <ISBN:978-0-387-94992-5>, and Wang and Xu (2010) <doi:10.1198/TECH.2009.08197>, respectively.
Computation of bootstrap confidence intervals in an almost automatic fashion as described in Efron and Narasimhan (2020, <doi:10.1080/10618600.2020.1714633>).
Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.
Calculates the prices of European options based on the universal solution provided by Bakshi, Cao and Chen (1997) <doi:10.1111/j.1540-6261.1997.tb02749.x>. This solution considers stochastic volatility, stochastic interest and random jumps. Please cite their work if this package is used.
It is very common nowadays for a study to collect multiple
    features and appropriately integrating multiple longitudinal features
    simultaneously for defining individual clusters becomes increasingly
    crucial to understanding population heterogeneity and predicting
    future outcomes.  'BCClong' implements a Bayesian consensus clustering
    (BCC) model for multiple longitudinal features via a generalized
    linear mixed model. Compared to existing packages, several key
    features make the 'BCClong' package appealing: (a) it allows
    simultaneous clustering of mixed-type (e.g., continuous, discrete and
    categorical) longitudinal features, (b) it allows each longitudinal
    feature to be collected from different sources with measurements taken
    at distinct sets of time points (known as irregularly sampled
    longitudinal data), (c) it relaxes the assumption that all features
    have the same clustering structure by estimating the feature-specific
    (local) clusterings and consensus (global) clustering.
Developed for the following tasks. Simulating, computing maximum likelihood 
             estimator, computing the Fisher information matrix, computing goodness-of-fit measures,
             and correcting bias of the ML estimator for a wide range of distributions fitted to units
             placed on progressive type-I interval censoring and progressive type-II censoring plans.  
             The methods of Cox and Snell (1968) <doi:10.1111/j.2517-6161.1968.tb00724.x> and
             bootstrap method for computing the bias-corrected maximum likelihood estimator.
A collection of functions for structure learning of causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo scheme for posterior inference of causal structures, parameters and causal effects between variables.
    References:
    F. Castelletti and A. Mascaro (2021) <doi:10.1007/s10260-021-00579-1>,
    F. Castelletti and A. Mascaro (2022) <arXiv:2201.12003>.
Search, query, and download tabular and
    'geospatial' data from the British Columbia Data Catalogue
    (<https://catalogue.data.gov.bc.ca/>).  Search catalogue data records
    based on keywords, data licence, sector, data format, and B.C.
    government organization. View metadata directly in R, download many
    data formats, and query 'geospatial' data available via the B.C.
    government Web Feature Service ('WFS') using 'dplyr' syntax.
Tools for Dating Business Cycles using Harding-Pagan (Quarterly Bry-Boschan) method and various plotting features.
Diagnostic tools based on two-way
        anova and median-polish residual plots for Bicluster output
        obtained from packages; "biclust" by Kaiser et al.(2008),"isa2"
        by Csardi et al. (2010) and "fabia" by Hochreiter et al.
        (2010). Moreover, It provides visualization tools for bicluster
        output and corresponding non-bicluster rows- or columns
        outcomes.  It has also extended the idea of Kaiser et al.(2008)
        which is, extracting bicluster output in a text format, by
        adding two bicluster methods from the fabia and isa2 R
        packages.
Produces an economic evaluation of a sample of suitable variables of
             cost and effectiveness / utility for two or more interventions,
             e.g. from a Bayesian model in the form of MCMC simulations.
             This package computes the most cost-effective alternative and
             produces graphical summaries and probabilistic sensitivity analysis,
             see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>.
A Bayesian model averaging approach to causal effect estimation
    based on the BCEE algorithm. Currently supports binary or continuous
    exposures and outcomes. For more details, see 
    Talbot et al. (2015) <doi:10.1515/jci-2014-0035>
    Talbot and Beaudoin (2022) <doi:10.1515/jci-2021-0023>.
Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) <doi:10.1214/19-BA1195> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arXiv:1309.1906>.
Fit and simulate a semiparametric bivariate correlated frailty models with proportional 
    hazard structure. Frailty distributions, such as gamma and lognormal models are 
    supported. Bivariate gamma fit is obtained using the approach given in Iachine (1995) and 
    lognormal fit is based on the approach by 
    Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>.
Fit and simulate bivariate correlated frailty models with
    proportional hazard structure. Frailty distributions, such as gamma and
    lognormal models are supported for semiparametric procedures. Frailty variances of the two subjects 
    can be varied or equal. Details on the models are available in book of 
    Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given 
    in Iachine (1995) with modifications. Lognormal fit is based on the approach
    by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Frailty distributions, 
    such as gamma, inverse gaussian and power variance frailty models are supported for parametric approach.
Fits generalised partial linear regression models using a Bayesian approach, where shape and
			smoothness constraints are imposed on nonparametrically modelled predictors through shape-restricted splines, and 
			no constraints are imposed on optional parametrically modelled covariates. See Meyer et al. (2011) <doi/10.1080/10485252.2011.597852> 
			for more details. IMPORTANT: before installing 'bcgam', you need to install 'Rtools' (Windows) or 'Xcode' (Mac OS X). These are	
			required for the correct installation of 'nimble' (<https://r-nimble.org/download>).
Provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>.
Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, 'JAGS' software and 'rjags' package are required, and users need to pre-install them.
Enables quick calibration of radiocarbon dates under various 
  calibration curves (including user generated ones); age-depth modelling 
  as per the algorithm of Haslett and Parnell (2008) <DOI:10.1111/j.1467-9876.2008.00623.x>; Relative sea level 
  rate estimation incorporating time uncertainty in polynomial regression 
  models (Parnell and Gehrels 2015) <DOI:10.1002/9781118452547.ch32>; non-parametric phase modelling via 
  Gaussian mixtures as a means to determine the activity of a site 
  (and as an alternative to the Oxcal function SUM; currently 
  unpublished), and reverse calibration of dates from calibrated into 
  un-calibrated years (also unpublished).
Many clustering methods have been proposed, but
    most of them cannot work for longitudinal gene expression data. 
    'BClustLonG' is a package that allows us to perform clustering analysis for 
    longitudinal gene expression data. It adopts a linear-mixed effects framework 
    to model the trajectory of genes over time, while clustering is jointly 
    conducted based on the regression coefficients obtained from all genes. 
    To account for the correlations among genes and alleviate the 
    high dimensionality challenges, factor analysis models are adopted
    for the regression coefficients. The Dirichlet process prior distribution 
    is utilized for the means of the regression coefficients to induce clustering.
    This package allows users to specify which variables to use for clustering 
    (intercepts or slopes or both) and whether a factor analysis model is desired.
    More details about this method can be found in Jiehuan Sun, et al. (2017) <doi:10.1002/sim.7374>.
Various layers of B.C., including administrative boundaries,
    natural resource management boundaries, census boundaries etc. All
    layers are available in BC Albers
    (<https://spatialreference.org/ref/epsg/3005/>) equal-area
    projection, which is the B.C. government standard. The layers are
    sourced from the British Columbia and Canadian government under open
    licenses, including B.C. Data Catalogue (<https://data.gov.bc.ca>),
    the Government of Canada Open Data Portal
    (<https://open.canada.ca/en/using-open-data>), and Statistics Canada
    (<https://www.statcan.gc.ca/en/reference/licence>).
Inference on the marginal model of the mixed effect model with 
    the Box-Cox transformation and on the model median differences between
    treatment groups for longitudinal randomized clinical trials. These 
    statistical methods are proposed by Maruo et al. (2017) 
    <doi:10.1002/sim.7279>.
The Behavioral Change Point Analysis (BCPA) is a method of
    identifying hidden shifts in the underlying parameters of a time series,
    developed specifically to be applied to animal movement data which is
    irregularly sampled.  The method is based on: E. Gurarie, R. Andrews and 
    K. Laidre A novel method for identifying behavioural changes in animal 
    movement data (2009) Ecology Letters 12:5 395-408. A development version is 
    on <https://github.com/EliGurarie/bcpa>. NOTE: the BCPA method may be useful 
    for any univariate, irregularly sampled Gaussian time-series, but animal 
    movement analysts are encouraged to apply correlated velocity change point 
    analysis as implemented in the smoove package, as of this writing on GitHub 
    at <https://github.com/EliGurarie/smoove>. An example of a univariate analysis
    is provided in the UnivariateBCPA vignette.
Provides functions to utilize a command line utility that does bulk inserts and exports from SQL Server databases.
Functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals.
             Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>. 
             Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.
Implements a wide variety of one- and two-parameter Bayesian CRM
    designs. The program can run interactively, allowing the user to enter outcomes
    after each cohort has been recruited, or via simulation to assess operating
    characteristics. See Sweeting et al. (2013): <doi:10.18637/jss.v054.i13>.
The bias-corrected estimation methods for the receiver operating characteristics
  ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR)
  assumption.
Bindings to the 'blowfish' password hashing algorithm derived from
    the 'OpenBSD' implementation.
Gene expression profiles are commonly utilized to infer disease
             subtypes and many clustering methods can be adopted for this task.
             However, existing clustering methods may not perform well when
             genes are highly correlated and many uninformative genes are included
             for clustering. To deal with these challenges, we develop a novel
             clustering method in the Bayesian setting. This method, called BCSub,
             adopts an innovative semiparametric Bayesian factor analysis model
             to reduce the dimension of the data to a few factor scores for
             clustering. Specifically, the factor scores are assumed to follow
             the Dirichlet process mixture model in order to induce clustering.
An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, segmentation/change-point detection and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. 'Bayesian Context Trees: Modelling and exact inference for discrete time series.' Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: <arXiv:2007.14900> [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' <arXiv:2203.04341> [stat.ME], March 2022].
Methods for choosing the rank of an SVD (singular value decomposition) 
    approximation via cross validation. The package provides both Gabriel-style 
    "block" holdouts and Wold-style "speckled" holdouts. It also includes an 
    implementation of the SVDImpute algorithm. For more information about
    Bi-cross-validation, see Owen & Perry's 2009 AoAS article
    (at <arXiv:0908.2062>) and Perry's 2009 PhD thesis
    (at <arXiv:0909.3052>).
Algorithms developed for binned data analysis,
  gene expression data analysis and
  measurement error models for ordinal data analysis.
The Bloom Detecting Algorithm enables the detection of blooms within a time series of species abundance and extracts 22 phenological variables. For details, see Karasiewicz et al. (2022) <doi:10.3390/jmse10020174>.
It brings together several aspects of biodiversity
    data-cleaning in one place. 'bdc' is organized in thematic modules
    related to different biodiversity dimensions, including 1) Merge
    datasets: standardization and integration of different datasets; 2)
    Pre-filter: flagging and removal of invalid or non-interpretable
    information, followed by data amendments; 3) Taxonomy: cleaning,
    parsing, and harmonization of scientific names from several taxonomic
    groups against taxonomic databases locally stored through the
    application of exact and partial matching algorithms; 4) Space:
    flagging of erroneous, suspect, and low-precision geographic
    coordinates; and 5) Time: flagging and, whenever possible, correction
    of inconsistent collection date. In addition, it contains
    features to visualize, document, and report data quality – which is
    essential for making data quality assessment transparent and
    reproducible. The reference for the methodology is Bruno et al. (2022)
    <doi:10.1111/2041-210X.13868>.
The 'shiny' application 'bdDwC' makes biodiversity data field names Darwin Core compatible.
A collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.
For a balanced design of experiments, this package calculates the sample size required to detect a certain standardized effect size, under a significance level. This package also provides three graphs; detectable standardized effect size vs power, sample size vs detectable standardized effect size, and sample size vs power, which show the mutual relationship between the sample size, power and the detectable standardized effect size. The detailed procedure is described in R. V. Lenth (2006-9) <https://homepage.divms.uiowa.edu/~rlenth/Power/>, Y. B. Lim (1998), M. A. Kastenbaum, D. G. Hoel and K. O. Bowman (1970) <doi:10.2307/2334851>, and Douglas C. Montgomery (2013, ISBN: 0849323312).
Statistical tools for Bayesian structure learning in undirected graphical models for continuous, ordinal/discrete/count, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models' literature, including Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, and Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>.
Interface to Local Data Bank ('Bank Danych Lokalnych' - 'bdl') API 
    <https://api.stat.gov.pl/Home/BdlApi?lang=en> with set of useful tools like 
    quick plotting and map generating using data from bank.
The main function generateDataset() processes a user-supplied .R file that 
  contains metadata parameters in order to generate actual data. The metadata parameters 
  have to be structured in the form of metadata objects, the format of which is 
  outlined in the package vignette. This approach allows to generate artificial data 
  in a transparent and reproducible manner.
Analyze differences among time series curves with p-value
        adjustment for multiple comparisons introduced in Oleson et al
        (2015) <DOI:10.1177/0962280215607411>.
Tools and workflow to choose design parameters in Bayesian adaptive single-arm phase II trial designs with binary endpoint (response, success) with possible stopping for efficacy and futility at interim analyses. Also contains routines to determine and visualize operating characteristics. See Kopp-Schneider et al. (2018) <doi:10.1002/bimj.201700209>.
Provide a tool to easily build customized data flows to pre-process large volumes 
    of information from different sources. To this end, 'bdpar' allows to (i) easily use and 
    create new functionalities and (ii) develop new data source extractors according to the 
    user needs. Additionally, the package provides by default a predefined data flow 
    to extract and pre-process the most relevant information (tokens, dates, ... ) from some textual 
    sources (SMS, Email, YouTube comments).
Computation of asymptotic confidence intervals for negative and positive predictive values in binary diagnostic tests in case-control studies. Experimental design for hypothesis tests on predictive values.
Fits a discharge rating curve based on the power-law and the generalized power-law from data on paired stage and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2020) <arXiv:2010.04769>.
Implements Bayesian inference to detect signal from blinded 
    clinical trial when total number of adverse events of special 
    concerns and total risk exposures from all patients are available in the study.  
    For more details see the article by Mukhopadhyay et. al. (2018) 
    titled 'Bayesian Detection of Potential Risk Using Inference on Blinded Safety Data', 
    in Pharmaceutical Statistics (to appear).
Provides a continuous date scale, omitting weekends and holidays.
This is a special case of sparse matrices, used by coxme.
Performs block diagonal covariance matrix detection using singular vectors (BD-SVD), which can be extended to hierarchical variable clustering (HC-SVD). The methods are described in Bauer (202Xa) <arXiv:2211.16155> and Bauer (202Xb) <arXiv:2308.06820>.
Provides a set of functions to create basic visualizations to quickly
    preview different aspects of biodiversity information such as inventory 
    completeness, extent of coverage (taxonomic, temporal and geographic), gaps
    and biases. Barve & Otegui (2016) <DOI:10.1093/bioinformatics/btw333>.
A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.
Analyze bioequivalence study data with industrial strength. Sample size could be determined for various crossover designs, such as 2x2 design, 2x4 design, 4x4 design, Balaam design, Two-sequence dual design, and William design.
             Reference: Chow SC, Liu JP. Design and Analysis of Bioavailability and Bioequivalence Studies. 3rd ed. (2009, ISBN:978-1-58488-668-6).
Provides an R interface for the Bureau of Economic Analysis (BEA) 
		API (see <http://www.bea.gov/API/bea_web_service_api_user_guide.htm> for 
		more information) that serves two core purposes - 
    1. To Extract/Transform/Load data [beaGet()] from the BEA API as R-friendly 
		formats in the user's work space [transformation done by default in beaGet() 
		can be modified using optional parameters; see, too, bea2List(), bea2Tab()].
		2. To enable the search of descriptive meta data [beaSearch()].
		Other features of the library exist mainly as intermediate methods 
		or are in early stages of development.
		Important Note - You must have an API key to use this library.  
		Register for a key at <http://www.bea.gov/API/signup/index.cfm> .
A platform is provided for interactive analyses with a goal of totally easy to develop, deploy, interact, and explore (TEDDIE). Using this package, users can create customized analyses and make them available to end users who can perform interactive analyses and save analyses to RTF or HTML files. It allows developers to focus on R code for analysis, instead of dealing with html or shiny code.
Reproducible and automated analysis of multiplex bead assays such
    as CBA (Morgan et al. 2004; <doi: 10.1016/j.clim.2003.11.017>), LEGENDplex
    (Yu et al. 2015; <doi: 10.1084/jem.20142318>), and MACSPlex (Miltenyi
    Biotec 2014; Application note: Data acquisition and analysis without the
    MACSQuant analyzer;
    <https://www.miltenyibiotec.com/upload/assets/IM0021608.PDF>). The
    package provides functions for streamlined reading of fcs files, and
    identification of bead clusters and analyte expression. The package eases
    the calculation of standard curves and the subsequent calculation of the
    analyte concentration.
A minimalist web framework for developing application programming 
    interfaces in R that provides a flexible framework for handling common 
    HTTP-requests, errors, logging, and an ability to integrate any R code as 
    server middle-ware.
Plots univariate comparison graphs, an alternative to
        boxplot/stripchart/violin plot.
These data contain morphological image measurements for dried
    beans from Koklu and Ozkan (2020) <doi:10.1016/j.compag.2020.105507>.
It is vital to assess the heterogeneity of treatment effects
    (HTE) when making health care decisions for an individual patient or a group
    of patients. Nevertheless, it remains challenging to evaluate HTE based
    on information collected from clinical studies that are often designed and
    conducted to evaluate the efficacy of a treatment for the overall population.
    The Bayesian framework offers a principled and flexible approach to estimate
    and compare treatment effects across subgroups of patients defined by their
    characteristics. This package allows users to explore a wide range of Bayesian
    HTE analysis models, and produce posterior inferences about HTE. See Wang et al.
    (2018) <DOI:10.18637/jss.v085.i07> for further details.
Stock, Options and Futures Trading Strategies for Traders and Investors with Bearish Outlook. The indicators, strategies, calculations, functions and all other discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Juan A. Serur, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 385-453)", 2019, ISBN: 9781119593577).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).
Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2017) <arXiv:1709.06111> for a detailed presentation of the method.
'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 a command-line tool.
    This package provides a way to call 'BEAST2' 
    from an 'R' function call.
Provides JAR to perform Markov chain Monte Carlo (MCMC) inference using
    the popular Bayesian Evolutionary Analysis by Sampling Trees 'BEAST' software library
    of Suchard et al (2018) <doi:10.1093/ve/vey016>. 'BEAST' supports auto-tuning
    Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte Carlo
    sampling for a large variety of composable standard and phylogenetic statistical
    models using high performance computing.  By placing the 'BEAST' JAR in this package,
    we offer an efficient distribution system for 'BEAST' use by other R packages using
    CRAN.
'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.
  'BEAUti 2' (which is part of 'BEAST2') is a GUI tool 
  that allows users to specify the many possible setups
  and generates the XML file 'BEAST2' needs to run.
  This package provides a way to create 'BEAST2' input
  files without active user input, but using
  R function calls instead.
An interface for the 'Neo4j' database providing
    mapping between different identifiers of biological entities.
    This Biological Entity Dictionary (BED)
    has been developed to address three main challenges.
    The first one is related to the completeness of identifier mappings.
    Indeed, direct mapping information provided by the different systems
    are not always complete and can be enriched by mappings provided by other
    resources.
    More interestingly, direct mappings not identified by any of these
    resources can be indirectly inferred by using mappings to a third reference.
    For example, many human Ensembl gene ID are not directly mapped to any
    Entrez gene ID but such mappings can be inferred using respective mappings
    to HGNC ID. The second challenge is related to the mapping of deprecated
    identifiers. Indeed, entity identifiers can change from one resource
    release to another. The identifier history is provided by some resources,
    such as Ensembl or the NCBI, but it is generally not used by mapping tools.
    The third challenge is related to the automation of the mapping process
    according to the relationships between the biological entities of interest.
    Indeed, mapping between gene and protein ID scopes should not be done
    the same way than between two scopes regarding gene ID.
    Also, converting identifiers from different organisms should be possible
    using gene orthologs information.
    The method has been published by
    Godard and van Eyll (2018) <doi:10.12688/f1000research.13925.3>.
Provides functions that allow users to quantify the relative 
	contributions of geographic and ecological distances to empirical patterns of genetic 
	differentiation on a landscape.  Specifically, we use a custom Markov chain 
	Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the 
	inference model, as well as functions for performing MCMC diagnosis and assessing 
	model adequacy.
A matrix-like data structure that allows for efficient,
    convenient, and scalable subsetting of binary genotype/phenotype files
    generated by PLINK (<https://www.cog-genomics.org/plink2>), the whole
    genome association analysis toolset, without loading the entire file into
    memory.
Genomic regions processing using open-source command line tools such as 'BEDTools', 'BEDOPS' and 'Tabix'. 
  These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging.
  bedr API enhances access to these tools as well as offers additional utilities for genomic regions processing.
Flags and checks occurrence data that are in Darwin Core
    format.  The package includes generic functions and data as well as
    some that are specific to bees. This package is meant to build upon
    and be complimentary to other excellent occurrence cleaning packages,
    including 'bdc' and 'CoordinateCleaner'.  This package uses datasets
    from several sources and particularly from the Discover Life Website,
    created by Ascher and Pickering (2020).  For further information,
    please see the original publication and package website.  Publication
    - Dorey et al. (2023) <doi:10.1101/2023.06.30.547152> and package
    website - Dorey et al. (2023) <https://github.com/jbdorey/BeeBDC>.
Tools to calibrate, validate, and make predictions with the
    General Unified Threshold model of Survival adapted for Bee species. The
    model is presented in the publication from Baas, J., Goussen, B., Miles, M.,
    Preuss, T.G., Roessing, I. (2022) <doi:10.1002/etc.5423> and is based on the GUTS framework
    Jager, T., Albert, C., Preuss, T.G. and Ashauer, R. (2011) <doi:10.1021/es103092a>.
    The authors are grateful to Bayer A.G. for its financial support.
The main function of this package is beep(), with the purpose to
    make it easy to play notification sounds on whatever platform you are on.
    It is intended to be useful, for example, if you are running a long analysis
    in the background and want to know when it is ready.
The bee swarm plot is a one-dimensional scatter plot like
    "stripchart", but with closely-packed, non-overlapping points.
Facilitates many of the analyses performed in studies of
    behavioral economic demand. The package supports commonly-used options for
		modeling operant demand including (1) data screening proposed by Stein,
		Koffarnus, Snider, Quisenberry, & Bickel (2015; <doi:10.1037/pha0000020>),
		(2) fitting models of demand such as linear (Hursh, Raslear, Bauman,
		& Black, 1989, <doi:10.1007/978-94-009-2470-3_22>), exponential	(Hursh & Silberberg, 2008,
		<doi:10.1037/0033-295X.115.1.186>) and modified exponential (Koffarnus,
		Franck, Stein, & Bickel, 2015, <doi:10.1037/pha0000045>), and (3) calculating
		numerous measures	relevant to applied behavioral economists (Intensity,
		Pmax, Omax). Also	supports plotting and comparing data.
Facilitates some of the analyses performed in studies of
    behavioral economic discounting. The package supports scoring of the 27-Item Monetary Choice
    Questionnaire (see Kaplan et al., 2016; <doi:10.1007/s40614-016-0070-9>) and scoring of the 
    minute discounting task (see Koffarnus & Bickel, 2014; <doi:10.1037/a0035973>) using the 
    Qualtrics 5-trial discounting template (see the Qualtrics Minute Discounting User Guide; 
    <doi:10.13140/RG.2.2.26495.79527>), which is also available as a .qsf file in this package.
This is a sub national population projection model for calculating
    population development. The model uses a cohort component method.
    Further reading: Stanley K. Smith: A Practitioner's Guide to State and 
    Local Population Projections. 2013.  <doi:10.1007/978-94-007-7551-0>.
Contains specialised analyses and
    visualisation tools for behavior change science.
    These facilitate conducting determinant studies
    (for example, using confidence interval-based
    estimation of relevance, CIBER, or CIBERlite
    plots, see Crutzen, Noijen & Peters (2017)
    <doi:10/ghtfz9>),
    systematically developing, reporting,
    and analysing interventions (for example, using
    Acyclic Behavior Change Diagrams), and reporting
    about intervention effectiveness (for example, using
    the Numbers Needed for Change, see Gruijters & Peters
    (2017) <doi:10/jzkt>), and computing the
    required sample size (using the Meaningful Change
    Definition, see Gruijters & Peters (2020)
    <doi:10/ghpnx8>).
    This package is especially useful for
    researchers in the field of behavior change or
    health psychology and to behavior change
    professionals such as intervention developers and
    prevention workers.
Implements an S3 class based on 'data.table' to store and process efficiently ethomics (high-throughput behavioural) data.
Methods and tools for estimating, simulating and forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner) based on the fast Berndt–Hall–Hall–Hausman (BHHH) algorithm described in Hafner and Herwartz (2008) <doi:10.1007/s00184-007-0130-y>.
Tools for downloading historical financial data from the www.belex.rs.
Calculates the Boltzmann entropy of a landscape gradient.
    This package uses the analytical method created by Gao, P., Zhang, H.
    and Li, Z., 2018 (<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019
    (<doi:10.1007/s10980-019-00854-3>). It also extend the original ideas by
    allowing calculations on data with missing values.
A likelihood method is implemented to present evidence for evaluating bioequivalence (BE). The functions use bioequivalence data [area under the blood concentration-time curve (AUC) and peak concentration (Cmax)] from various crossover designs commonly used in BE studies including a fully replicated, a partially replicated design, and a conventional 2x2 crossover design. They will calculate the profile likelihoods for the mean difference, total standard deviation ratio, and within subject standard deviation ratio for a test and a reference drug. A plot of a standardized profile likelihood can be generated along with the maximum likelihood estimate and likelihood intervals, which present evidence for bioequivalence. See Liping Du and Leena Choi (2015) <doi:10.1002/pst.1661>.
Bell regression models for count data with overdispersion. The implemented models account for ordinary and zero-inflated regression models under both frequentist and Bayesian approaches. Theoretical details regarding the models implemented in the package can be found in Castellares et al. (2018) <doi:10.1016/j.apm.2017.12.014> and Lemonte et al. (2020) <doi:10.1080/02664763.2019.1636940>.
Tools to accurately benchmark and analyze execution times for
    R expressions.
Full implementation of the 28 distributions introduced as benchmarks for nonparametric density estimation by Berlinet and Devroye (1994) <https://hal.science/hal-03659919>. Includes densities, cdfs, quantile functions and generators for samples as well as additional information on features of the densities. Also contains the 4 histogram densities used in Rozenholc/Mildenberger/Gather (2010) <doi:10.1016/j.csda.2010.04.021>.
Methods for frontier
    analysis, Data Envelopment Analysis (DEA), under different
    technology assumptions (fdh, vrs, drs, crs, irs, add/frh, and fdh+),
    and using different efficiency measures (input based, output based,
    hyperbolic graph, additive, super, and directional efficiency). Peers
    and slacks are available, partial price information can be included,
    and optimal cost, revenue and profit can be calculated. Evaluation of
    mergers is also supported.  Methods for graphing the technology sets
    are also included. There is also support for comparative methods based
    on Stochastic Frontier Analyses (SFA) and for convex nonparametric 
    least squares of convex functions (STONED). In general, the methods 
    can be used to solve not only standard models, but also many other 
    model variants. It complements the book, Bogetoft and Otto,
    Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of
    course also be used as a stand-alone package.
Benchmark your CPU and compare against other CPUs.  Also
    provides functions for obtaining system specifications, such as RAM,
    CPU type, and R version.
Crowd sourced benchmarks from running the
    'benchmarkme' package.
Provides infrastructure to accurately measure and compare
    the execution time of R expressions.
Provides a set of models to estimate nonlinear longitudinal data using Bayesian estimation methods. These models include the: 1) Bayesian Piecewise Random Effects Model (Bayes_PREM()) which estimates a piecewise random effects (mixture) model for a given number of latent classes and a latent number of possible changepoints in each class, and can incorporate class and outcome predictive covariates (see Lamm (2022) <https://hdl.handle.net/11299/252533> and Lock et al., (2018) <doi:10.1007/s11336-017-9594-5>), 2) Bayesian Crossed Random Effects Model (Bayes_CREM()) which estimates a linear, quadratic, exponential, or piecewise crossed random effects models where individuals are changing groups over time (e.g., students and schools; see Rohloff et al., (2024) <doi:10.1111/bmsp.12334>), and 3) Bayesian Bivariate Piecewise Random Effects Model (Bayes_BPREM()) which estimates a bivariate piecewise random effects model to jointly model two related outcomes (e.g., reading and math achievement; see Peralta et al., (2022) <doi:10.1037/met0000358>).
R client for Bender Hyperparameters optimizer : <https://bender.dreem.com>
    The R client allows you to communicate with the Bender API and therefore submit some new trials within your R script itself.
Perform the Benford's Analysis to a data set in order to evaluate if it contains human fabricated data. For more details on the method see Moreau, 2021, Model Assist. Statist. Appl., 16 (2021) 73–79. <doi:10.3233/MAS-210517>.
Provides tools that make it easier to validate data using Benford's Law.
Several specialized statistical tests and support functions 
			for determining if numerical data could conform to Benford's law.
Analysis tool for evaluating benthic multimetric indices (BENMMIs). 
    It generates reproducible reports on the analysis of benthic data, e.g., 
    validation and correction of species names, sample pooling, automatic
    conversion of genus to species names, outlier detection, benthic 
    indicator calculation, optimization of single and 
    multimetric indicators against a pressure gradient, and spatial aggregation
    of benthic indicators. One of its use cases was the development of a common 
    benthic indicator for <https://www.ospar.org> (publication accepted by 
    Ecological Indicators). See Van Loon et al. (2018) <doi:10.1016/j.ecolind.2017.09.029> for details.
Bayesian model and associated tools for generating estimates of 
    total naloxone kit numbers distributed and used from naloxone kit orders 
    data. Provides functions for generating simulated data of naloxone kit use
    and functions for generating samples from the posterior.
Included are two main interfaces, bentcable.ar() and
	bentcable.dev.plot(), for fitting and diagnosing bent-cable
	regressions for autoregressive time-series data (Chiu and
	Lockhart 2010, <doi:10.1002/cjs.10070>) or independent data (time
	series or otherwise - Chiu, Lockhart and Routledge 2006,
	<doi:10.1198/016214505000001177>). Some components in the package
	can also be used as stand-alone functions. The bent cable
	(linear-quadratic-linear) generalizes the broken stick
	(linear-linear), which is also handled by this package. Version
	0.2 corrected a glitch in the computation of confidence intervals
	for the CTP. References that were updated from Versions 0.2.1 and
	0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improved
	robustness of the error-message producing mechanism. Version 0.3.1
	improves the NAMESPACE file of the package. It is the author's
	intention to distribute any future updates via GitHub.
Preprocessing tools and biodiversity measures 
    (species abundance, species richness, population heterogeneity and 
    sensitivity) for analysing marine benthic data. See Van Loon et al.
    (2015) <doi:10.1016/j.seares.2015.05.002> for an application of 
    these tools.
Using a Bayesian estimation procedure, this package fits linear quantile regression models such as linear quantile models, linear quantile mixed models, quantile regression joint models for time-to-event and longitudinal data. The estimation procedure is based on the asymmetric Laplace distribution and the 'JAGS' software is used to get posterior samples (Yang, Luo, DeSantis (2019) <doi:10.1177/0962280218784757>).
Bayesian analysis for exponential random graph models using
    advanced computational algorithms. More information can be found at:
    <https://acaimo.github.io/Bergm/>.
Bayesian analysis for stochastic extensions of non-linear
    dynamic systems using advanced computational algorithms. Described in Bouranis, L., 
    Demiris, N., Kalogeropoulos, K., and Ntzoufras, I. (2022) <arXiv:2211.15229>.
Draw horizontal histograms, color scattered points by 3rd dimension,
    enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, 
    use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, 
    fit multiple functions.
An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.
Computations for Bessel function for complex, real and partly
  'mpfr' (arbitrary precision) numbers; notably interfacing TOMS 644;
  approximations for large arguments, experiments, etc.
Best subset glm using information criteria or cross-validation,
        carried by using 'leaps' algorithm (Furnival and Wilson, 1974) <doi:10.2307/1267601>
        or complete enumeration (Morgan and Tatar, 1972) <doi:10.1080/00401706.1972.10488918>.
        Implements PCR and PLS using AIC/BIC.
        Implements one-standard deviation rule for use with the 'caret' package.
Creates plots showing scored HR experiments and plots of distribution of
    means of ranks of HR score from bootstrapping. Authors (2019) <doi:10.5281/zenodo.3374507>.
An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.
Estimate a suite of normalizing transformations, including 
    a new adaptation of a technique based on ranks which can guarantee 
    normally distributed transformed data if there are no ties: ordered 
    quantile normalization (ORQ). ORQ normalization combines a rank-mapping
    approach with a shifted logit approximation that allows
    the transformation to work on data outside the original domain. It is 
    also able to handle new data within the original domain via linear 
    interpolation. The package is built to estimate the best normalizing 
    transformation for a vector consistently and accurately. It implements 
    the Box-Cox transformation, the Yeo-Johnson transformation, three types 
    of Lambert WxF transformations, and the ordered quantile normalization 
    transformation. It estimates the normalization efficacy of other
    commonly used transformations, and it allows users to specify 
    custom transformations or normalization statistics. Finally, functionality
    can be integrated into a machine learning workflow via recipes.
Decision tree algorithm with a major feature added. Allows for users to define an ordering on the partitioning process.
    Resulting in Branch-Exclusive Splits Trees (BEST). Cedric Beaulac and Jeffrey S. Rosentahl (2019) <arXiv:1804.10168>.
The bestridge package is designed to provide a one-stand service for users to successfully carry out best ridge regression in various complex situations via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>. This package allows users to perform the regression, classification, count regression and censored regression for (ultra) high dimensional data, and it also supports advanced usages like group variable selection and nuisance variable selection.
Provides a practical tool for estimating the burden of common communicable diseases in settlements of displaced populations. An online version of the tool can be found at <http://who-refugee-bod.ecdf.ed.ac.uk/shiny/app/>. Estimates of burden of disease aim to synthesize data about cause-specific morbidity and mortality through a systematic approach that enables evidence-based decisions and comparisons across settings. The focus of this tool is on four acute communicable diseases and syndromes, including Acute respiratory infections, Acute diarrheal diseases, Acute jaundice syndrome and Acute febrile illnesses.
Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, <DOI:10.1080/01621459.2018.1537921>, Kai Zhang, Zhigen Zhai, and Wen Zhou. (2021). BEAUTY Powered BEAST, <arXiv:2103.00674>  and Wan Zhang, Zhigen Zhao, Michael Baiocchi, Yao Li, Kai Zhang. SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables, Techinical report, 2023.
Provides a class of Bayesian beta regression models for the analysis of continuous data with support restricted to an unknown finite support. The response variable is modeled using a four-parameter beta distribution with the mean or mode parameter depending linearly on covariates through a link function. When the response support is known to be (0,1), the above class of models reduce to traditional (0,1) supported beta regression models. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou and Huang (2022) <doi:10.1016/j.csda.2021.107345>.
Three games: proton, frequon and regression. Each one is a console-based data-crunching game for younger and older data scientists.
  Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server.
  In proton you have to solve four data-based puzzles to find the login and password.
  There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools.
  Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try.
  In frequon you will help to perform statistical cryptanalytic attack on a corpus of ciphered messages.
  This time seven sub-tasks are pushing the bar much higher. Do you accept the challenge?
  In regression you will test your modeling skills in a series of eight sub-tasks.
  Try only if ANOVA is your close friend.
  It's a part of Beta and Bit project.
  You will find more about the Beta and Bit project at <https://github.com/BetaAndBit/Charts>.
Fit beta calibration models and obtain calibrated probabilities from
    them.
A family of novel beta mixture models (BMMs) has been developed by Majumdar et al. (2022) <arXiv:2211.01938v1> to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility.
Generates confidence intervals for standardized regression coefficients
    using delta method standard errors for models fitted by lm()
    as described in Yuan and Chan (2011) <doi:10.1007/s11336-011-9224-6>
    and Jones and Waller (2015) <doi:10.1007/s11336-013-9380-y>.
    The package can also be used to generate confidence intervals for
    differences of standardized regression coefficients 
    and as a general approach to performing the delta method.
    A description of the package and code examples
    are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.
Package providing a number of functions for working with Two- and 
    Four-parameter Beta and closely related distributions (i.e., the Gamma-
    Binomial-, and Beta-Binomial distributions).
        Includes, among other things: 
    - d/p/q/r functions for Four-Parameter Beta distributions and Generalized
    "Binomial" (continuous) distributions, and d/p/r- functions for Beta-
    Binomial distributions.
    - d/p/q/r functions for Two- and Four-Parameter Beta distributions
    parameterized in terms of their means and variances rather than their
    shape-parameters.
    - Moment generating functions for Binomial distributions, Beta-Binomial 
    distributions, and observed value distributions.
    - Functions for estimating classification accuracy and consistency, 
    making use of the Classical Test-Theory based 'Livingston and Lewis' (L&L) 
    and 'Hanson and Brennan' approaches.
      A shiny app is available, providing a GUI for the L&L approach when used 
    for binary classifications. For url to the app, see documentation for the 
    LL.CA() function.
    Livingston and Lewis (1995) <doi:10.1111/j.1745-3984.1995.tb00462.x>.
    Lord (1965) <doi:10.1007/BF02289490>.
    Hanson (1991) <https://files.eric.ed.gov/fulltext/ED344945.pdf>.
Generates Monte Carlo confidence intervals
    for standardized regression coefficients (beta) and other effect sizes,
    including multiple correlation, semipartial correlations,
    improvement in R-squared, squared partial correlations,
    and differences in standardized regression coefficients,
    for models fitted by lm().
    'betaMC' combines ideas from Monte Carlo confidence intervals for the indirect effect
    (Pesigan and Cheung, 2023 <doi:10.3758/s13428-023-02114-4>)
    and the sampling covariance matrix of regression coefficients
    (Dudgeon, 2017 <doi:10.1007/s11336-017-9563-z>)
    to generate confidence intervals effect sizes in regression.
Generates nonparametric bootstrap confidence intervals
    (Efron & Tibshirani, 1993: <doi:10.1201/9780429246593>)
    for standardized regression coefficients (beta) and other effect sizes,
    including multiple correlation, semipartial correlations,
    improvement in R-squared, squared partial correlations,
    and differences in standardized regression coefficients,
    for models fitted by lm().
Functions to compute pair-wise dissimilarities (distance matrices) and multiple-site dissimilarities, separating the turnover and nestedness-resultant components of taxonomic (incidence and abundance based), functional and phylogenetic beta diversity.
Power calculations are a critical component of any research study to determine the 
	minimum sample size necessary to detect differences between multiple groups. 
	Researchers often work with data taking the form of proportions that can be modeled with 
	a beta distribution. Here we present an R package, 'BetaPASS', that perform power and 
	sample size calculations for data following a beta distribution with comparative 
	nonparametric output. This package allows flexibility with multiple options for link 
	functions to fit the data and graphing functionality for visual comparisons.
Permutational method to incorporate taxonomic uncertainty  and some functions to assess its effects on parameters of some widely used multivariate methods in ecology, as explained in Cayuela et al. (2011) <doi:10.1111/j.1600-0587.2009.05899.x>.
Beta regression for modeling beta-distributed dependent variables, e.g., rates and proportions.
  In addition to maximum likelihood regression (for both mean and precision of a beta-distributed
  response), bias-corrected and bias-reduced estimation as well as finite mixture models and
  recursive partitioning for beta regressions are provided.
Generates robust confidence intervals for standardized regression coefficients
    using heteroskedasticity-consistent standard errors for models fitted by lm()
    as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>.
    The package can also be used to generate confidence intervals for R-squared,
    adjusted R-squared, and differences of standardized regression coefficients.
    A description of the package and code examples
    are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.
Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions).
The sample size according to the Bethel's procedure.
It provides access to and information about the most important
    Brazilian economic time series - from the Getulio Vargas Foundation <http://portal.fgv.br/en>,
    the Central Bank of Brazil <http://www.bcb.gov.br> and the Brazilian Institute of Geography
    and Statistics <http://www.ibge.gov.br>. It also presents tools for managing, analysing (e.g.
    generating dynamic reports with a complete analysis of a series) and exporting
    these time series.
Implements likelihood inference for early epidemic analysis. BETS is short for the four key epidemiological events being modeled: Begin of exposure, End of exposure, time of Transmission, and time of Symptom onset. The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020) <arXiv:2004.07743>.
Provides squared semi partial correlations, tolerance, Mahalanobis, Likelihood Ratio Chi Square, and Pseudo R Square. Aberson, C. L. (2022) <doi:10.31234/osf.io/s2yqn>.
A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - 'A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.
Provides functions for summarizing and plotting the output of the command-line tool 'BeXY' (<https://bitbucket.org/wegmannlab/bexy>), a tool that performs Bayesian inference of sex chromosome karyotypes and sex-linked scaffolds from low-depth sequencing data.
Allows to compare the goodness of fit of Benford's and Blondeau Da Silva's digit distributions in a dataset. It is used to check whether the data distribution is consistent with theoretical distributions highlighted by Blondeau Da Silva or not (through the dat.distr() function): this ideal theoretical distribution must be at least approximately followed by the data for the use of Blondeau Da Silva's model to be well-founded. It also enables to plot histograms of digit distributions, both observed in the dataset and given by the two theoretical approaches (with the digit.ditr() function). Finally, it proposes to quantify the goodness of fit via Pearson's chi-squared test (with the chi2() function).
Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2023) <arXiv:2303.11561>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.
The bezier package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.
Decomposition of time series into
    trend, seasonal, and remainder components with methods for detecting and
    characterizing abrupt changes within the trend and seasonal components. 'BFAST'
    can be used to analyze different types of satellite image time series and can
    be applied to other disciplines dealing with seasonal or non-seasonal time
    series, such as hydrology, climatology, and econometrics. The algorithm can be
    extended to label detected changes with information on the parameters of the
    fitted piecewise linear models. 'BFAST' monitoring functionality is described
    in Verbesselt et al. (2010) <doi:10.1016/j.rse.2009.08.014>. 'BFAST monitor'
    provides functionality to detect disturbance in near real-time based on 'BFAST'-
    type models, and is described in Verbesselt et al. (2012) <doi:10.1016/j.rse.2012.02.022>.
    'BFAST Lite' approach is a flexible approach that handles missing data
    without interpolation, and will be described in an upcoming paper.
    Furthermore, different models can now be used to fit the
    time series data and detect structural changes (breaks).
Bayes factors represent the ratio of probabilities assigned to data by competing scientific hypotheses. However, one drawback of Bayes factors is their dependence on prior specifications that define null and alternative hypotheses. Additionally, there are challenges in their computation. To address these issues, we define Bayes factor functions (BFFs) directly from common test statistics. BFFs express Bayes factors as a function of the prior densities used to define the alternative hypotheses. These prior densities are centered on standardized effects, which serve as indices for the BFF. Therefore, BFFs offer a summary of evidence in favor of alternative hypotheses that correspond to a range of scientifically interesting effect sizes. Such summaries remove the need for arbitrary thresholds to determine "statistical significance." BFFs are available in closed form and can be easily computed from z, t, chi-squared, and F statistics. They depend on hyperparameters "r" and "tau^2", which determine the shape and scale of the prior distributions defining the alternative hypotheses. For replicated designs, the "r" parameter in each function can be adjusted to be greater than 1. Plots of BFFs versus effect size provide informative summaries of hypothesis tests that can be easily aggregated across studies.
The Bayesian Federated Inference ('BFI') method combines inference results 
    obtained from local data sets in the separate centers. In this version of the package, 
    the 'BFI' methodology is programmed for linear and logistic regression models; 
    see Jonker, Pazira and Coolen (2024) <doi:10.1002/sim.10072>.
Implements the Bayesian paradigm for fractional
 polynomial models under the assumption of normally distributed error terms, see
 Sabanes Bove, D. and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>.
Implementation of default Bayes factors
    for testing statistical hypotheses under various statistical models. The package is
    intended for applied quantitative researchers in the
    social and behavioral sciences, medical research,
    and related fields. The Bayes factor tests can be
    executed for statistical models such as 
    univariate and multivariate normal linear models,
    correlation analysis, generalized linear models, special cases of 
    linear mixed models, survival models, relational
    event models. Parameters that can be tested are
    location parameters (e.g., group means, regression coefficients),
    variances (e.g., group variances), and measures of 
    association (e.g,. polychoric/polyserial/biserial/tetrachoric/product
    moments correlations), among others.
    The statistical underpinnings are
    described in 
    Mulder and Xin (2019) <DOI:10.1080/00273171.2021.1904809>,
    Mulder and Gelissen (2019) <DOI:10.1080/02664763.2021.1992360>,
    Mulder (2016) <DOI:10.1016/j.jmp.2014.09.004>,
    Mulder and Fox (2019) <DOI:10.1214/18-BA1115>,
    Mulder and Fox (2013) <DOI:10.1007/s11222-011-9295-3>,
    Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) <DOI:10.1037/met0000116>,
    Hoijtink, Mulder, van Lissa, and Gu, (2018) <DOI:10.31234/osf.io/v3shc>,
    Gu, Mulder, and Hoijtink, (2018) <DOI:10.1111/bmsp.12110>,
    Hoijtink, Gu, and Mulder, (2018) <DOI:10.1111/bmsp.12145>, and
    Hoijtink, Gu, Mulder, and Rosseel, (2018) <DOI:10.1037/met0000187>. When using the
    packages, please refer to Mulder et al. (2021) <DOI:10.18637/jss.v100.i18>.
Search and download data from the Swiss Federal Statistical Office (BFS) APIs <https://www.bfs.admin.ch/>.
How to fit a straight line through a set of points with errors in
  both coordinates? The 'bfsl' package implements the York regression 
  (York, 2004 <doi:10.1119/1.1632486>). It provides unbiased estimates of the 
  intercept, slope and standard errors for the best-fit straight line to 
  independent points with (possibly correlated) normally distributed errors in 
  both x and y. Other commonly used errors-in-variables methods, such as 
  orthogonal distance regression, geometric mean regression or Deming regression
  are special cases of the 'bfsl' solution.
At the Swiss Federal Statistical Office (SFSO), spatial maps of Switzerland are available free of charge as 'Cartographic bases for small-scale thematic mapping'. This package contains convenience functions to import ESRI (Environmental Systems Research Institute) shape files using the package 'sf' and to plot them easily and quickly without having to worry too much about the technical details.
      It contains utilities to combine multiple areas to one single polygon and to find neighbours for single regions. For any point on a map, a special locator can be used to determine to which municipality, district or canton it belongs.
Derived from the work of Kruschke (2015,
    <ISBN:9780124058880>), the present package aims to provide a framework
    for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC)
    sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer,
    2003, <https://mcmc-jags.sourceforge.io>).  The initial version
    includes several modules for conducting Bayesian equivalents of
    chi-squared tests, analysis of variance (ANOVA), multiple
    (hierarchical) regression, softmax regression, and for fitting data
    (e.g., structural equation modeling).
An umbrella package providing a phenotype/genotype data structure
    and scalable and efficient computational methods for large genomic datasets
    in combination with several other packages: 'BEDMatrix', 'LinkedMatrix',
    and 'symDMatrix'.
Density, distribution function, quantile function random generation and estimation of bimodal GEV distribution given in Otiniano et al. (2023) <doi:10.1007/s10651-023-00566-7>. This new generalization of the well-known GEV (Generalized Extreme Value) distribution is useful for modeling heterogeneous bimodal data from different areas.
Routine for fitting regression models for binary rare events with linear and nonlinear covariate effects when using the quantile function of the Generalized Extreme Value random variable.
Evaluates the probability density function, cumulative distribution function, quantile function, random numbers, survival function, hazard rate function, and maximum likelihood estimates for the following distributions: Bell exponential, Bell extended exponential, Bell Weibull, Bell extended Weibull, Bell-Fisk, Bell-Lomax, Bell Burr-XII, Bell Burr-X, complementary Bell exponential, complementary Bell extended exponential, complementary Bell Weibull, complementary Bell extended Weibull, complementary Bell-Fisk, complementary Bell-Lomax, complementary Bell Burr-XII and complementary Bell Burr-X distribution. Related work includes:
     a) Fayomi A., Tahir M. H., Algarni A., Imran M. and Jamal F. (2022). "A new useful exponential model with applications to quality control and 
        actuarial data". Computational Intelligence and Neuroscience, 2022. <doi:10.1155/2022/2489998>.
     b) Alanzi, A. R., Imran M., Tahir M. H., Chesneau C., Jamal F. Shakoor S. and Sami, W. (2023). "Simulation analysis, 
        properties and applications on a new Burr XII model based on the Bell-X functionalities". AIMS Mathematics, 8(3): 6970-7004. <doi:10.3934/math.2023352>.
     c) Algarni A. (2022). "Group Acceptance Sampling Plan Based on New Compounded Three-Parameter Weibull Model". Axioms, 11(9): 438. <doi:10.3390/axioms11090438>.
Tools for analysing board game data. Mainly focused on providing 
    an interface for BoardGameGeek's XML API2 through R6 class system objects.
    More details about the BoardGameGeek's API can be obtained here
    <https://boardgamegeek.com/wiki/page/BGG_XML_API2>.
Application of genome prediction for a continuous variable, focused 
             on genotype by environment (GE) genomic selection models (GS). It consists a group of functions 
             that help to create regression kernels for some GE genomic models proposed by Jarquín et al. (2014) <doi:10.1007/s00122-013-2243-1>
             and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches.
             The prediction function uses an orthogonal transformation of the data and specific priors
             present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>.
Fit Bayesian Gaussian graphical models. The methods are separated into 
    two Bayesian approaches for inference: hypothesis testing and estimation. There are 
    extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, 
    and node wise predictability. These methods were recently introduced in the Gaussian 
    graphical model literature, including 
    Williams (2019) <doi:10.31234/osf.io/x8dpr>, 
    Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>,
    Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.
Provides a Metropolis-coupled Markov chain Monte Carlo sampler,
    post-processing and parameter estimation functions, and plotting utilities
    for the generalized graded unfolding model of Roberts, Donoghue, and
    Laughlin (2000) <doi:10.1177/01466216000241001>.
A collection of box-geometry model (BGM) files for the Atlantis 
    ecosystem model. Atlantis is a deterministic, biogeochemical, 
    whole-of-ecosystem model (see <http://atlantis.cmar.csiro.au/> for more information).
The BGmisc R package offers a comprehensive suite of
    functions tailored for extended behavior genetics analysis, including
    model identification, calculating relatedness, pedigree conversion,
    pedigree simulation, and more.
Two partially supervised mixture modeling methods: 
        soft-label and belief-based modeling are implemented.
        For completeness, we equipped the package also with the
        functionality of unsupervised, semi- and fully supervised
        mixture modeling.  The package can be applied also to selection
        of the best-fitting from a set of models with different
        component numbers or constraints on their structures.
        For detailed introduction see:
        Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy
        Tiuryn (2012), The R Package bgmm: Mixture Modeling with
        Uncertain Knowledge, Journal of Statistical Software 
        <doi:10.18637/jss.v047.i03>.
Bayesian variable selection methods for analyzing the structure of a Markov Random Field model for a network of binary and/or ordinal variables. Details of the implemented methods can be found in: Marsman and Haslbeck (2023) <doi:10.31234/osf.io/ukwrf>.
Computes the hazard rate estimate as described by
    Nieto-Barajas & Walker (2002), Nieto-Barajas (2003), Nieto-Barajas &
    Walker (2007) and Nieto-Barajas & Yin (2008).
Implementation of Bayesian multi-task regression models and was developed within the context of imaging genetics. The package can currently fit two models. The Bayesian group sparse multi-task regression model of Greenlaw et al. (2017)<doi:10.1093/bioinformatics/btx215> can be fit with implementation using Gibbs sampling. An extension of this model developed by Song, Ge et al. to accommodate both spatial correlation as well as correlation across brain hemispheres can also be fit using either mean-field variational Bayes or Gibbs sampling. The model can also be used more generally for multivariate (non-imaging) phenotypes with spatial correlation.
Bimodal Gumbel distribution. General functions for performing extreme value analysis.
Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 <doi:10.18637/jss.v104.i09>.
Boost provides free peer-reviewed portable C++ source 
 libraries.  A large part of Boost is provided as C++ template code
 which is resolved entirely at compile-time without linking.  This 
 package aims to provide the most useful subset of Boost libraries 
 for template use among CRAN packages. By placing these libraries in 
 this package, we offer a more efficient distribution system for CRAN 
 as replication of this code in the sources of other packages is 
 avoided. As of release 1.84.0-0, the following Boost libraries are
 included: 'accumulators' 'algorithm' 'align' 'any' 'atomic' 'beast'
 'bimap' 'bind' 'circular_buffer' 'compute' 'concept' 'config'
 'container' 'date_time' 'detail' 'dynamic_bitset' 'exception'
 'flyweight' 'foreach' 'functional' 'fusion' 'geometry' 'graph' 'heap'
 'icl' 'integer' 'interprocess' 'intrusive' 'io' 'iostreams'
 'iterator' 'lambda2' 'math' 'move' 'mp11' 'mpl' 'multiprecision'
 'numeric' 'pending' 'phoenix' 'polygon' 'preprocessor' 'process'
 'propery_tree' 'qvm' 'random' 'range' 'scope_exit' 'smart_ptr' 'sort'
 'spirit' 'tuple' 'type_traits' 'typeof' 'unordered' 'url' 'utility'
 'uuid'.
Provides an approach which is based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) and can be used for large and small samples such as individual countries. The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables.
Provides functions for Maximum Likelihood
             Estimation, Markov Chain Monte Carlo, finding confidence
             intervals.  The implementation is heavily based on the
             original Fortran source code translated to R.
Functions and data sets reproducing some examples in
             Box, Hunter and Hunter II.  Useful for statistical design
             of experiments, especially factorial experiments.
Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.
Provides functions for the evaluation of basket
    trial designs with binary endpoints. Operating characteristics of a
    basket trial design are assessed by simulating trial data according to
    scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and
    assessing decision probabilities on stratum and trial-level based on Go / No-go decision making.
    The package is build for high flexibility regarding decision rules,
    number of interim analyses, number of strata, and recruitment.
    The BHMs proposed by
    Berry et al. (2013) <doi:10.1177/1740774513497539>
    and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>,
    as well as a model that combines both approaches are implemented.
    Functions are provided to implement Bayesian decision rules as for example
    proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>.
    In addition, posterior point estimates (mean/median) and credible intervals
    for response rates and some model parameters can be calculated.
    For simulated trial data, bias and mean squared errors of posterior
    point estimates for response rates can be provided.
Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) <DOI:10.1016/j.neuroimage.2012.08.041>, or other multiscale data, using wavelet based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution.
Bayesian hierarchical methods for the detection of differences in rates of related outcomes for multiple treatments for clustered observations. Theoretical background for the models is given in Carragher (2017) <https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.736866>. The models in this package are extensions for multiple treatments and clusters. This software was developed for the Precision Drug Theraputics: Risk Prediction in Pharmacoepidemiology project as part of a Rutherford Fund Fellowship at Health Data Research (UK), Medical Research Council (UK) award reference MR/S003967/1 (<https://gtr.ukri.org/>). Principal Investigator: Raymond Carragher.
Provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017) <doi:10.3386/w24167>, and Baumeister and Hamilton (2018) <doi:10.1016/j.jmoneco.2018.06.005>. Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided.
Generate the James Blinding Index, as described in James et al (1996) 
             <https://pubmed.ncbi.nlm.nih.gov/8841652/> and the Bang Blinding Index, 
             as described in Bang et al (2004) <https://pubmed.ncbi.nlm.nih.gov/15020033/>.
             These are measures to assess whether or not satisfactory blinding has been 
             maintained in a randomized, controlled, clinical trial. These can be generated 
             for trial subjects, research coordinators and principal investigators, based 
             upon standardized questionnaires that have been administered, to assess whether
             or not they can correctly guess to which treatment arm (e.g. placebo or treatment) 
             subjects were assigned at randomization.
A GUI to correct measurement bias in DNA methylation
    analyses. The 'BiasCorrector' package just wraps the functions
    implemented in the 'R' package 'rBiasCorrection' into a shiny web
    application in order to make them more easily accessible. Publication:
    Kapsner et al. (2021) <doi:10.1002/ijc.33681>.
Statistical models of biased sampling in the form of 
  univariate and multivariate noncentral hypergeometric distributions, 
  including Wallenius' noncentral hypergeometric distribution and
  Fisher's noncentral hypergeometric distribution. 
  See vignette("UrnTheory") for explanation of these distributions.
  Literature: 
  Fog, A. (2008a). Calculation Methods for Wallenius' Noncentral Hypergeometric Distribution, Communications in Statistics, Simulation and Computation, 37(2) <doi:10.1080/03610910701790269>.
  Fog, A. (2008b). Sampling methods for Wallenius’ and Fisher’s noncentral hypergeometric distributions, Communications in Statistics—Simulation and Computation, 37(2)
  <doi:10.1080/03610910701790236>.
Parse a BibTeX file to a data.frame to make it accessible for further analysis and visualization.
A simple R wrapper for the Java BiBit algorithm from "A
    biclustering algorithm for extracting bit-patterns from binary datasets"
    from Domingo et al. (2011) <DOI:10.1093/bioinformatics/btr464>. An simple adaption for the BiBit algorithm which allows noise in the biclusters is also introduced as well as a function to guide the algorithm towards given (sub)patterns. Further, a workflow to derive noisy biclusters from discoverd larger column patterns is included as well.
Reading and writing BibTeX files using data frames in R sessions.
Tool for quantitative research in scientometrics and bibliometrics.
    It provides various routines for importing bibliographic data from 'SCOPUS',
    'Clarivate Analytics Web of Science' (<https://www.webofknowledge.com/>), 'Digital Science Dimensions' 
	(<https://www.dimensions.ai/>), 'OpenAlex' (<https://openalex.org/>), 'Cochrane Library' (<https://www.cochranelibrary.com/>),  'Lens' (<https://lens.org>), 
	and 'PubMed' (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis 
    and building networks for co-citation, coupling, scientific collaboration and co-word analysis.
It contains some example datasets used in 'bibliometrix'. The data are bibliographic datasets exported from the 'SCOPUS' (<https://scopus.com>) and 'Clarivate Analytics Web of Science' (<https://www.webofscience.com/>) databases. They can be used to test the different features of the package 'bibliometrix' (<https://bibliometrix.org>).
Functions to find edges for bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. The weights of network edges can be calculated according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse. These functions are optimized to be be used on large dataset. The package contains functions inspired by: Leydesdorff, Loet and Park, Han Woo (2017) <doi:10.1016/j.joi.2016.11.007>; Perianes-Rodriguez, Antonio, Ludo Waltman, and Nees Jan Van Eck (2016) <doi:10.1016/j.joi.2016.10.006>; Sen, Subir K. and Shymal K. Gan (1983) <http://nopr.niscair.res.in/handle/123456789/28008>; Shen, Si, Zhu, Danhao, Rousseau, Ronald, Su, Xinning and Wang, Dongbo (2019) <doi:10.1016/j.joi.2019.01.012>; Zhao, Dangzhi and Strotmann, Andreas (2008) <doi:10.1002/meet.2008.1450450292>.
Generates a list, with a size defined by the user, containing the main scientific references and the frequency distribution of authors and journals in the list obtained.
    The database is a dataframe with academic production metadata made available by bibliographic collections such as Scopus, Web of Science, etc.
    The temporal evolution of scientific production on a given topic is presented and ordered lists of articles are constructed by number of citations and of authors and journals by level of productivity.
    Massimo Aria, Corrado Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>.
    Caibo Zhou, Wenyan Song. (2021) <doi:10.1016/j.jclepro.2021.126943>.
Identifies and visualizes document overlap in any number of bibliographic datasets.
    This package implements the identification of overlapping documents through the exact match of a unique 
    identifier (e.g. Digital Object Identifier - DOI) and, for records where the identifier is absent, through a score calculated 
    from a set of fields commonly found in bibliographic datasets (Title, Source, Authors and Publication Year).
    Additionally, it provides functions to visualize the results of the document matching through a Venn diagram 
    and/or UpSet plot, as well as a summary of the matching procedure.
Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software 'CRExplorer' (see <http://crexplorer.net>). Further extension to more plot variants is planned.
Developed for the following tasks. 1- Simulating and computing the maximum likelihood 
              estimator for the Birnbaum-Saunders (BS) distribution, 2- Computing the Bayesian estimator for
              the parameters of the BS distribution based on reference prior proposed by Xu and Tang (2010)
              <doi:10.1016/j.csda.2009.08.004> and conjugate prior. 3- Computing the Bayesian estimator for
              the BS distribution based on conjugate prior. 4- Computing the Bayesian estimator for the BS
              distribution based on Jeffrey prior given by Achcar (1993) <doi:10.1016/0167-9473(93)90170-X>
              5- Computing the Bayesian estimator for the BS distribution under progressive type-II censoring
              scheme.
A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j.  The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) <doi:10.1016/j.heliyon.2023.e15947>.
Implements methods for sample size 
  reduction within Linear and Quadratic Discriminant Analysis in Lapanowski and Gaynanova (2020) <arXiv:2005.03858>.
  Also includes methods for non-linear discriminant analysis with simultaneous sparse feature selection in Lapanowski and Gaynanova (2019) PMLR 89:1704-1713.
The main function biclust() provides several algorithms to
        find biclusters in two-dimensional data: Cheng and Church (2000, ISBN:1-57735-115-0),
        spectral (2003) <doi:10.1101/gr.648603>, plaid model (2005) <doi:10.1016/j.csda.2004.02.003>, xmotifs (2003) <doi:10.1142/9789812776303_0008> and bimax (2006) <doi:10.1093/bioinformatics/btl060>. In addition, the
        package provides methods for data preprocessing (normalization
        and discretisation), visualisation, and validation of bicluster
        solutions.
Biclustering is a statistical learning technique that simultaneously 
    partitions and clusters rows and columns of a data matrix. Since the solution 
    space of biclustering is in infeasible to completely search with current 
    computational mechanisms, this package uses a greedy heuristic. The algorithm 
    featured in this package is, to the best our knowledge, the first biclustering 
    algorithm to work on data with missing values. Li, J., Reisner, J., Pham, H., 
    Olafsson, S., and Vardeman, S. (2020) Biclustering with Missing Data. Information 
    Sciences, 510, 304–316.
Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor.
Implementation of a collection of MCMC methods for Bayesian structure learning
    of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient
    inference on larger DAGs, the space of DAGs is pruned according to the data. To filter
    the search space, the algorithm employs a hybrid approach, combining constraint-based 
    learning with search and score. A reduced search space is initially defined on the basis
    of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with
    search and score. Search and score is then performed following two approaches:
    Order MCMC, or Partition MCMC.
    The BGe score is implemented for continuous data and the BDe score is implemented
    for binary data or categorical data. The algorithms may provide the maximum a posteriori 
    (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data.
    All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks.
    References:
    J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>,
    N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, 
    J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, 
    M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>,
    D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, 
    P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.
Implements an efficient estimator of bid-ask spreads from open, high, low, and close prices
  as described in Ardia, Guidotti, & Kroencke (2021) <https://www.ssrn.com/abstract=3892335>. 
  It also provides an implementation of the estimators described in 
  Roll (1984) <doi:10.1111/j.1540-6261.1984.tb03897.x>, 
  Corwin & Schultz (2012) <doi:10.1111/j.1540-6261.2012.01729.x>,
  and Abdi & Ranaldo (2017) <doi:10.1093/rfs/hhx084>.
Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).
Provides Tools for Accessing the Botanical Information and Ecology Network Database.  The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data (See <https://bien.nceas.ucsb.edu/bien/> for more Information).  This package provides functions that query the BIEN database by constructing and executing optimized SQL queries.
The calculator computes bifactor indices such as explained common variance (ECV), hierarchical Omega (OmegaH), percentage of uncontaminated correlations (PUC), item explained common variance (I-ECV), and more. This package is an R version of the 'Excel' based 'Bifactor Indices Calculator' (Dueber, 2017)  <doi:10.13023/edp.tool.01> with added convenience features for directly utilizing output from several programs that can fit confirmatory factor analysis or item response models.
Estimates fixed effects binary choice models (logit and probit) with potentially many
  individual fixed effects and computes average partial effects. Incidental parameter bias can be
  reduced with an asymptotic bias correction proposed by Fernandez-Val (2009)
  <doi:10.1016/j.jeconom.2009.02.007>.
Contains tools for survey statistics (especially in educational
    assessment) for datasets with replication designs (jackknife, 
    bootstrap, replicate weights; see Kolenikov, 2010;
    Pfefferman & Rao, 2009a, 2009b, <doi:10.1016/S0169-7161(09)70003-3>,
    <doi:10.1016/S0169-7161(09)70037-9>); Shao, 1996, 
    <doi:10.1080/02331889708802523>). 
    Descriptive statistics, linear and logistic regression, 
    path models for manifest variables with measurement error 
    correction and two-level hierarchical regressions for weighted 
    samples are included. Statistical inference can be conducted for 
    multiply imputed datasets and nested multiply imputed datasets
    and is in particularly suited for the analysis of plausible values
    (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; 
    Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham &
    Yanagida, 2016). The package development was supported by BIFIE 
    (Federal Institute for Educational Research, Innovation and Development 
    of the Austrian School System; Salzburg, Austria).
Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) <doi:10.1016/j.spl.2005.04.024> and Elbayoumi and Mostafa (2020) <doi:10.1002/sta4.342>. Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023)
             <doi:10.6339/23-JDS1092>.
Provides arithmetic functions for R matrix and 'big.matrix' objects as well as functions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value decomposition (SVD). A method matrix multiplication and an arithmetic method -for matrix addition, matrix difference- allows for mixed type operation -a matrix class object and a big.matrix class object- and pure type operation for two big.matrix class objects.
Extend the 'bigmemory' package with various analytics.
    Functions 'bigkmeans' and 'binit' may also be used with native R objects.
    For 'tapply'-like functions, the bigtabulate package may also be helpful.
    For linear algebra support, see 'bigalgebra'.  For mutex (locking) support
    for advanced shared-memory usage, see 'synchronicity'.
Enhanced message functions (cat() / message() / warning() / error()) 
    using wrappers around sprintf(). Also, multiple assertion functions 
    (e.g. to check class, length, values, files, arguments, etc.).
These tools accept integers in any base from 2 to 36, including 2's complement format, and perform actions like "AND," "OR", "NOT", "SHIFTR/L" etc. The output can be in any base specified. A direct base to base converter is included.
Provides functions for reading *.PGN files with more than one game, including large files without copying it into RAM (using 'ff' package or 'RSQLite' package). Handle chess data and chess aggregated data, count figure moves statistics, create player profile, plot winning chances, browse openings. Set of functions of R API to communicate with UCI-protocol based chess engines.
Format dates and times flexibly and to whichever locales
    make sense. Parses dates, times, and date-times in various formats
    (including string-based ISO 8601 constructions). The formatting syntax gives
    the user many options for formatting the date and time output in a precise
    manner. Time zones in the input can be expressed in multiple ways and there
    are many options for formatting time zones in the output as well. Several of
    the provided helper functions allow for automatic generation of locale-aware
    formatting patterns based on date/time skeleton formats and standardized
    date/time formats with varying specificity.
Functions to compute distances between probability measures or any other data object than can be posed in this way, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. For further details about the metrics please refer to Martos et al (2014) <doi:10.3233/IDA-140706>; Martos et al (2018) <doi:10.3390/e20010033>; Hernandez et al (2018, submitted); Martos et al (2018, submitted).
Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) <doi:10.1016/j.humimm.2015.12.006>. Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) <doi:10.1007/978-1-61779-842-9_14>.
Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 <doi:10.1016/j.spasta.2021.100496>; Orozco-Acosta et al., 2023 <doi:10.1016/j.cmpb.2023.107403> and Vicente et al., 2023 <doi:10.1007/s11222-023-10263-x>). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001).
A toolbox to analyze and simulate large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm' implements the estimation for large networks efficiently on large networks building on the 'lighthergm' package. Moreover, the package contains tools for simulating networks with local dependence to assess the estimates' goodness-of-fit.
Distributes Gaussian process calculations across nodes
    in a distributed memory setting, using Rmpi. The bigGP class 
    provides high-level methods for maximum likelihood with normal data, 
    prediction, calculation of uncertainty (i.e., posterior covariance 
    calculations), and simulation of realizations. In addition, bigGP 
    provides an API for basic matrix calculations with distributed 
    covariance matrices, including Cholesky decomposition, back/forwardsolve, 
    crossproduct, and matrix multiplication.
Response surface methods for drug synergy analysis. Available
    methods include generalized and classical Loewe formulations as well as Highest
    Single Agent methodology. Response surfaces can be plotted in an interactive
    3-D plot and formal statistical tests for presence of synergistic effects are
    available. Implemented methods and tests are described in the article 
    "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction 
    of the expected combined effect compatible with partial agonism and antagonism"
    by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, 
    Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) 
    <doi:10.1038/s41598-017-18068-5>.
Extend lasso and elastic-net model fitting for ultra
    high-dimensional, multi-gigabyte data sets that cannot be loaded into
    memory. Designed to be more memory- and computation-efficient than existing
    lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing
    the user to analyze big data analysis even on an ordinary laptop.
Calculation of physical (e.g. aerodynamic conductance, surface temperature), 
             and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties
			 from eddy covariance data and accompanying meteorological measurements. Calculations
			 assume the land surface to behave like a 'big-leaf' and return bulk ecosystem/canopy variables.
Regression for data too large to fit in memory.
Regression for data too large to fit in memory. This package functions exactly like the 'biglm' package, but works with later versions of R.
Unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. Dimensionality reduction is performed using a parallelized implementation of the t-Stochastic Neighboring Embedding algorithm (Garriga J. and Bartumeus F. (2018), <arXiv:1812.09869>).
Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. The main functions in the 'bigmatch' package are optcal() to find the optimal caliper, optconstant() to find the optimal number of nearest neighbors, and nfmatch() to find a near-fine balance match with a caliper and a restriction on the number of nearest neighbors. 
    Yu, R., Silber, J. H., and Rosenbaum, P. R. (2020). <DOI:10.1214/19-sts699>.
MDS is a statistic tool for reduction of dimensionality, using as input a distance
    matrix of dimensions n × n. When n is large, classical algorithms suffer from
    computational problems and MDS configuration can not be obtained.
    With this package, we address these problems by means of six algorithms, being two of them 
    original proposals:
        - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004).
        - Interpolation MDS proposed by Delicado P. and C. Pachón-García (2021)
        <arXiv:2007.11919> (original proposal).
        - Reduced MDS proposed by Paradis E (2018).
        - Pivot MDS proposed by Brandes U. and C. Pich (2007)
        - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021)
        <arXiv:2007.11919> (original proposal).
        - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006).
Create, store, access, and manipulate massive matrices.
    Matrices are allocated to shared memory and may use memory-mapped
    files.  Packages 'biganalytics', 'bigtabulate', 'synchronicity', and
    'bigalgebra' provide advanced functionality.
A shared resource interface for the bigmemory and synchronicity packages.
Classes for storing and manipulating arbitrary-precision
    integer vectors and high-precision floating-point vectors. These
    extend the range and precision of the 'integer' and 'double' data
    types found in R. This package utilizes the 'Boost.Multiprecision' C++
    library. It is specifically designed to work well with the 'tidyverse'
    collection of R packages.
Utility functions for easy parallelism in R. Include some reexports
    from other packages, utility functions for splitting and parallelizing over
    blocks, and choosing and setting the number of cores used.
A computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics <doi:10.1002/gepi.22136>. Also provides stochastic singular value decomposition for dense or sparse matrices.
Use Newton's method, coordinate descent, and METIS clustering
    to solve the L1 regularized Gaussian MLE inverse covariance
    matrix estimation problem.
Read large text files by splitting them in smaller files.
    Package 'bigreadr' also provides some convenient wrappers around fread()
    and fwrite() from package 'data.table'.
Allows the user to carry out GLM on very large
    data sets. Data can be created using the data_frame() function and appended
    to the object with object$append(data); data_frame and data_matrix objects
    are available that allow the user to store large data on disk. The data is
    stored as doubles in binary format and any character columns are transformed
    to factors and then stored as numeric (binary) data while a look-up table is
    stored in a separate .meta_data file in the same folder. The data is stored in
    blocks and GLM regression algorithm is modified and carries out a MapReduce-
    like algorithm to fit the model. The functions bglm(), and summary()
    and bglm_predict() are available for creating and post-processing of models.
    The library requires Armadillo installed on your system. It may not 
    function on windows since multi-core processing is done using mclapply() 
    which forks R on Unix/Linux type operating systems.
Easily talk to Google's 'BigQuery' database from R.
Easily talk to Google's 'BigQuery Storage' API from R
    (<https://cloud.google.com/bigquery/docs/reference/storage/rpc>).
Simulate multivariate data with arbitrary marginal distributions.
  'bigsimr' is a package for simulating high-dimensional multivariate data with 
  a target correlation and arbitrary marginal distributions via Gaussian copula. 
  It utilizes the Julia package 'Bigsimr.jl' for its core routines.
Easy-to-use, efficient, flexible and scalable tools for analyzing 
    massive SNP arrays. Privé et al. (2018) <doi:10.1093/bioinformatics/bty185>.
Provide a sparse matrix format with data stored on disk, to be
    used in both R and C++. This is intended for more efficient use of sparse 
    data in C++ and also when parallelizing, since data on disk does not need
    copying. Only a limited number of features will be implemented. For now,
    conversion can be performed from a 'dgCMatrix' or a 'dsCMatrix' from R 
    package 'Matrix'. A new compact format is also now available.
Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.
Easy-to-use, efficient, flexible and scalable statistical tools.
  Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping.
  It provides for instance matrix operations, Principal Component Analysis,
  sparse linear supervised models, utility functions and more
  <doi:10.1093/bioinformatics/bty185>.
Selecting linear and generalized linear models for large data sets
    using modified stepwise procedure and modern selection criteria (like
    modifications of Bayesian Information Criterion). Selection can be 
    performed on data which exceed RAM capacity. Bogdan et al., (2004)
    <doi:10.1534/genetics.103.021683>.
Fits Cox model via stochastic gradient descent. This implementation avoids computational instability of the standard Cox Model when dealing large datasets. Furthermore, it scales up with large datasets that do not fit the memory. It also handles large sparse datasets using proximal stochastic gradient descent algorithm. For more details about the method, please see Aliasghar Tarkhan and Noah Simon (2020) <arXiv:2003.00116v2>.
Extend the bigmemory package with 'table', 'tapply', and 'split'
    support for 'big.matrix' objects. The functions may also be used with native R
    matrices for improving speed and memory-efficiency.
For studying recurrent disease and death with competing
    risks, comparisons based on the well-known cumulative incidence function
    can be confounded by different prevalence rates of the competing events.
    Alternatively, comparisons of the conditional distribution of the survival
    time given the failure event type are more relevant for investigating the
    prognosis of different patterns of recurrence disease. This package implements
    a nonparametric estimator for the conditional cumulative incidence function
    and a nonparametric conditional bivariate cumulative incidence function for the
    bivariate gap times proposed in Huang et al. (2016) <doi:10.1111/biom.12494>.
Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.
Utility functions for large-scale data. For now, package 'bigutilsr'
    mainly includes functions for outlier detection and unbiased PCA projection.
Estimates VAR and VARX models with Structured Penalties using the methods developed by Nicholson et al (2017)<doi:10.1016/j.ijforecast.2017.01.003> and Nicholson et al (2020) <doi:10.48550/arXiv.1412.5250>.
Anonymised Bay Area bike share trip data for the year 2014. 
    Also contains additional metadata on stations and weather.
Co-clustering of the rows and columns of a contingency or binary matrix, or double binary matrices and model selection for the number of row and column clusters. Three models are considered: the Poisson latent block model for contingency matrix, the binary latent block model for binary matrix and a new model we develop: the multiple latent block model for double binary matrices. A new procedure named bikm1 is implemented to investigate more efficiently the grid of numbers of clusters. Then, the studied model selection criteria are the integrated completed likelihood (ICL) and the Bayesian integrated likelihood (BIC). Finally, the co-clustering adjusted Rand index (CARI) to measure agreement between co-clustering partitions is implemented. Robert Valerie, Vasseur Yann, Brault Vincent (2021) <doi:10.1007/s00357-020-09379-w>.
Performs logistic regression for binary longitudinal
  data, allowing for serial dependence among observations from a given
  individual and a random intercept term. Estimation is via maximization
  of the exact likelihood of a suitably defined model. Missing values and 
  unbalanced data are allowed, with some restrictions. 
  M. Helena Goncalves et al.(2007) <DOI: 10.18637/jss.v046.i09>.
Contains data sets regarding songs on the Billboard Hot 100 list
    from 1960 to 2016. The data sets include the ranks for the given year,
    musical features of a lot of the songs and lyrics for several of the songs
    as well.
Provides an 'htmlwidgets' interface to 'billboard.js', 
    a re-usable easy interface JavaScript chart library, based on D3 v4+.
    Chart types include line charts, scatterplots, bar/lollipop charts, histogram/density plots, pie/donut charts and gauge charts.
    All charts are interactive, and a proxy method is implemented to smoothly update a chart without rendering it again in 'shiny' apps.
Time series analysis, (dis)aggregation and manipulation, e.g. time series extension, merge, projection, lag, lead, delta, moving and cumulative average and product, selection by index, date and year-period, conversion to daily, monthly, quarterly, (semi)annually. Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, endogenous targeting and model renormalization, structural stability, stochastic simulation and forecast, optimal control.
Defines the functions used to compute the
  bimodal index as defined by Wang et al. (2009)
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2730180/>,
  <doi:10.4137/CIN.S2846>.
R client to the 'Binance' Public Rest API for data collection on cryptocurrencies, portfolio management and trading: <https://github.com/binance/binance-spot-api-docs/blob/master/rest-api.md>.
Provides methods for the binarization of one-dimensional data and some visualization functions.
Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold.
Tools to create binary dosage from either VCF
    or GEN files, merge binary dosage files, and read
    binary dosage files.
Implements variable selection for high dimensional datasets with a binary response
    variable using the EM algorithm.  Both probit and logit models are supported.  Also included 
    is a useful function to generate high dimensional data with correlated variables.
Allows the estimation and prediction for binary Gaussian process model. The mean function can be assumed to have time-series structure. The estimation methods for the unknown parameters are based on penalized quasi-likelihood/penalized quasi-partial likelihood and restricted maximum likelihood. The predicted probability and its confidence interval are computed by Metropolis-Hastings algorithm. More details can be seen in Sung et al (2017) <arXiv:1705.02511>.
Estimates marginalized mean and dependence model parameters for correlated binary response data. 
             Dependence model may include transition and/or latent variable terms. 
             Methods are described in: Schildcrout and Heagerty (2007) <doi:10.1111/j.1541-0420.2006.00680.x>, Heagerty (1999) <doi:10.1111/j.0006-341x.1999.00688.x>, Heagerty (2002) <doi:10.1111/j.0006-341x.2002.00342.x>.
A collection of 'LaTeX' styles using 'Beamer' customization for
 pdf-based presentation slides in 'RMarkdown'. At present it contains
 'RMarkdown' adaptations of the LaTeX themes 'Metropolis' (formerly 'mtheme')
 theme by Matthias Vogelgesang and others (now included in 'TeXLive'), the
 'IQSS' by Ista Zahn (which is included here), and the 'Monash' theme by
 Rob J Hyndman. Additional (free) fonts may be needed: 'Metropolis' prefers
 'Fira', and 'IQSS' requires 'Libertinus'.
Estimate the correlation between two irregular time series that are not necessarily sampled on identical time points. This program is also applicable to the situation of two evenly spaced time series that are not on the same time grid. 'BINCOR' is based on a novel estimation approach proposed by Mudelsee (2010, 2014) to estimate the correlation between two climate time series with different timescales. The idea is that autocorrelation (AR1 process) allows to correlate values obtained on different time points. 'BINCOR' contains four functions: bin_cor() (the main function to build the binned time series), plot_ts() (to plot and compare the irregular and binned time series, cor_ts() (to estimate the correlation between the binned time series) and ccf_ts() (to estimate the cross-correlation between the binned time series).
Implements functions for multi-class
   discriminant analysis using binary predictors, for corresponding 
   variable selection, and for dichotomizing continuous data.
Generation of correlated artificial binary data.
Provides a simple interface for creating active bindings where the
    bound function accepts additional arguments.
Provides an easy way to fill an environment with active
    bindings that call a C++ function.
Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored.  We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results.  We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.
Binford's hunter-gatherer data includes more than 200 variables
    coding aspects of hunter-gatherer subsistence, mobility, and social organization
    for 339 ethnographically documented groups of hunter-gatherers.
Provides maximum likelihood estimates of the 
    performance parameters that drive a binomial distribution of observed 
    errors, and takes full advantage of zero error observations. High performance 
    communications systems typically have inherent noise sources and other 
    performance limitations that need to be estimated. Measurements made at 
    high signal to noise ratios typically result in zero 
    errors due to limitation in available measurement time. Package includes 
    theoretical performance functions for common modulation schemes (Proakis, 
    "Digital Communications" (1995, <ISBN:0-07-051726-6>)), polarization shifted 
    QPSK (Agrell & Karlsson (2009, <DOI:10.1109/JLT.2009.2029064>)), and utility 
    functions to work with the performance functions.
Collect  your data on digital marketing campaigns from bing Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Methods for estimation and hypothesis testing of proportions
 in group testing designs: methods for estimating a proportion in a single
        population (assuming sensitivity and specificity equal to 1 in designs
        with equal group sizes), as well as hypothesis tests and
        functions for experimental design for this situation. For
        estimating one proportion or the difference of proportions, a
        number of confidence interval methods are included, which can
        deal with various different pool sizes. Further, regression
        methods are implemented for simple pooling and matrix pooling
        designs.
        Methods for identification of positive items in group
        testing designs: Optimal testing configurations can be found 
        for hierarchical and array-based algorithms. Operating 
        characteristics can be calculated for testing configurations 
        across a wide variety of situations.
Methods for the group testing identification problem: 1) Operating 
    characteristics (e.g., expected number of tests) for commonly used 
    hierarchical and array-based algorithms, and 2) Optimal testing 
    configurations for these same algorithms. Methods for the group testing 
    estimation problem: 1) Estimation and inference procedures for an overall 
    prevalence, and 2) Regression modeling for commonly used hierarchical and 
    array-based algorithms.
Consider an at-most-K-stage group sequential design with only an upper bound for the last analysis and non-binding lower bounds.With binary endpoint, two kinds of test can be applied, asymptotic test based on normal distribution and exact test based on binomial distribution. This package supports the computation of boundaries and conditional power for single-arm group sequential test with binary endpoint, via either asymptotic or exact test. The package also provides functions to obtain boundary crossing probabilities given the design.
Binomial Haar-Fisz transforms for Gaussianization as in Nunes and Nason (2009).
Tools and functions for managing the download of binary files.
    Binary repositories are defined in 'YAML' format. Defining new 
    pre-download, download and post-download templates allow additional 
    repositories to be added.
A molecular genetics tool that processes binary data from fragment analysis. It consolidates replicate sample pairs, outputs summary statistics, and produces hierarchical clustering trees and nMDS plots. This package was developed from the publication available here: <https://www.sciencedirect.com/science/article/pii/S1049964420306538>. The GUI version of this package is available on the R Shiny online server at: <https://clarkevansteenderen.shinyapps.io/BINMAT/> or it is accessible via GitHub by typing: shiny::runGitHub("BinMat", "clarkevansteenderen") into the console in R. Two real-world datasets accompany the package: an AFLP dataset of Bunias orientalis samples from Tewes et. al. (2017) <https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2745.12869>, and an ISSR dataset of Nymphaea specimens from Reid et. al. (2021) <https://www.sciencedirect.com/science/article/pii/S0304377021000218> . The authors of these publications are thanked for allowing the use of their data.
Asymptotic simultaneous confidence intervals for comparison of many treatments with one control,
 for the difference of binomial proportions, allows for Dunnett-like-adjustment, Bonferroni or unadjusted intervals.
 Simulation of power of the above interval methods, approximate calculation of any-pair-power, and sample size
 iteration based on approximate any-pair power. Exact conditional maximum test for many-to-one comparisons to a control.
Generation of multiple binary and continuous non-normal variables simultaneously 
             given the marginal characteristics and association structure based on the methodology 
             proposed by Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Generating multiple binary and normal variables simultaneously given marginal characteristics and association structure based on the methodology proposed by Demirtas and Doganay (2012) <DOI:10.1080/10543406.2010.521874>.
Constructs confidence intervals on the probability of
        success in a binomial experiment via several parameterizations.
Twelve confidence intervals for one binomial proportion or a vector of binomial proportions are computed. The confidence intervals are: Jeffreys,  Wald, Wald corrected, Wald, Blyth and Still,  Agresti and Coull, Wilson, Score, Score corrected, Wald logit, Wald logit corrected, Arcsine and Exact binomial. References include, among others: Vollset, S. E. (1993). "Confidence intervals for a binomial proportion". Statistics in Medicine, 12(9): 809-824. <doi:10.1002/sim.4780120902>.
The 'binomialRF' is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, 'binomialRF' then tests whether a feature is selected more often than by random chance.
Detection of a statistically significant trend in the data provided by the user.
    This is based on the a signed test based on the binomial distribution.
    The package returns a trend test value, T, and also a p-value. A T value 
    close to 1 indicates a rising trend, whereas a T value close to -1 indicates a decreasing
    trend. A T value close to 0 indicates no trend. There is also a command to visualize
    the trend. A test data set called gtsa_data is also available, which has global mean 
    temperatures for January, April, July, and October for the years 1851 to 2022.
    Reference: Walpole, Myers, Myers, Ye. (2007, ISBN: 0-13-187711-9).
Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Simulation and visualization depth-dependent integrated visual fields. Visual fields are measured monocularly at a single depth, yet real-life activities involve predominantly binocular vision at multiple depths. The package provides functions to simulate and visualize binocular visual field impairment in a depth-dependent fashion from monocular visual field results based on Ping Liu, Allison McKendrick, Anna Ma-Wyatt, Andrew Turpin (2019) <doi:10.1167/tvst.9.3.8>. At each location and depth plane, sensitivities are linearly interpolated from corresponding locations in monocular visual field and returned as the higher value of the two. Its utility is demonstrated by evaluating DD-IVF defects associated with 12 glaucomatous archetypes of 24-2 visual field pattern in the included 'shiny' apps.
Implements the First Fit Decreasing algorithm to achieve one dimensional heuristic bin packing. Runtime is of order O(n log(n)) where n is the number of items to pack. See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details.
Implementation of algorithms for cutting numerical values
    exhibiting a potentially highly skewed distribution into evenly distributed
    groups (bins). This functionality can be applied for binning discrete
    values, such as counts, as well as for discretization of continuous values,
    for example, during generation of features used in machine learning
    algorithms.
Provides methods for piecewise smooth regression. A piecewise smooth signal is estimated by applying a bootstrapped test recursively (binary segmentation approach). Each bootstrapped test decides whether the underlying signal is smooth on the currently considered subsegment or contains at least one further change-point.
Standard template library 
 containers are used to implement an efficient binary segmentation
 algorithm, which is log-linear on average and quadratic in the
 worst case.
For a series of binary responses, create stopping boundary with exact results after stopping, allowing updating for missing assessments.
Provides several methods for generating density functions
    based on binned data. Methods include step function, recursive
    subdivision, and optimized spline. Data are assumed to be nonnegative, 
    the top bin is assumed to have no upper bound, but the bin widths need
    be equal. All PDF smoothing methods maintain the areas specified by 
    the binned data. (Equivalently, all CDF smoothing methods interpolate 
    the points specified by the binned data.) In practice, an estimate for 
    the mean of the distribution should be supplied as an optional argument.
    Doing so greatly improves the reliability of statistics computed from 
    the smoothed density functions. Includes methods for estimating the Gini 
    coefficient, the Theil index, percentiles, and random deviates from a 
    smoothed distribution. Among the three methods, the optimized spline 
    (splinebins) is recommended for most purposes. The percentile and 
    random-draw methods should be regarded as experimental, and these methods 
    only support splinebins.
The Bayesian MCMC estimation of parameters for Thomas-type cluster
    point process with various inhomogeneities. It allows for inhomogeneity in
    (i) distribution of parent points, (ii) mean number of points in a cluster,
    (iii) cluster spread. The package also allows for the Bayesian MCMC
    algorithm for the homogeneous generalized Thomas process. The cluster size
    is allowed to have a variance that is greater or less than the expected
    value (cluster sizes are over or under dispersed). Details are described in
    Dvořák, Remeš, Beránek & Mrkvička (2022) <arXiv: 10.48550/arXiv.2205.07946>.
Provides tools for statistical analysis using the binscatter methods developed by Cattaneo, Crump, Farrell and Feng (2023a) <arXiv:1902.09608>, Cattaneo, Crump, Farrell and Feng (2023b) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2023_NonlinearBinscatter.pdf> and Cattaneo, Crump, Farrell and Feng (2023c) <arXiv:1902.09615>. Binscatter provides a flexible way of describing the relationship between two variables based on partitioning/binning of the independent variable of interest. binsreg(), binsqreg() and binsglm() implement binscatter least squares regression, quantile regression and generalized linear regression respectively, with particular focus on constructing binned scatter plots. They also implement robust (pointwise and uniform) inference of regression functions and derivatives thereof. binstest() implements hypothesis testing procedures for parametric functional forms of and nonparametric shape restrictions on the regression function. binspwc() implements hypothesis testing procedures for pairwise group comparison of binscatter estimators. binsregselect() implements data-driven procedures for selecting the number of bins for binscatter estimation. All the commands allow for covariate adjustment, smoothness restrictions and clustering.
Various supervised and unsupervised binning tools
    including using entropy, recursive partition methods
    and clustering.
A recently proposed Bayesian BIN model disentangles the underlying processes 
    that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into 
    three components: bias, partial information, and noise. By describing the differences between two 
    groups of forecasters, the model allows the user to carry out useful inference, such as calculating 
    the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information.
    It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient 
    extraction of valid information from the environment improves forecasting accuracy. This package provides 
    easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov,
    Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN 
    Model of Forecasting" <doi:10.1287/mnsc.2020.3882>.
Imports benthic count data, reformats this data, and
        computes environmental inferences from this data.
Utilities to process, organize and explore protein structure,
    sequence and dynamics data. Features include the ability to read and write
    structure, sequence and dynamic trajectory data, perform sequence and structure
    database searches, data summaries, atom selection, alignment, superposition,
    rigid core identification, clustering, torsion analysis, distance matrix
    analysis, structure and sequence conservation analysis, normal mode analysis,
    principal component analysis of heterogeneous structure data, and correlation
    network analysis from normal mode and molecular dynamics data. In addition,
    various utility functions are provided to enable the statistical and graphical
    power of the R environment to work with biological sequence and structural data.
    Please refer to the URLs below for more information.
Contains all the necessary tools to process audio recordings of
             various formats (e.g., WAV, WAC, MP3, ZC), filter noisy files, 
             display audio signals, detect and extract automatically acoustic
             features for further analysis such as classification.
Provides an integrated data management solution for assets installed via the 'Biobricks.ai' platform. Streamlines the process of loading and interacting with diverse datasets in a consistent manner. A list of bricks is available at <https://status.biobricks.ai>. Documentation for 'Biobricks.ai' is available at <https://docs.biobricks.ai>.
Download stats reported from the BioConductor.org stats website.
Implement in 'R' interactive Circos-like visualizations of genomic data, to map information
	such as genetic variants, genomic fusions and aberrations to a circular genome, as proposed by the
	'JavaScript' library 'BioCircos.js', based on the 'JQuery' and 'D3' technologies. The output is by 
	default displayed in stand-alone HTML documents or in the 'RStudio' viewer pane. Moreover it can be
	integrated in 'R Markdown' documents and 'Shiny' applications.
Using numeric or raster data, this package contains functions to 
    calculate: complete water balance, bioclimatic balance, bioclimatic 
    intensities, reports for individual locations, multi-layered rasters for
    spatial analysis.
A convenient tool to install and update Bioconductor packages.
Tools to create, validate, and export BioCompute Objects
    described in King et al. (2019) <doi:10.17605/osf.io/h59uh>.
    Users can encode information in data frames, and compose
    BioCompute Objects from the domains defined by the standard.
    A checksum validator and a JSON schema validator are provided.
    This package also supports exporting BioCompute Objects as JSON,
    PDF, HTML, or 'Word' documents, and exporting to cloud-based platforms.
The Biodem package provides a number of functions for Biodemographic analysis.
Graphical User Interface (via the R-Commander) and utility functions (often based on the vegan package) for statistical analysis of biodiversity and ecological communities, including species accumulation curves, diversity indices, Renyi profiles, GLMs for analysis of species abundance and presence-absence, distance matrices, Mantel tests, and cluster, constrained and unconstrained ordination analysis. A book on biodiversity and community ecology analysis is available for free download from the website. In 2012, methods for (ensemble) suitability modelling and mapping were expanded in the package.
A tool to perform all different statistical tests and calculations
    needed by Biological Dosimetry Laboratories.
Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity.  Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions.
High- and low-level functions for processing biogas data and predicting biogas production. Molar mass and calculated oxygen demand (COD') can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to (possibly user-defined) standard temperature and pressure. Gas quantity can be converted between volume, mass, and moles. Gas composition, cumulative production, or other variables can be interpolated to a specified time. Cumulative biogas and methane production (and rates) can be calculated using volumetric, manometric, gravimetric, or gas density methods for any number of bottles. With cumulative methane production data and data on bottle contents, biochemical methane potential (BMP) can be calculated and summarized, including subtraction of the inoculum contribution and normalization by substrate mass. Cumulative production and production rates can be summarized in several different ways (e.g., omitting normalization) using the same function. Biogas quantity and composition can be predicted from substrate composition and additional, optional data. Lastly, inoculum and substrate mass can be determined for planning BMP experiments.
Is used to simulate and fit biological geometries. 'biogeom' incorporates several novel universal parametric equations that can generate the profiles of bird eggs, flowers, linear and lanceolate leaves, seeds, starfish, and tree-rings (Gielis (2003) <doi:10.3732/ajb.90.3.333>; Shi et al. (2020) <doi:10.3390/sym12040645>), three growth-rate curves representing the ontogenetic growth trajectories of animals and plants against time, and the axially symmetrical and integral forms of all these functions (Shi et al. (2017) <doi:10.1016/j.ecolmodel.2017.01.012>; Shi et al. (2021) <doi:10.3390/sym13081524>). The optimization method proposed by Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308> was used to estimate model parameters. 'biogeom' includes several real data sets of the boundary coordinates of natural shapes, including avian eggs, fruit, lanceolate and ovate leaves, tree rings, seeds, and sea stars,and can be potentially applied to other natural shapes. 'biogeom' can quantify the conspecific or interspecific similarity of natural outlines, and provides information with important ecological and evolutionary implications for the growth and form of living organisms. Please see Shi et al. (2022) <doi:10.1111/nyas.14862> for details.
Tools for extraction and analysis of various
    n-grams (k-mers) derived from biological sequences (proteins
    or nucleic acids). Contains QuiPT (quick permutation test) for fast
    feature-filtering of the n-gram data.
Modelling of population growth under static and dynamic environmental conditions. 
    Includes functions for model fitting and making prediction under isothermal and 
    dynamic conditions. The methods (algorithms & models) are based on
    predictive microbiology (See Perez-Rodriguez and Valero (2012, ISBN:978-1-4614-5519-6)).
Single linkage clustering and connected component analyses are often performed on biological images. 'Bioi' provides a set of functions for performing these tasks. This functionality is implemented in several key functions that can extend to from 1 to many dimensions. The single linkage clustering method implemented here can be used on n-dimensional data sets, while connected component analyses are limited to 3 or fewer dimensions.
Tools for 3D imaging, mostly for biology/microscopy. 
    Read and write TIFF stacks. Functions for segmentation, filtering and analyzing 3D point patterns.
Functions for modelling microbial inactivation under
    isothermal or dynamic conditions. The calculations are based on several mathematical models broadly
    used by the scientific community and industry. Functions enable to make predictions for cases where the
    kinetic parameters are known. It also implements functions for parameter estimation for isothermal and
    dynamic conditions. The model fitting capabilities include an Adaptive Monte Carlo method for a Bayesian
    approach to parameter estimation.
Analyze and plot the abundance of different RNA biotypes present in a count matrix, this evaluation can be useful if you want to test different strategies of normalization or analyze a particular biotype in a differential gene expression analysis.
Generate urls and hyperlinks to commonly used biological databases
    and resources based on standard identifiers. This is primarily useful when
    writing dynamic reports that reference things like gene symbols in text or
    tables, allowing you to, for example, convert gene identifiers to hyperlinks
    pointing to their entry in the 'NCBI' Gene database. Currently supports
    'NCBI' Gene, 'PubMed', Gene Ontology, 'KEGG', CRAN and Bioconductor.
Variable selection methods are provided for several classification methods: the lasso/elastic net, PCLDA, PLSDA, and several t-tests. Two approaches for selecting cutoffs can be used, one based on the stability of model coefficients under perturbation, and the other on higher criticism.
Perform large scale genomic data retrieval and functional annotation retrieval. This package aims to provide users with a standardized
                way to automate genome, proteome, 'RNA', coding sequence ('CDS'), 'GFF', and metagenome
                retrieval from 'NCBI RefSeq', 'NCBI Genbank', 'ENSEMBL', 
                and 'UniProt' databases. Furthermore, an interface to the 'BioMart' database
                (Smedley et al. (2009) <doi:10.1186/1471-2164-10-22>) allows users to retrieve
                functional annotation for genomic loci. In addition, users can download entire databases such
                as 'NCBI RefSeq' (Pruitt et al. (2007) <doi:10.1093/nar/gkl842>), 'NCBI nr',
                'NCBI nt', 'NCBI Genbank' (Benson et al. (2013) <doi:10.1093/nar/gks1195>), etc. with only one command.
Contains functions to estimate aboveground biomass/carbon and its uncertainty in tropical forests. 
	These functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty, 
	(3) construct height-diameter models, (4) manage tree and plot coordinates, 
	(5) estimate the aboveground biomass/carbon at the stand level with associated uncertainty. 
	To cite 'BIOMASS', please use citation("BIOMASS"). 
	See more in the article of Réjou-Méchain et al. (2017) <doi:10.1111/2041-210X.12753>.
Provides functions to aid in the design and analysis of
    agronomic and agricultural experiments through easy access to
    documentation and helper functions, especially for users who are
    learning these concepts. While not required for most functionality,
    this package enhances the `asreml` package which provides a 
    computationally efficient algorithm for fitting mixed models 
    using Residual Maximum Likelihood. It is a commercial package 
    that can be purchased as 'asreml-R' from 'VSNi' 
    <https://vsni.co.uk/>, who will supply a zip file for local 
    installation/updating (see <https://asreml.kb.vsni.co.uk/>).
Functions for species distribution modeling, calibration and evaluation, 
  ensemble of models, ensemble forecasting and visualization. The package permits to run
  consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences)
  dataset and to combine them in ensemble models and ensemble projections. Some bench of other 
  evaluation and visualisation tools are also available within the package.
Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.
Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population (R. Temple (2010) <DOI:10.1038/clpt.2010.233>). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package is a tool to help evaluate biomarkers for prognostic enrichment of clinical trials.
Prognostic Enrichment is a strategy of enriching a clinical trial for testing an intervention intended to prevent or delay an unwanted clinical event.  A prognostically enriched trial enrolls only patients who are more likely to experience the unwanted clinical event than the broader patient population (R. Temple (2010) <doi:10.1038/clpt.2010.233>). By testing the intervention in an enriched study population, the trial may be adequately powered with a smaller sample size, which can have both practical and ethical advantages.
    This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes.
Combines the 'magick' and 'imager' packages to streamline image analysis, focusing on feature extraction and quantification from biological images, especially 
    microparticles. By providing high throughput pipelines and clustering capabilities, 'biopixR' facilitates efficient insight generation for researchers (Schneider J. et al. (2019) 
    <doi:10.21037/jlpm.2019.04.05>).
bioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction
  networks.
  Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions
  are defined.
  For deterministic solutions, bioPN creates the associated system of differential equations "on the fly", and
  solves it with a Runge Kutta Dormand Prince 45 explicit algorithm.
  For stochastic solutions, bioPN offers variants of Gillespie algorithm, or SSA.
  For hybrid deterministic/stochastic,
  it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions.
  bioPN algorithms are developed in C to achieve adequate performance.
Several tools for analyzing diagnostic tests and 2x2 contingency tables are provided. In particular, positive and negative predictive values for a diagnostic tests can be calculated from prevalence, sensitivity and specificity values. For contingency tables, relative risk and odds ratio measures are estimated. Furthermore, confidence intervals are provided.
Extract, visualize and summarize aerial movements of birds and
    insects from weather radar data. See Dokter, A. M. et al. (2018) 
    "bioRad: biological analysis and visualization of weather radar data" <doi:10.1111/ecog.04028>
    for a software paper describing package and methodologies.
The main purpose of this package is to propose a transparent methodological framework to compare bioregionalisation methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) <doi:10.1111/j.1365-2699.2010.02375.x>) and network algorithms (Lenormand et al. (2019) <doi:10.1002/ece3.4718> and Leroy et al. (2019) <doi:10.1111/jbi.13674>).
A robust framework for analyzing mortality data from bioassays for one or several strains/lines/populations.
Utilities dedicated to the analysis of
        biological sequences by metric MultiDimensional Scaling with
        projection of supplementary data. It contains functions for
        reading multiple sequence alignment files, calculating distance
        matrices, performing metric multidimensional scaling and
        visualizing results.
Provides tools for the calculation of common biodiversity indices from count data. Additionally, it incorporates bootstrapping techniques to generate multiple samples, facilitating the estimation of confidence intervals around these indices. Furthermore, the package allows for the exploration of how variation in these indices changes with differing numbers of sites, making it a useful tool with which to begin an ecological analysis. Methods are based on the following references: Chao et al. (2014) <doi:10.1890/13-0133.1>, Chao and Colwell (2022) <doi:10.1002/9781119902911.ch2>, Hsieh, Ma,` and Chao (2016) <doi:10.1111/2041-210X.12613>.
Unified and user-friendly framework for using new 
             distributional representations of biosensors data in different statistical modeling 
             tasks: regression models, hypothesis testing, cluster analysis, visualization, and 
             descriptive analysis. Distributional representations are a functional extension of 
             compositional time-range metrics and we have used them successfully so far in modeling 
             glucose profiles and accelerometer data. However, these functional representations can 
             be used to represent any biosensor data such as ECG or medical imaging such as fMRI.
             Matabuena M, Petersen A, Vidal JC, Gude F. "Glucodensities: A new representation of 
             glucose profiles using distributional data analysis" (2021) 
             <doi:10.1177/0962280221998064>.
Classes and functions to work with biological sequences (DNA, RNA and amino acid sequences).
    Implements S3 infrastructure to work with biological sequences as described in Keck (2020) <doi:10.1111/2041-210X.13490>.
    Provides a collection of functions to perform biological conversion among classes
    (transcription, translation) and basic operations on sequences
    (detection, selection and replacement based on positions or patterns).
    The package also provides functions to import and export sequences from and to other package formats.
Data processing tools to compute the rectified, integrated and the averaged EMG. Routines for automatic detection of activation phases. A routine to compute and plot the ensemble average of the EMG. An EMG signal simulator for general purposes.
A beginners toolbox to help those in ecology who want to deepen their understanding or utilize Bioacoustics in their work. The package has a number of utilizations from calculating frequency from waveform, performing operations in dB, and determining acoustic range of recorders. The majority of this package is based on key concepts learned from the K. Lisa Yang Center for Conservation Bioacoustics at Cornell University and their associated course: Introduction to Bioacoustics course. More information can be found within the walk through vignettes at <https://github.com/MattyD797/bioSNR/tree/main/vignettes>.
Provides some tools for developing and validating prediction models, estimate expected survival of patients and visualize them graphically. Most of the implemented methods are based on penalized regressions such as: the lasso (Tibshirani R (1996)), the elastic net (Zou H et al. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>), the adaptive lasso (Zou H (2006) <doi:10.1198/016214506000000735>), the stability selection (Meinshausen N et al. (2010) <doi:10.1111/j.1467-9868.2010.00740.x>), some extensions of the lasso (Ternes et al. (2016) <doi:10.1002/sim.6927>), some methods for the interaction setting (Ternes N et al. (2016) <doi:10.1002/bimj.201500234>), or others. A function generating simulated survival data set is also provided.
Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".
Tutorials for statistics, aimed at biological scientists. 
    Subjects range from basic descriptive statistics 
    through to complex linear modelling. The tutorials
    include text, videos, interactive coding exercises
    and multiple choice quizzes. The package also 
    includes 19 datasets which are used in the 
    tutorials.
Datasets and functions for the book "Initiation à la Statistique avec R", F. Bertrand and M. Maumy-Bertrand (2022, ISBN:978-2100782826 Dunod, fourth edition).
Miscellaneous code snippets and functions with pipes and
    multiple package dependencies used for summarizing nested data and 
    adverse events.
Calculates a range of UK freshwater invertebrate biotic indices
    including BMWP, Whalley, WHPT, Habitat-specific BMWP, AWIC, LIFE and PSI.
Tools designed to perform and evaluate cluster analysis (including Tocher's algorithm), 
	discriminant analysis and path analysis (standard and under collinearity), as well as some 
	useful miscellaneous tools for dealing with sample size and optimum plot size calculations. 
	A test for seed sample heterogeneity is now available. Mantel's permutation test can be found in this package. 
	A new approach for calculating its power is implemented. biotools also contains tests for genetic covariance components.
	Heuristic approaches for performing non-parametric spatial predictions of generic response variables and 
	spatial gene diversity are implemented.
Creates an area-proportional Venn diagram of 2 or 3 circles. 'BioVenn' is the only R package that can automatically generate an accurate area-proportional Venn diagram by having only lists of (biological) identifiers as input. Also offers the option to map Entrez and/or Affymetrix IDs to Ensembl IDs. In SVG mode, text and numbers can be dragged and dropped. Based on the BioVenn web interface available at <https://www.biovenn.nl>. Hulsen (2021) <doi:10.3233/DS-210032>.
Functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology.
Generates interactive bipartite graphs using the D3 library.
    Designed for use with the 'bipartite' analysis package.
    Sources open source 'viz-js' library 
    Adapted from examples at <https://bl.ocks.org/NPashaP> (released under GPL-3).
Function bipmod() that partitions a bipartite network into non-overlapping biclusters by maximizing bipartite modularity defined in Barber (2007) <doi:10.1103/PhysRevE.76.066102> using the bipartite version of the algorithm described in Treviño (2015) <doi:10.1088/1742-5468/2015/02/P02003>.
We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) <DOI:10.1002/sim.8859>. This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.
A modern view on the principal component analysis biplot with 
    calibrated axes. Create principal component analysis biplots rendered in 
    HTML with significant reactivity embedded within the plot. Furthermore, 
    the traditional biplot view is enhanced by translated axes with inter-class
    kernel densities superimposed. For more information on biplots, see 
    Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0).
A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot. This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology.
Provides users with an EZ-to-use platform for representing data
             with biplots. Currently principal component analysis (PCA) and canonical variate
             analysis (CVA) biplots are included. This is accompanied by various formatting
             options for the samples and axes. Alpha-bags and concentration ellipses
             are included for visual enhancements and interpretation. For an extensive
             discussion on the topic, see Gower, J.C., Lubbe, S. and le Roux, N.J.
             (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Wiley: Chichester.
Provides a GUI with which users can construct and interact
        with biplots.
Highly efficient functions for estimating various rank (centrality) measures of nodes in bipartite graphs (two-mode networks). Includes methods for estimating HITS, CoHITS, BGRM, and BiRank with implementation primarily inspired by He et al. (2016) <doi:10.1109/TKDE.2016.2611584>. Also provides easy-to-use tools for efficiently estimating PageRank in one-mode graphs, incorporating or removing edge-weights during rank estimation, projecting two-mode graphs to one-mode, and for converting edgelists and matrices to sparseMatrix format. Best of all, the package's rank estimators can work directly with common formats of network data including edgelists (class data.frame, data.table, or tbl_df) and adjacency matrices (class matrix or dgCMatrix).
R functions to read EURING data and analyse re-encounter data of birds marked by metal rings. For a tutorial, go to <doi:10.1080/03078698.2014.933053>.
Extract data from 'Birdscan MR1' 'SQL' vertical-looking radar databases, filter, and process them to Migration Traffic Rates (#objects per hour and km) of, for example birds, and insects. Object classifications in the 'Birdscan MR1' databases are based on the dataset of Haest et al. (2021) <doi:10.5281/zenodo.5734960>). Migration Traffic Rates can be calculated separately for different height bins (with a height resolution of choice) as well as over time periods of choice (e.g., 1/2 hour, 1 hour, 1 day, day/night, the full time period of observation, and anything in between). Two plotting functions are also included to explore the data in the 'SQL' databases and the resulting Migration Traffic Rate results. For details on the Migration Traffic Rate calculation procedures, see Schmid et al. (2019) <doi:10.1111/ecog.04025>.
Collection of tools to make R more convenient. Includes tools to
    summarize data using statistics not available with base R and manipulate
    objects for analyses.
R functions for "The Basics of Item Response Theory Using R" by Frank B. Baker and Seock-Ho Kim (Springer, 2017, ISBN-13: 978-3-319-54204-1) including iccplot(), icccal(), icc(), iccfit(), groupinv(), tcc(), ability(), tif(), and rasch().  For example, iccplot() plots an item characteristic curve under the two-parameter logistic model.
Provides a 'ggplot2' centric approach to bivariate mapping. This is a 
    technique that maps two quantities simultaneously rather than the single value 
    that most thematic maps display. The package provides a suite of tools 
    for calculating breaks using multiple different approaches, a selection of 
    palettes appropriate for bivariate mapping and scale functions for 'ggplot2' 
    calls that adds those palettes to maps. Tools for creating bivariate legends 
    are also included.
Functions for downloading data from the Bank for
     International Settlements (BIS; <https://www.bis.org/>) in
     Basel.  Supported are only full datasets in (typically) CSV
     format.  The package is lightweight and without dependencies;
     suggested packages are used only if data is to be transformed
     into particular data structures, for instance into 'zoo'
     objects. Downloaded data can optionally be cached, to avoid
     repeated downloads of the same files.
Tools to find bad commits with git bisect. See
        https://github.com/wch/bisectr for examples and test script
        templates.
Enables the user to infer potential synthetic lethal relationships
    by analysing relationships between bimodally distributed gene pairs in big
    gene expression datasets.  Enables the user to visualise these candidate
    synthetic lethal relationships.
A collection of functions to test spatial autocorrelation between variables, including Moran I, Geary C and Getis G together with scatter plots, functions for mapping and identifying clusters and outliers, functions associated with the moments of the previous statistics that will allow testing whether there is bivariate spatial autocorrelation, and a function that allows identifying (visualizing neighbours) on the map, the neighbors of any region once the scheme of the spatial weights matrix has been established.
Implementation of the 'bisque' strategy for approximate Bayesian posterior inference.  See Hewitt and Hoeting (2019) <arXiv:1904.07270> for complete details.  'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models.  The resulting approximations are computationally efficient for many hierarchical Bayesian models.  The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation.
Provides tools to accurately estimate cell type abundances 
    from heterogeneous bulk expression. A reference-based method utilizes
    single-cell information to generate a signature matrix and transformation
    of bulk expression for accurate regression based estimates. A marker-based
    method utilizes known cell-specific marker genes to measure relative
    abundances across samples.
    For more details, see Jew and Alvarez et al (2019) <doi:10.1101/669911>.
Bisulfite-treated RNA non-conversion in a set of samples is
  analysed as follows : each sample's non-conversion distribution is
  identified to a Poisson distribution. P-values adjusted for multiple
  testing are calculated in each sample. Combined non-conversion P-values
  and standard errors are calculated on the intersection of the set of
  samples. For further details, see C Legrand, F Tuorto, M Hartmann, 
  R Liebers, D Jakob, M Helm and F Lyko (2017) <doi:10.1101/gr.210666.116>.
Estimates cumulative history for time-series for continuously
    viewed bistable perceptual rivalry displays. Computes cumulative history
    via a homogeneous first order differential process. I.e., it assumes
    exponential growth/decay of the history as a function time and perceptually
    dominant state, Pastukhov & Braun (2011) <doi:10.1167/11.10.12>.
    Supports Gamma, log normal, and normal distribution families.
    Provides a method to compute history directly and example of using the
    computation on a custom Stan code.
Provided are classes for boolean and skewed boolean vectors,
    fast boolean methods, fast unique and non-unique integer sorting,
    fast set operations on sorted and unsorted sets of integers, and
    foundations for ff (range index, compression, chunked processing).
Package 'bit64' provides serializable S3 atomic 64bit (signed) integers. 
 These are useful for handling database keys and exact counting in +-2^63.
 WARNING: do not use them as replacement for 32bit integers, integer64 are not
 supported for subscripting by R-core and they have different semantics when 
 combined with double, e.g. integer64 + double => integer64. 
 Class integer64 can be used in vectors, matrices, arrays and data.frames. 
 Methods are available for coercion from and to logicals, integers, doubles, 
 characters and factors as well as many elementwise and summary functions. 
 Many fast algorithmic operations such as 'match' and 'order' support inter-
 active data exploration and manipulation and optionally leverage caching.
Contains the JIVE (joint inter and intra-specific model of variance evolution) model and other Bayesian models aimed at understanding trait evolution. The goal of the package is to join phylogenetic comparative models (PCM) that tend to integrate various type of data (individual observations, environmental data, fossil data) into a hierarchical Bayesian framework. It contains various PCMs as well as functions to join those models into a hierarchical Bayesian framework in a flexible and user friendly way. It contains various Markov chain Monte-Carlo (MCMC) algorithms, methods for model comparison and many plotting function for pre- and post-processing data visualization. Finally, this package integrates functions allowing bridges between 'R' and the 'BEAST2' implementations of PCMs. Kostikova A, Silvestro D, Pearman PB, Salamin N (2016) <doi:10.1093/sysbio/syw010>. Gaboriau T, Mendes FK, Joly S, Silvestro D, Salamin N (in prep).
A client for cryptocurrency exchange BitMEX
    <https://www.bitmex.com/> including the ability to obtain historic
    trade data and place, edit and cancel orders. BitMEX's Testnet and
    live API are both supported.
Functions for bitwise operations on integer vectors.
Provides methods for the binarization and trinarization of one-dimensional data and some visualization functions.
Provides functions for creating and modifying bitmaps with special emphasis on bitmap fonts and their glyphs.  Provides native read/write support for the 'hex' and 'yaff' bitmap font formats and if 'Python' is installed can also read/write several more bitmap font formats using an embedded version of 'monobit'.
Perform competing risks analysis under bivariate Pareto models. See Shih et al. (2019) <doi:10.1080/03610926.2018.1425450> for details.
Contains functions to plot bivariate maps. For more info, see: Hidasi-Neto, J (2015) <https://rfunctions.blogspot.com/2015/03/bivariate-maps-bivariatemap-function.html>.
Computes the joint probability mass function (pmf), the joint cumulative function (cdf), the joint survival function (sf), the correlation coefficient, the covariance, the cross-factorial moment and generate random deviates for the Basu-Dhar bivariate geometric distribution as well the joint probability mass, cumulative and survival function assuming the presence of a cure fraction given by the standard bivariate mixture cure fraction model. The package also computes the estimators based on the method of moments.
Implements Roy's bivariate geometric model (Roy (1993) <doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more.
Maximum likelihood estimation, random values generation, density computation and other functions for the bivariate Poisson distribution. References include: Kawamura K. (1984). "Direct calculation of maximum likelihood estimator for the bivariate Poisson distribution". Kodai Mathematical Journal, 7(2): 211--221. <doi:10.2996/kmj/1138036908>. Kocherlakota S. and Kocherlakota K. (1992). "Bivariate discrete distributions". CRC Press. <doi:10.1201/9781315138480>. Karlis D. and Ntzoufras I. (2003). "Analysis of sports data by using bivariate Poisson models". Journal of the Royal Statistical Society: Series D (The Statistician), 52(3): 381--393. <doi:10.1111/1467-9884.00366>.
A collection of models for bivariate alternating recurrent event data analysis. 
             Includes non-parametric and semi-parametric methods.
Assess the agreement in method comparison studies by tolerance intervals and errors-in-variables (EIV) regressions. The Ordinary Least Square regressions (OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate Least Square regressions (BLS and CBLS) can be used with unreplicated or replicated data. The BLS() and CBLS() are the two main functions to estimate a regression line, while XY.plot() and MD.plot() are the two main graphical functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or CBLS results. Four hyperbolic statistical intervals are provided: the Confidence Interval (CI), the Confidence Bands (CB), the Prediction Interval and the Generalized prediction Interval. Assuming no proportional bias, the (M,D) plot (Band-Altman plot) may be simplified by calculating univariate tolerance intervals (beta-expectation (type I) or beta-gamma content (type II)). Major updates from last version 1.0.0 are: title shortened, include the new functions BLS.fit() and CBLS.fit() as shortcut of the, respectively, functions BLS() and CBLS(). References: B.G. Francq, B. Govaerts (2016) <doi:10.1002/sim.6872>, B.G. Francq, B. Govaerts (2014) <doi:10.1016/j.chemolab.2014.03.006>, B.G. Francq, B. Govaerts (2014) <http://publications-sfds.fr/index.php/J-SFdS/article/view/262>, B.G. Francq (2013), PhD Thesis, UCLouvain, Errors-in-variables regressions to assess equivalence in method comparison studies, <https://dial.uclouvain.be/pr/boreal/object/boreal%3A135862/datastream/PDF_01/view>.
Generates bivariate residual plots with simulation polygons for any diagnostics and bivariate model from which functions to extract the desired diagnostics, simulate new data and refit the models are available.
Simulation of bivariate uniform data with a full range of correlations based on 
             two beta densities and computation of the tetrachoric correlation 
             (correlation of bivariate uniform data) from the phi coefficient 
             (correlation of bivariate binary data) and vice versa.
This is a port of the WTC MATLAB package written by Aslak Grinsted
    and the wavelet program written by Christopher Torrence and Gibert P.
    Compo. This package can be used to perform univariate and bivariate
    (cross-wavelet, wavelet coherence, wavelet clustering) analyses.
Compute multivariate location, scale, and correlation
        estimates based on Tukey's biweight M-estimator.
Business days calculations based on a list of holidays and
    nonworking weekdays. Quite useful for fixed income and derivatives pricing.
Maximum likelihood estimation of copula-based zero-inflated 
    (and non-inflated) Poisson and negative binomial count models. Supports Frank 
    and Gaussian copulas. Allows for mixed margins (e.g., one margin Poisson, the 
    other zero-inflated negative binomial), and several marginal link functions. 
    Built-in methods for publication-quality tables using 'texreg', post-estimation 
    diagnostics using 'DHARMa', and testing for marginal zero-modification
    via <doi:10.1177/0962280217749991>. For information on copula regression for count data, 
    see Genest and Nešlehová (2007) <doi:10.1017/S0515036100014963> as well as 
    Nikoloulopoulos (2013) <doi:10.1007/978-3-642-35407-6_11>. For information on zero-inflated
    count regression generally, see Lambert (1992) <https:www.jstor.org/stable/1269547?origin=crossref>. The author 
    acknowledges support by NSF DMS-1925119 and DMS-212324.
Implementation of a statistical approach 
  for estimating the joint health effects of multiple 
  concurrent exposures, as described in Bobb et al (2015) 
  <doi:10.1093/biostatistics/kxu058>.
Bayesian kernel machine regression (from the 'bkmr' package)
    is a Bayesian semi-parametric generalized linear model approach under
    identity and probit links. There are a number of functions in this
    package that extend Bayesian kernel machine regression fits to allow
    multiple-chain inference and diagnostics, which leverage functions
    from the 'future', 'rstan', and 'coda' packages.  Reference: Bobb, J.
    F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical
    software for analyzing the health effects of multiple concurrent
    exposures via Bayesian kernel machine regression. ;
    <doi:10.1186/s12940-018-0413-y>.
Facilitates scalable spatiotemporally varying coefficient
    modelling with Bayesian kernelized tensor regression.
    The important features of this package are:
    (a) Enabling local temporal and spatial modeling of the relationship between
    the response variable and covariates.
    (b) Implementing the model described by Lei et al. (2023) <doi:10.48550/arXiv.2109.00046>.
    (c) Using a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to sample from the posterior
    distribution of the model parameters.
    (d) Employing a tensor decomposition to reduce the number of estimated parameters.
    (e) Accelerating tensor operations and enabling graphics processing unit (GPU) acceleration
    with the 'torch' package.
Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.
Geographically referenced data and statistics of nighttime lights from NASA Black Marble <https://blackmarble.gsfc.nasa.gov/>.
Can be used to read and write a fwf with an accompanying 'Blaise' datamodel.
    Blaise is the software suite built by Statistics Netherlands (CBS). It is essentially a 
    way to write and collect surveys and perform statistical analysis on the data. It stores its data in 
    fixed width format with an accompanying metadata file, this is the Blaise format. The package automatically 
    interprets this metadata and reads the file into an R dataframe.
    When supplying a datamodel for writing, the dataframe will be automatically converted 
    to that format and checked for compatibility.
    Supports dataframes, tibbles and LaF objects.
    For more information about 'Blaise', see <https://blaise.com/products/general-information>.
Fast and accurate calculation of Blaker's binomial and Poisson confidence limits (and some related stuff).
Bland-Altman Plots using either base graphics or ggplot2,
    augmented with confidence intervals, with detailed return values and
    a sunflowerplot option for data with ties.
Carries out Bland Altman analyses (also known as a Tukey
    mean-difference plot) as described by JM Bland and DG Altman in
    1986 <doi:10.1016/S0140-6736(86)90837-8>. This package was created in 
    2015 as existing Bland-Altman analysis functions did not calculate 
    confidence intervals. This package was created to rectify this, 
    and create reproducible plots. This package is also available as a module
    for the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org>
    for more information).
Laplace approximations and penalized B-splines are combined
    for fast Bayesian inference in latent Gaussian models. The routines can be
    used to fit survival models, especially proportional hazards and promotion 
    time cure models (Gressani, O. and Lambert, P. (2018) 
    <doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also
    be implemented for inference in (generalized) additive models
    (Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>).
    See the associated website for more information and examples.
Implementation of an efficient BLAST-like sequence
             comparison algorithm, written in 'C++11' and using
             native R datatypes. Blaster is based on 'nsearch' -
             Schmid et al (2018) <doi:10.1101/399782>.
Compose and send out responsive HTML email messages that render
    perfectly across a range of email clients and device sizes. Helper functions
    let the user insert embedded images, web link buttons, and 'ggplot2' plot
    objects into the message body. Messages can be sent through an 'SMTP'
    server, through the 'RStudio Connect' service, or through the 'Mailgun' API
    service <https://www.mailgun.com/>.
Estimation of latent variable models using Bayesian methods. Currently estimates the loglinear cognitive diagnosis model of Henson, Templin, and Willse (2009) <doi:10.1007/s11336-008-9089-5>.
A wrapper around the 'Blat' command line SMTP mailer for Windows.
   'Blat' is public domain software, but be sure to read the license before use.
    It can be found at the Blat website http://www.blat.net.
Fit a variety of Bayesian latent variable models, including confirmatory
   factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) <doi:10.18637/jss.v085.i04>; Merkle et al. (2021) <doi:10.18637/jss.v100.i06>.
An implementation of the Black-Litterman Model and Attilio
    Meucci's copula opinion pooling framework as described in
    Meucci, Attilio (2005) <doi:10.2139/ssrn.848407>,
    Meucci, Attilio (2006) <doi:10.2139/ssrn.872577> and
    Meucci, Attilio (2008) <doi:10.2139/ssrn.1117574>.
Tools for assessing exotic species' contributions to
        landscape homogeneity using average pairwise Jaccard similarity
        and an analytical approximation derived in Harris et al. (2011,
        "Occupancy is nine-tenths of the law," The American
        Naturalist). Also includes a randomization method for assessing
        sources of model error.
Performs a joint analysis of experiments with mixtures and random effects, taking on a process variable represented by a covariable.
Computation of key characteristics and plots for blinded sample size recalculation.
   Continuous as well as binary endpoints are supported in superiority and non-inferiority trials.
   See Baumann, Pilz, Kieser (2022) <doi:10.32614/RJ-2022-001> for a detailed description.
   The implemented methods include the approaches by
   Lu, K. (2019) <doi:10.1002/pst.1737>,
   Kieser, M. and Friede, T. (2000) <doi:10.1002/(SICI)1097-0258(20000415)19:7%3C901::AID-SIM405%3E3.0.CO;2-L>,
   Friede, T. and Kieser, M. (2004) <doi:10.1002/pst.140>, 
   Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) <doi:10.1002/bimj.200610373>, and
   Friede, T. and Kieser, M. (2011) <doi:10.3414/ME09-01-0063>.
Randomly reassigns the group identifications to one of the variables of the 
   database, say Treatment, and randomly reassigns the observation numbers of the dataset. 
   Reorders the observations according to these new numbers. Centers each group of Treatment
   at the grand mean in order to further mask the treatment. An unmasking function is 
   provided so that the user can identify the potential outliers in terms of their original 
   values when blinding is no longer needed. It is suggested that a forward search procedure 
   be performed on the masked data. Details of some forward search functions may be found in
   <https://CRAN.R-project.org/package=forsearch>.
An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.
A method for the Bayesian functional linear regression model (scalar-on-function),
  including two estimators of the coefficient function and an estimator of its support.
  A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., 
  Baragatti M., Pudlo P. (2019) <doi:10.1214/18-BA1095>.
Implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data.
Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013) <doi:10.1007/s11336-013-9328-2>. Extends package 'lme4' (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).
Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.
Posterior distribution in the Black-Litterman model is computed from a prior distribution given in the form of a time series of asset returns and a continuous distribution of views provided by the user as an external function.
R's raw vector is useful for storing a single binary object.
    What if you want to put a vector of them in a data frame? The 'blob'
    package provides the blob object, a list of raw vectors, suitable for
    use as a column in data frame.
Simultaneous clustering of rows and columns, usually designated by
    biclustering, co-clustering or block clustering, is an important technique
    in two way data analysis. It consists of estimating a mixture model which
    takes into account the block clustering problem on both the individual and
    variables sets. The 'blockcluster' package provides a bridge between the C++
    core library build on top of the 'STK++' library, and the R statistical
    computing environment. This package allows to co-cluster binary
    <doi:10.1016/j.csda.2007.09.007>, contingency <doi:10.1080/03610920903140197>,
    continuous <doi:10.1007/s11634-013-0161-3> and categorical data-sets
    <doi:10.1007/s11222-014-9472-2>. It also provides utility functions to
    visualize the results. This package may be useful for various applications
    in fields of Data mining, Information retrieval, Biology, computer vision
    and many more. More information about the project and comprehensive tutorial
    can be found on the link mentioned in URL.
Computation of large covariance matrices having a block structure up to a permutation of their columns and rows 
    from a small number of samples with respect to the dimension of the matrix.
 The method is described in the paper Perrot-Dockès et al. (2019) <arXiv:1806.10093>.
Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.
A random forest variant 'block forest' ('BlockForest') tailored to the 
  prediction of binary, survival and continuous outcomes using block-structured 
  covariate data, for example, clinical covariates plus measurements of a certain 
  omics data type or multi-omics data, that is, data for which measurements of 
  different types of omics data and/or clinical data for each patient exist. Examples 
  of different omics data types include gene expression measurements, mutation data
  and copy number variation measurements.
  Block forest are presented in Hornung & Wright (2019). The package includes four
  other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel', 
  'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2019), 
  but performed worse than block forest in their comparison study based on 20 real 
  multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest') 
  in applications. The other random forest variants can, however, be consulted for 
  academic purposes, for example, in the context of further methodological 
  developments. 
  Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. <doi:10.1186/s12859-019-2942-y>.
A set of functions to select the optimal block-length for a 
    dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing
    (1995) <doi:10.1093/biomet/82.3.561> cross-validation method and the 
    Politis and White (2004) <doi:10.1081/ETC-120028836> Spectral Density 
    Plug-in method, including the Patton, Politis, and White (2009)
    <doi:10.1080/07474930802459016> correction with a corresponding set of S3
    plot methods.
Some elementary matrix algebra tools are implemented to manage
    block matrices or partitioned matrix, i.e. "matrix of matrices"
    (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here
    defined as a new S3 object. In this package, some methods for "matrix"
    object are rewritten for "blockmatrix" object. New methods are implemented.
    This package was created to solve equation systems with block matrices for
    the analysis of environmental vector time series .
    Bugs/comments/questions/collaboration of any kind are warmly welcomed.
Model selection method with multiple block-wise imputation for block-wise missing data; see Xue, F., and Qu, A. (2021) <doi:10.1080/01621459.2020.1751176>.
This is primarily meant as an implementation of generalized blockmodeling for valued networks.
 In addition, measures of similarity or dissimilarity based on structural equivalence and 
 regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted:
 Žiberna (2007)<doi:10.1016/j.socnet.2006.04.002>, Žiberna (2008)<doi:10.1080/00222500701790207>,
 Žiberna (2014)<doi:10.1016/j.socnet.2014.04.002>.
This app provides some useful tools for Offering an accessible GUI for generalised blockmodeling of single-relation, one-mode networks. The user can execute blockmodeling without having to write a line code by using the app's visual helps. Moreover, there are several ways to visualisations networks and their partitions. Finally, the results can be exported as if they were produced by writing code. The development of this package is financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).
Latent and Stochastic Block Model estimation by a Variational EM algorithm.
             Various probability distribution are provided (Bernoulli,
             Poisson...), with or without covariates.
Create randomizations for block random clinical trials.  Can also produce a pdf file of randomization cards.
Constructs treatment and block designs for linear treatment models
  with crossed or nested block factors. The treatment design can be any feasible 
  linear model and the block design can be any feasible combination of crossed or 
  nested block factors. The block design is a sum of one or more block factors
  and the block design is optimized sequentially with the levels of each successive
  block factor optimized conditional on all previously optimized block factors. 
  D-optimality is used throughout except for square or rectangular lattice block designs 
  which are constructed algebraically using mutually orthogonal Latin squares.
  Crossed block designs with interaction effects are optimized using a weighting scheme
  which allows for differential weighting of first and second-order block effects. 
  Outputs include a table showing the allocation of treatments to blocks and tables showing
  the achieved D-efficiency factors for each block and treatment design.  
  Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. 
  JABES 25, 500–522 (2020) <doi:10.1007/s13253-020-00416-0>.
Blocks units into experimental blocks, with one unit per treatment condition, by creating a measure of multivariate distance between all possible pairs of units.  Maximum, minimum, or an allowable range of differences between units on one variable can be set.  Randomly assign units to treatment conditions.  Diagnose potential interference between units assigned to different treatment conditions.  Write outputs to .tex and .csv files.
Functions to combine data on voting blocs' 
    size, turnout, and vote choice to estimate each bloc's
    vote contributions to the Democratic and Republican parties.
    The package also includes functions for uncertainty estimation and
    plotting. Users may define voting blocs along a discrete or continuous variable.
    The package implements methods described in Grimmer, Marble, and Tanigawa-Lau (2023) <doi:10.31235/osf.io/c9fkg>.
Write blog posts and web pages in R Markdown. This package
    supports the static site generator 'Hugo' (<https://gohugo.io>) best,
    and it also supports 'Jekyll' (<https://jekyllrb.com>) and 'Hexo'
    (<https://hexo.io>).
It includes estimating the area under the concentrations
        versus time curve (AUC) and its standard error for data with
        Below the Limit of Quantification (BLOQ) observations. Two 
        approaches are implemented: direct estimation using censored maximum 
        likelihood, also by first imputing the BLOQ's 
        using various methods, then compute AUC and its standard 
        error using imputed data. Technical details can found in 
        Barnett, Helen Yvette, Helena Geys, Tom Jacobs, and Thomas Jaki. 
		"Methods for Non-Compartmental Pharmacokinetic Analysis With Observations 
		Below the Limit of Quantification." Statistics in Biopharmaceutical 
		Research (2020): 1-12.
        (available online: 
        <https://www.tandfonline.com/doi/full/10.1080/19466315.2019.1701546>).
Tools designed to make it easier for beginner and intermediate users to build and validate 
    binary logistic regression models. Includes bivariate analysis, comprehensive regression output, 
    model fit statistics, variable selection procedures, model validation techniques and a 'shiny' 
    app for interactive model building.
Provides the estimation algorithm to perform the demand estimation described in Berry, Levinsohn and Pakes (1995) <DOI:10.2307/2171802> . The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines.
Design dose escalation using Bayesian logistic regression modeling in Phase I oncology trial.
Due to a limited availability of observed high-resolution precipitation records with adequate length, simulations with stochastic precipitation models are used to generate series for subsequent studies [e.g. Khaliq and Cunmae, 1996, <doi:10.1016/0022-1694(95)02894-3>, Vandenberghe et al., 2011, <doi:10.1029/2009WR008388>]. This package contains an R implementation of the original Bartlett-Lewis rectangular pulse model (BLRPM), developed by Rodriguez-Iturbe et al. (1987) <doi:10.1098/rspa.1987.0039>. It contains a function for simulating a precipitation time series based on storms and cells generated by the model with given or estimated model parameters. Additionally BLRPM parameters can be estimated from a given or simulated precipitation time series. The model simulations can be plotted in a three-layer plot including an overview of generated storms and cells by the model (which can also be plotted individually), a continuous step-function and a discrete precipitation time series at a chosen aggregation level.
An interactive document on  the topic of binary logistic regression  analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/BinaryLogisticRegressionModelling/>.
Allows users to easily visualize data from the BLS (United States of America Bureau of Labor Statistics) <https://www.bls.gov>. Currently unemployment data series U1-U6 are available. Not affiliated with the Bureau of Labor Statistics or United States Government.
Scrapes various data from <https://www.bls.gov/>. The Bureau of Labor Statistics is the statistical branch of the United States Department of Labor. The package has additional functions to help parse, analyze and visualize the data.
Provides a Bayesian latent space 
    model for complex networks, either weighted or unweighted.
    Given an observed input graph, the estimates for the latent coordinates 
    of the nodes are obtained through a Bayesian MCMC algorithm. 
    The overall likelihood of the graph depends on a fundamental probability 
    equation, which is defined so that ties are more likely to exist 
    between nodes whose latent space coordinates are close. 
    The package is mainly based on the model by Hoff, Raftery and Handcock (2002)
    <doi:10.1198/016214502388618906> and contains some extra features 
    (e.g., removal of the Procrustean step, weights implemented as 
    coefficients of the latent distances, 3D plots). 
    The original code related to the above model was retrieved from
    <https://www.stat.washington.edu/people/pdhoff/Code/hoff_raftery_handcock_2002_jasa/>.
    Users can inspect the MCMC simulation, create and customize insightful 
    graphical representations or apply clustering techniques.
Implements v2 of the B.L.S. API for requests of survey information
  and time series data through 3-tiered API that allows users to interact with
  the raw API directly, create queries through a functional interface, and
  re-shape the data structures returned to fit common uses. The API definition 
  is located at: <https://www.bls.gov/developers/api_signature_v2.htm>.
Fits latent threshold model for simulated data
    and describes how to adjust model using real data. Implements algorithm
    proposed by Nakajima and West (2013) <doi:10.1080/07350015.2012.747847>. 
    This package has a function to generate data, a function to configure 
    priors and a function to fit the model. Examples may be checked inside 
    the demonstration files.
Facilitates the importation of the Boston Blue Bike trip data
    since 2015. Functions include the computation of trip distances of
    given trip data. It can also map the location of stations within a
    given radius and calculate the distance to nearby stations. Data is
    from <https://www.bluebikes.com/system-data>.
Computes Blyth-Still-Casella exact binomial confidence intervals based on a refining procedure proposed by George Casella (1986) <doi:10.2307/3314658>.
Package for Bayesian model averaging and variable selection for linear models,
        generalized linear models and survival models (cox
        regression).
An implementation of the Bayesian model averaging method 
    of Psioda and others (2019) <doi:10.1093/biostatistics/kxz014> for basket trials. 
    Contains a user-friendly wrapper for simulating basket trials under conditions 
    and analyzing them with a Bayesian model averaging approach.
Toolkit for Bayesian estimation of the dependence structure in multivariate extreme value parametric models, following Sabourin and Naveau (2014) <doi:10.1016/j.csda.2013.04.021> and Sabourin, Naveau and Fougeres (2013) <doi:10.1007/s10687-012-0163-0>.
Multivariate tool for analyzing genome-wide association
    study results in the form of univariate summary statistics. The 
    goal of 'bmass' is to comprehensively test all possible multivariate
    models given the phenotypes and datasets provided. Multivariate
    models are determined by assigning each phenotype to being either
    Unassociated (U), Directly associated (D) or Indirectly associated
    (I) with the genetic variant of interest. Test results for each model 
    are presented in the form of Bayes factors, thereby allowing direct
    comparisons between models. The underlying framework implemented
    here is based on the modeling developed in "A Unified Framework 
    for Association Analysis with Multiple Related Phenotypes",
    M. Stephens (2013) <doi:10.1371/journal.pone.0065245>.
Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models. Details about the methods used can be found in these articles. Zhang and Wang (2003) <doi:10.1007/s11336-012-9301-5>. Zhang (2014) <doi:10.3758/s13428-013-0424-0>.
Methods for mediation analysis with missing data and non-normal data are implemented. For missing data, four methods are available: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included to handle missing data. For handling non-normal data, bootstrap and two-stage robust methods can be used. Technical details of the methods can be found in Zhang and Wang (2013, <doi:10.1007/s11336-012-9301-5>), Zhang (2014, <doi:10.3758/s13428-013-0424-0>), and Yuan and Zhang (2012, <doi:10.1007/s11336-012-9282-4>).
Calculates the necessary quantities to perform Bayesian multigroup equivalence testing. 
             Currently the package includes the Bayesian models and equivalence criteria outlined in Pourmohamad and Lee (2023) 
             <doi:10.1002/sta4.645>, but more models and equivalence testing features may be added over time.
Fit Bayesian multivariate GARCH models using 'Stan' for full Bayesian inference. Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) <doi:10.1198/073500102288618487> and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) <doi:10.1017/S0266466600009063> while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) <doi:10.31234/osf.io/j57pk>. The fitted models contain 'rstan' objects and can be examined with 'rstan' functions.
Full Bayesian estimation of Multidimensional Generalized Graded Unfolding Model (MGGUM) using 'rstan' (See Stan Development Team (2020) <https://mc-stan.org/>).
    Functions are provided for estimation, result extraction, model fit statistics, and plottings.
These are miscellaneous functions for working with panel data, quantiles, and printing results.  For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance.  For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions.  Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas.
Provides statistical tools for Bayesian estimation of mixture distributions, mainly a mixture of Gamma, Normal, and t-distributions. The package is implemented based on the Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <doi:10.1080/03610918.2011.588358>.
Easy estimation of Bayesian multilevel mediation models with Stan.
Reads Windows BMP format images. Currently limited to 8 bit
    greyscale images and 24,32 bit (A)RGB images. Pure R implementation without
    external dependencies.
The Bayesian Markov renewal mixed models take sequentially observed categorical data with continuous duration times, being either state duration or inter-state duration. These models comprehensively analyze the stochastic dynamics of both state transitions and duration times under the influence of multiple exogenous factors and random individual effect. The default setting flexibly models the transition probabilities using Dirichlet mixtures and the duration times using gamma mixtures. It also provides the flexibility of modeling the categorical sequences using Bayesian Markov mixed models alone, either ignoring the duration times altogether or dividing duration time into multiples of an additional category in the sequence by a user-specific unit. The package allows extensive inference of the state transition probabilities and the duration times as well as relevant plots and graphs. It also includes a synthetic data set to demonstrate the desired format of input data set and the utility of various functions. Methods for Bayesian Markov renewal mixed models are as described in: Abhra Sarkar et al., (2018) <doi:10.1080/01621459.2018.1423986> and Yutong Wu et al., (2022) <doi:10.1093/biostatistics/kxac050>.
Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods.
Analyse single case analyses against a control group.
    Its purpose is to provide a flexible, with good power and
    low first type error
    approach that can manage at the same time controls' and patient's data.
    The use of Bayesian statistics allows to test both the alternative and
    null hypothesis.
    Scandola, M., & Romano, D. (2020, August 3). <doi:10.31234/osf.io/sajdq>
    Scandola, M., & Romano, D. (2021). <doi:10.1016/j.neuropsychologia.2021.107834>.
Fits, validates and compares a number of Bayesian models for
    spatial and space time point referenced and areal unit data. Model fitting
    is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer',
    'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using
    the DIC and WAIC,  and K-fold cross-validation where the user is free
    to select their own subset of data rows for validation. Sahu (2022)
    <doi:10.1201/9780429318443> describes the methods in detail.
Density, distribution, quantile function, random number generation for the BMT (Bezier-Montenegro-Torres) distribution. Torres-Jimenez C.J. and Montenegro-Diaz A.M. (2017) <arXiv:1709.05534>. Moments, descriptive measures and parameter conversion for different parameterizations of the BMT distribution. Fit of the BMT distribution to non-censored data by maximum likelihood, moment matching, quantile matching, maximum goodness-of-fit, also known as minimum distance, maximum product of spacing, also called maximum spacing, and minimum quantile distance, which can also be called maximum quantile goodness-of-fit. Fit of univariate distributions for non-censored data using maximum product of spacing estimation and minimum quantile distance estimation is also included.
Implements parameter estimation using a Bayesian approach for Multivariate Threshold Autoregressive (MTAR) models with missing data using Markov Chain Monte Carlo methods. Performs the simulation of MTAR processes (mtarsim()), estimation of matrix parameters and the threshold values (mtarns()),  identification of the autoregressive orders using Bayesian variable selection (mtarstr()), identification of the number of regimes using Metropolised Carlin and Chib (mtarnumreg()) and estimate missing data, coefficients and covariance matrices conditional on the autoregressive orders, the threshold values and the number of regimes (mtarmissing()). Calderon and Nieto (2017) <doi:10.1080/03610926.2014.990758>.
State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.
Allows access to data on BNDES disbursements and contracts since 1995. The package makes it easy to import data from the bank into R.<https://www.bndes.gov.br/SiteBNDES/bndes/bndes_en>.
Functions to implement a Hwang(2021) <doi:10.2139/ssrn.3866876> estimator, which bounds an omitted variable bias using auxiliary data.
Bayesian network structure learning, parameter learning and inference.
  This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC,
  Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu
  Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete,
  Gaussian and conditional Gaussian networks, along with many score functions and
  conditional independence tests.
  The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented.
  Some utility functions (model comparison and manipulation, random data generation, arc
  orientation testing, simple and advanced plots) are included, as well as support for
  parameter estimation (maximum likelihood and Bayesian) and inference, conditional
  probability queries, cross-validation, bootstrap and model averaging.
  Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.
Network meta-analyses using Bayesian framework following Dias et al. (2013) <DOI:10.1177/0272989X12458724>. Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results.
An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. References: H. Chan, A. Darwiche (2002) <doi:10.1613/jair.967>; C. Goergen, M. Leonelli (2020) <ArXiv:1809.10794>; M. Leonelli, R. Ramanathan, R.L. Wilkerson (2021) <ArXiv:2107.11785>.
Implements a bootstrap aggregated (bagged) version of
    the k-nearest neighbors survival probability prediction method (Lowsky et
    al. 2013). In addition to the bootstrapping of training samples, the
    features can be subsampled in each baselearner to break the correlation
    between them. The Rcpp package is used to speed up the computation.
This project aims to enable the method of Path Analysis to infer causalities 
             from data. For this we propose a hybrid approach, which uses Bayesian network 
             structure learning algorithms from data to create the input file for creation of a 
             PA model. The process is performed in a semi-automatic way by our intermediate 
             algorithm, allowing novice researchers to create and evaluate their own PA models
             from a data set. The references used for this project are: 
             Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. <doi:10.1017/S0269888910000275>. 
             Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. <doi:10.1007/978-1-4614-6446-4>.
             Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. <doi:10.1201/b17065>. 
             Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. <doi:10.18637/jss.v048.i02>.
Bayesian nonparametric density estimation modeling mixtures by a Ferguson-Klass type algorithm for posterior normalized random measures.
Functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data. See Corradin et al. (2021) <doi:10.18637/jss.v100.i15> for more details.
The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate subpopulations from which n individuals draw their alleles dictated by their individual-specific admixture proportions.  The BN-PSD model additionally imposes the Balding-Nichols (BN) allele frequency model to the intermediate populations, which therefore evolved independently from a common ancestral population T with subpopulation-specific FST (Wright's fixation index) parameters.  The BN-PSD model can be used to yield complex population structures.  This simulation approach is now extended to subpopulations related by a tree.  Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.
Performs the algorithm for time series clustering described in Nieto-Barajas and Contreras-Cristan (2014).
Maleknia et al. (2020) <doi:10.1101/2020.01.13.905448>. A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisher’s Exact Test on significant parameters.
From a given data frame, this package learns its Bayesian network structure based on a selected score.
MCMC algorithms & processing functions for: 1. single response multiple regression, see Papageorgiou, G. (2018) <doi: 10.32614/RJ-2018-069>, 2. multivariate response multiple regression, with nonparametric models for the means, the variances and the correlation matrix, with variable selection, see Papageorgiou, G. and Marshall, B. C. (2020) <doi: 10.1080/10618600.2020.1739534>, 3. joint mean-covariance models for multivariate responses, see Papageorgiou, G. (2022) <doi: 10.1002/sim.9376>, and 4.Dirichlet process mixtures, see Papageorgiou, G. (2019) <doi: 10.1111/anzs.12273>.
Bayesian Network Structure Learning from Data with Missing Values.
    The package implements the Silander-Myllymaki complete search,
    the Max-Min Parents-and-Children, the Hill-Climbing, the
    Max-Min Hill-climbing heuristic searches, and the Structural
    Expectation-Maximization algorithm. Available scoring functions are
    BDeu, AIC, BIC. The package also implements methods for generating and using
    bootstrap samples, imputed data, inference.
Bayesian networks provide an intuitive framework for probabilistic reasoning 
             and its graphical nature can be interpreted quite clearly. Graph based methods 
             of machine learning are becoming more popular because they offer a richer model 
             of knowledge that can be understood by a human in a graphical format. The 'bnviewer' 
             is an R Package that allows the interactive visualization of Bayesian Networks. 
             The aim of this package is to improve the Bayesian Networks visualization over 
             the basic and static views offered by existing packages.
A menu-driven program and library of functions for carrying out
    convergence diagnostics and statistical and graphical analysis of Markov
    chain Monte Carlo sampling output.
Tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.
This data package contains a subset of the Bodenmiller et al, Nat Biotech 2012 dataset for testing single cell, high dimensional analysis and visualization methods.
A gradient boosting-based algorithm by incorporating a diversity term to guide the gradient boosting iterations, see Bourel, Cugliari, Goude, Poggi (2021) <https://hal.archives-ouvertes.fr/hal-03041309/>.
A wrapper to allow users to download Bus Open Data Service 'BODS' transport information from the API (<https://www.bus-data.dft.gov.uk/>).
    This includes timetable and fare metadata (including links for full datasets), timetable data at line level,
    and real-time location data.
Skinfold measurements is one of the most popular and practical methods for estimating percent body fat. Body composition is a term that describes the relative proportions of fat, bone, and muscle mass in the human body. Following the collection of skinfold measurements, regression analysis (a statistical procedure used to predict a dependent variable based on one or more independent or predictor variables) is used to estimate total percent body fat in humans. <doi:10.4324/9780203868744>.
Real-time quantitative polymerase chain reaction (qPCR) data
    sets by Boggy et al. (2008) <doi:10.1371/journal.pone.0012355>. This package
    provides a dilution series for one PCR target: a random sequence that
    minimizes secondary structure and off-target primer binding. The data set
    is a six-point, ten-fold dilution series.
    For each concentration there are two replicates. Each amplification curve
    is 40 cycles long. Original raw data file:
    <https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0012355.s004>.
Generic Extraction of main text content from HTML files; removal
    of ads, sidebars and headers using the boilerpipe 
    <https://github.com/kohlschutter/boilerpipe> Java library. The
    extraction heuristics from boilerpipe show a robust performance for a wide
    range of web site templates.
The Bayesian optimal interval (BOIN) design is a novel phase I
    clinical trial design for finding the maximum tolerated dose (MTD). It can be
    used to design both single-agent and drug-combination trials. The BOIN design
    is motivated by the top priority and concern of clinicians when testing a new
    drug, which is to effectively treat patients and minimize the chance of exposing
    them to subtherapeutic or overly toxic doses. The prominent advantage of the
    BOIN design is that it achieves simplicity and superior performance at the same
    time. The BOIN design is algorithm-based and can be implemented in a simple
    way similar to the traditional 3+3 design. The BOIN design yields an average
    performance that is comparable to that of the continual reassessment method
    (CRM, one of the best model-based designs) in terms of selecting the MTD, but
    has a substantially lower risk of assigning patients to subtherapeutic or overly
    toxic doses. For tutorial, please check Yan et al. (2020) <doi:10.18637/jss.v094.i13>.
Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). 'boinet' is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application.
Perform seasonal adjustment of weekly data. The package offers a user-friendly interface for computing seasonally adjusted estimates of weekly data and also includes diagnostic tools to assess the quality of the adjustments. Furthermore, it incorporates tools uniquely tailored to the specific characteristics of Israeli data. The method is described in more detail in Ginker (2023) <DOI:10.13140/RG.2.2.12221.44000>.
Provides an interface to Bank of Japan <https://www.boj.or.jp>
  statistics.
Implements the bolasso algorithm for consistent variable selection 
    and estimation accuracy. Includes support for many parallel backends via the 
    future package. For details see: Bach (2008),
    'Bolasso: model consistent Lasso estimation through the bootstrap', 
    <arXiv:0804.1302>.
A programmatic interface to the Web Service methods provided by
    Bold Systems (<http://www.boldsystems.org/>) for genetic 'barcode' data.
    Functions include methods for searching by sequences by taxonomic names,
    ids, collectors, and institutions; as well as a function for searching
    for specimens, and downloading trace files.
Provides data import and offers 3 daily snapshot functions from securities of varying prices traded on the Bolivian Securities Exchange, website <https://www.bbv.com.bo/>. The snapshots include a detailed list, scatter plot correlation, and descriptive statistics table for the securities.
A set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.
A set of R functions and data sets for the book "Understanding Computational Bayesian Statistics." This book was written by Bill (WM) Bolstad and published in 2009 by John Wiley & Sons (ISBN 978-0470046098).
Provides probability computation, data generation,
    and model estimation for fully-visible Boltzmann machines. It follows the methods
    described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> 
    and Nguyen and Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>.
Bond Pricing and Fixed-Income Valuation of Selected Securities included here serve as a quick reference of Quantitative Methods for undergraduate courses on Fixed-Income and CFA Level I Readings on Fixed-Income Valuation, Risk and Return.
    CFA Institute ("CFA Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 107-151, pp. 237-299)", 2019, ISBN: 9781119593577).
    Barbara S. Petitt ("Fixed Income Analysis", 2019, ISBN: 9781119628132).
    Frank J. Fabozzi ("Handbook of Finance: Financial Markets and Instruments", 2008, ISBN: 9780470078143).
    Frank J. Fabozzi ("Fixed Income Analysis", 2007, ISBN: 9780470052211).
Analysis of large datasets of fixed coupon bonds, allowing for irregular first and last coupon periods and various day count conventions. With this package you can compute the yield to maturity, the modified and MacAulay durations and the convexity of fixed-rate bonds. It provides the function AnnivDates, which can be used to evaluate the quality of the data and return time-invariant properties and temporal structure of a bond.
Bone Profiler is a scientific method and a software used to model 
    bone section for paleontological and ecological studies. See Girondot and Laurin 
    (2003) <https://www.researchgate.net/publication/280021178_Bone_profiler_A_tool_to_quantify_model_and_statistically_compare_bone-section_compactness_profiles>
    and Gônet, Laurin and Girondot (2022) <https://palaeo-electronica.org/content/2022/3590-bone-section-compactness-model>.
An improved multiple testing procedure for controlling false discovery rates which is developed based on the Bonferroni procedure with integrated estimates from the Benjamini-Hochberg procedure and the Storey's q-value procedure. It controls false discovery rates through controlling the expected number of false discoveries.
Retrieve and import data from the INKAR database (Indikatoren und Karten zur Raum- und Stadtentwicklung Datenbank, <https://www.inkar.de>) of the Federal Office for Building and Regional Planning (BBSR) in Bonn using their JSON API.
Bindings for additional tree-based model engines for use with
    the 'parsnip' package. Models include gradient boosted decision trees
    with 'LightGBM' (Ke et al, 2017.) and
    conditional inference trees and conditional random forests with
    'partykit' (Hothorn and Zeileis, 2015. and
    Hothorn et al, 2006. <doi:10.1198/106186006X133933>).
Make some distributions from the 'C++' library 'Boost'
    available in 'R'. In addition, the normal-inverse Gaussian distribution
    and the generalized inverse Gaussian distribution are provided. The 
    distributions are represented by 'R6' classes. The method to sample from 
    the generalized inverse Gaussian distribution is the one given in 
    "Random variate generation for the generalized inverse Gaussian 
    distribution" Luc Devroye (2012) <doi:10.1007/s11222-012-9367-z>.
Output formats and utilities for authoring books and technical documents with R Markdown.
Tools for optimal and approximate state estimation as well as
    network inference of Partially-Observed Boolean Dynamical Systems.
Functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks <doi:10.1093/bioinformatics/btq124>.
A C++ library for Bayesian modeling, with an emphasis on Markov
   chain Monte Carlo.  Although boom contains a few R utilities (mainly plotting
   functions), its primary purpose is to install the BOOM C++ library on your
   system so that other packages can link against it.
Spike and slab regression with a variety of residual error
  distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a
  few others.  Spike and slab regression is Bayesian regression with prior
  distributions containing a point mass at zero.  The posterior updates the
  amount of mass on this point, leading to a posterior distribution that is
  actually sparse, in the sense that if you sample from it many coefficients are
  actually zeros.  Sampling from this posterior distribution is an elegant way
  to handle Bayesian variable selection and model averaging.  See
  <DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.
Includes functions to estimate production frontiers 
    and make ideal output predictions in the Data Envelopment Analysis (DEA) 
    context using both standard models from DEA and Free Disposal Hull (FDH)
    and boosting techniques. In particular, EATBoosting (Guillen et al., 2023 
    <doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package 
    includes code for estimating several technical efficiency measures using 
    different models such as the input and output-oriented radial measures, the
    input and output-oriented Russell measures, the Directional Distance 
    Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based 
    Measure (SBM).
Jointly models the multivariate longitudinal responses and multiple covariates and time using gradient boosting approach.
Implements Friedman's gradient descent boosting algorithm for modeling longitudinal response using multivariate tree base learners. Longitudinal response could be continuous, binary, nominal or ordinal.  A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter. Although the package is design for longitudinal data, it can handle cross-sectional data as well. Implementation details are provided in Pande et al. (2017), Mach Learn <DOI:10.1007/s10994-016-5597-1>.
Boosting Regression Quantiles is a component-wise boosting algorithm, 
             that embeds all boosting steps in the well-established framework 
             of quantile regression. It is initialized with the corresponding quantile, 
             uses a quantile-specific learning rate, and uses quantile regression as its base learner.
             The package implements this algorithm and allows cross-validation and stability selection.
Functions and datasets for bootstrapping from the
  book "Bootstrap Methods and Their Application" by A. C. Davison and 
  D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Implements a bootstrap-based heterogeneity test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (or) in meta-analysis studies. Depending on the presence of moderators, this Monte Carlo based test can be implemented in the random- or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihoods, so installation of R package 'metafor' is required. This approach refers to the studies of Anscombe (1956) <doi:10.2307/2332926>, Haldane (1940) <doi:10.2307/2332614>, Hedges (1981) <doi:10.3102/10769986006002107>, Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) <doi:10.1002/14651858.CD000146.pub2>, Viechtbauer (2010) <doi:10.18637/jss.v036.i03>, and Zuckerman (1994, ISBN:978-0521432009).
Computation of bootstrap p-values through inversion of confidence intervals, including convenience functions for regression models.
Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016)<doi:10.1142/9789814749411_0007>. Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) <doi:10.1002/sam.11495>.
Propagate uncertainty from several estimates when combining these estimates via a function.
    This is done by using the parametric bootstrap to simulate values from the distribution of each estimate to build up an empirical distribution of the combined parameter.
    Finally either the percentile method is used or the highest density interval is chosen to derive a confidence interval for the combined parameter with the desired coverage.
    Gaussian copulas are used for when parameters are assumed to be dependent / correlated.
    References: Davison and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest density interval, Stockdale et al. (2020)<doi:10.1016/j.jhep.2020.04.008> for an example of combining conditional prevalences.
The bootstrap ARDL tests for cointegration is the main functionality of this package. It also acts as a wrapper of the most commond ARDL testing procedures for cointegration: the bound tests of Pesaran, Shin and Smith (PSS; 2001 - <doi:10.1002/jae.616>) and the asymptotic test on the independent variables of Sam, McNown and Goh (SMG: 2019 - <doi:10.1016/j.econmod.2018.11.001>). Bootstrap and bound tests are performed under both the conditional and unconditional ARDL models.
Calculate robust measures of effect sizes using the bootstrap.
Compare dissolution profiles with confidence interval of similarity
  factor f2 using bootstrap methodology as described in the literature, such as 
  Efron and Tibshirani (1993, ISBN:9780412042317), Davison and Hinkley (1997,
  ISBN:9780521573917), and Shah et al. (1998) <doi:10.1023/A:1011976615750>. 
  The package can also be used to simulate dissolution profiles based on 
  mathematical modelling and multivariate normal distribution.
Bootstrap based goodness-of-fit tests. It allows
    to perform rigorous statistical tests to check if a chosen
    model family is correct based on the marked empirical
    process. The implemented algorithms are described in
    (Dikta and Scheer (2021) <doi:10.1007/978-3-030-73480-0>)
    and can be applied
    to generalized linear models without any further implementation
    effort. As far as certain linearity conditions are fulfilled
    the resampling scheme are also applicable beyond generalized
    linear models. This is reflected in the software architecture
    which allows to reuse the resampling scheme by implementing
    only certain interfaces for models that are not supported
    natively by the package.
Bootstraps and imputes incomplete datasets. Then performs inference on estimates obtained from analysing the imputed datasets as proposed by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>.
Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) <doi:10.1177/0962280215592907>.  It is particularly useful when the sensitivity or specificity in the sample is 100%.  Note that this does not perform the test on nested models--for that, see 'epicalc::lrtest'.
Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).
Bootstrap methods to assess accuracy and stability of estimated network structures
              and centrality indices <doi:10.3758/s13428-017-0862-1>. Allows for flexible 
              specification of any undirected network estimation procedure in R, and offers 
              default sets for various estimation routines.
Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', accepted.).
Contains functions for bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series.
We provide a framework for testing the probability of ruin in the classical (compound Poisson) risk process. It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality.
Model selection by bootstrapping the stepAIC() procedure.
Software (bootstrap, cross-validation, jackknife) and data
        for the book "An Introduction to the Bootstrap" by B. Efron and
        R. Tibshirani, 1993, Chapman and Hall. This package is
        primarily provided for projects already based on it, and for
        support of the book. New projects should preferentially use the
        recommended package "boot".
Finite Population bootstrap algorithms to estimate the variance
    of the Horvitz-Thompson estimator for single-stage sampling. 
    For a survey of bootstrap methods for finite populations, see Mashreghi et Al. (2016) <doi:10.1214/16-SS113>.
Identifies genome-related molecular traits with significant 
  evidence of genetic regulation and performs a bootstrap procedure to 
  correct estimated effect sizes for over-estimation present in cis-QTL
  mapping studies (The "Winner's Curse"), described in Huang QQ *et al.* 
  2018 <doi: 10.1093/nar/gky780>.
Implements fast, exact bootstrap Principal Component Analysis and
    Singular Value Decompositions for high dimensional data, as described in
    <doi:10.1080/01621459.2015.1062383> (see also <arXiv:1405.0922> ). For data matrices that are too large to operate
    on in memory, users can input objects with class 'ff' (see the 'ff'
    package), where the actual data is stored on disk. In response, this
    package will implement a block matrix algebra procedure for calculating the
    principal components (PCs) and bootstrap PCs. Depending on options set by
    the user, the 'parallel' package can be used to parallelize the calculation of
    the bootstrap PCs.
Set of functions to perform various bootstrap unit root tests for both individual time series
  (including augmented Dickey-Fuller test and union tests), multiple time series and panel data; see
  Smeekes and Wilms (2023) <doi:10.18637/jss.v106.i12>,
  Palm, Smeekes and Urbain (2008) <doi:10.1111/j.1467-9892.2007.00565.x>,
  Palm, Smeekes and Urbain (2011) <doi:10.1016/j.jeconom.2010.11.010>, 
  Moon and Perron (2012) <doi:10.1016/j.jeconom.2012.01.008>, 
  Smeekes and Taylor (2012) <doi:10.1017/S0266466611000387> and 
  Smeekes (2015) <doi:10.1111/jtsa.12110> for key references.
The card game War is simple in its rules but can be lengthy. In
    another domain, the nonparametric bootstrap test with pooled resampling
    (nbpr) methods, as outlined in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>,
    is optimal for comparing paired or unpaired means in non-normal data,
    especially for small sample size studies. However, many researchers are
    unfamiliar with these methods. The 'bootwar' package bridges this gap by
    enabling users to grasp the concepts of nbpr via Boot War, a variation of the
    card game War designed for small samples. The package provides functions like
    score_keeper() and play_round() to streamline gameplay and scoring. Once a
    predetermined number of rounds concludes, users can employ the analyze_game()
    function to derive game results. This function leverages the 'npboottprm'
    package's nonparboot() to report nbpr results and, for comparative analysis,
    also reports results from the 'stats' package's t.test() function. Additionally,
    'bootwar' features an interactive 'shiny' web application, bootwar(). This
    offers a user-centric interface to experience Boot War, enhancing understanding
    of nbpr methods across various distributions, sample sizes, number of bootstrap
    resamples, and confidence intervals.
Provides significance tests for second-order stationarity
	for time series using bootstrap wavelet packet tests.
	Provides functionality to visualize the time series with
	the results of the hypothesis tests superimposed.
	The methodology is described in Cardinali, A and Nason, G P (2016)
	"Practical powerful wavelet packet tests for second-order stationarity."
	Applied and Computational Harmonic Analysis, 44, 558-585
	<doi:10.1016/j.acha.2016.06.006>.
Computes Bayesian A- and D-optimal block designs under the linear mixed effects model settings 
	using block/array exchange algorithm of Debusho, Gemechu and Haines (2018) <doi:10.1080/03610918.2018.1429617> where the interest is in a 
	comparison of all possible elementary treatment contrasts. The package also provides an optional 
	method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly.
Transforms focal observations' data, where different types of social interactions
  can be recorded by multiple observers, into asymmetric data matrices.
  Each cell in these matrices provides counts on the number of times
  a specific type of social interaction was initiated by the row subject and
  directed to the column subject.
Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (GLMs) to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.
Estimate population average treatment effects from a primary data source 
  with borrowing from supplemental sources. Causal estimation is done with either a 
  Bayesian linear model or with Bayesian additive regression trees (BART) to adjust 
  for confounding. Borrowing is done with multisource exchangeability models (MEMs). For 
  information on BART, see Chipman, George, & McCulloch (2010) <doi:10.1214/09-AOAS285>. 
  For information on MEMs, see Kaizer, Koopmeiners, & 
  Hobbs (2018) <doi:10.1093/biostatistics/kxx031>.
An all relevant feature selection wrapper algorithm.
 It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).
A novel feature selection algorithm for linear regression called 
    BOSO (Bilevel Optimization Selector Operator). The main contribution is the 
    use a bilevel optimization problem to select the variables in the training 
    problem that minimize the error in the validation set. Preprint available: 
    [Valcarcel, L. V., San Jose-Eneriz, E., Cendoya, X., Rubio, A., Agirre, X., 
    Prosper, F., & Planes, F. J. (2020). "BOSO: a novel feature selection 
    algorithm for linear regression with high-dimensional data." bioRxiv. 
    <doi:10.1101/2020.11.18.388579>]. 
    In order to run the vignette, it is recommended to install the 'bestsubset' package,
    using the following command: devtools::install_github(repo="ryantibs/best-subset", subdir="bestsubset").
    If you do not have gurobi, run devtools::install_github(repo="lvalcarcel/best-subset", subdir="bestsubset").
    Moreover, to install cplexAPI you can check <https://github.com/lvalcarcel/cplexAPI>.
Classical Boson Sampling using the algorithm of
 Clifford and Clifford (2017) <arXiv:1706.01260>. Also provides functions for
 generating random unitary matrices, evaluation of matrix permanents (both
 real and complex) and evaluation of complex permanent minors.
Reads and plots phylogenetic placements.
The Biomarker Optimal Segmentation System R package, 'bossR', is designed for precision medicine, helping to identify individual traits using biomarkers. It focuses on determining the most effective cutoff value for a continuous biomarker, which is crucial for categorizing patients into two groups with distinctly different clinical outcomes. The package simultaneously finds the optimal cutoff from given candidate values and tests its significance. Simulation studies demonstrate that 'bossR' offers statistical power and false positive control non-inferior to the permutation approach (considered the gold standard in this field), while being hundreds of times faster.
Best Orthogonalized Subset Selection (BOSS) is a least-squares (LS) based subset selection method, that performs best subset selection upon an orthogonalized basis of ordered predictors, with the computational effort of a single ordinary LS fit. This package provides a highly optimized implementation of BOSS and estimates a heuristic degrees of freedom for BOSS, which can be plugged into an information criterion (IC) such as AICc in order to select the subset from candidates. It provides various choices of IC, including AIC, BIC, AICc, Cp and GCV. It also implements the forward stepwise selection (FS) with no additional computational cost, where the subset of FS is selected via cross-validation (CV). CV is also an option for BOSS. For details see: Tian, Hurvich and Simonoff (2021), "On the Use of Information Criteria for Subset Selection in Least Squares Regression", <arXiv:1911.10191>.
Fork-safe, raw access to the 'Amazon Web Services' ('AWS') 'SDK' via the 'boto3' 'Python' module, and convenient helper functions to query the 'Simple Storage Service' ('S3') and 'Key Management Service' ('KMS'), partial support for 'IAM', the 'Systems Manager Parameter Store' and 'Secrets Manager'.
Analysis workflow for finding geographic boundaries of ecological or landscape traits and comparing the placement of geographic boundaries of two traits. If data are trait values, trait data are transformed to boundary intensities based on approximate first derivatives across latitude and longitude. The package includes functions to create custom null models based on the input data. The boundary statistics are described in: Fortin, Drapeau, and Jacquez (1996) <doi:10.2307/3545584>.
Computes uniform bounds on the distance between
  the cumulative distribution function of a standardized sum
  of random variables and its first-order Edgeworth expansion,
  following the article Derumigny, Girard, Guyonvarch (2021)
  <arXiv:2101.05780>.
Generate ground truth cases for object localization algorithms. 
    Cycle through a list of images, select points around which to generate bounding 
    boxes and assign classifiers. Output the coordinates, and images annotated with 
    boxes and labels. For an example study that uses bounding boxes for image 
    localization and classification see Ibrahim, Badr, Abdallah, and Eissa (2012)
    "Bounding Box Object Localization Based on Image Superpixelization"
    <doi:10.1016/j.procs.2012.09.119>.
A collection of R functions were implemented
        from published and available analytic solutions for the
        One-Dimensional Boussinesq Equation (ground-water). In
        particular, the function "beq.lin()" is the analytic solution of
        the linearized form of Boussinesq Equation between two
        different head-based boundary (Dirichlet) conditions;
        "beq.song" is the non-linear power-series analytic solution of
        the motion of a wetting front over a dry bedrock (Song at al,
        2007, see complete reference on function documentation).
        Bugs/comments/questions/collaboration of any kind are warmly
        welcomed.
Computational tools for outlier detection and influence diagnostics of meta-analysis. Bootstrap distributions of the influence statistics are calculated, and the thresholds to determine outliers are explicitly provided.
Contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals. P'ng et al.; BPG: Seamless, automated and interactive visualization of scientific data; BMC Bioinformatics 2019 <doi:10.1186/s12859-019-2610-2>.
A modern module system for R. Organise code into hierarchical,
    composable, reusable modules, and use it effortlessly across projects via a
    flexible, declarative dependency loading syntax.
Box-Cox-type transformations for linear and logistic models with random effects using non-parametric profile maximum likelihood estimation, as introduced in Almohaimeed (2018) <http://etheses.dur.ac.uk/12831/> and Almohaimeed and Einbeck (2022) <doi:10.1177/1471082X20966919>. The main functions are 'optim.boxcox()' for linear models with random effects and 'boxcoxtype()' for logistic models with random effects.
Noise filter based on determining the proportion of neighboring points. A false point will be rejected if it has only few neighbors, but accepted if the proportion of neighbors in a rectangular frame is high. The size of the rectangular frame as well as the cut-off value, i.e. of a minimum proportion of neighbor-points, may be supplied or can be calculated automatically. Originally designed for the cleaning of heart rates, but suitable for filtering any slowly-changing physiological variable.For more information see Signer (2010)<doi:10.1111/j.2041-210X.2009.00010.x>.
Interactive box plot using 'plotly' for clinical trial analysis.
Following Arroyo-Maté-Roque (2006), the function calculates the distance between rows or columns of the dataset using the generalized Minkowski metric as described by Ichino-Yaguchi (1994). The distance measure gives more weight to differences between quartiles than to differences between extremes, making it less sensitive to outliers. Further,the function calculates the silhouette width (Rousseeuw 1987) for different numbers of clusters and selects the number of clusters that maximizes the average silhouette width, unless a specific number of clusters is provided by the user. The approach implemented in this package is based on the following publications: Rousseeuw (1987) <doi:10.1016/0377-0427(87)90125-7>; Ichino-Yaguchi (1994) <doi:10.1109/21.286391>; Arroyo-Maté-Roque (2006) <doi:10.1007/3-540-34416-0_7>.
Correlation chart of two set (x and y) of data.  
  Using Quartiles with boxplot style. 
  Visualize the effect of factor.
An R interface for the remote file hosting service 'Box'
    (<https://www.box.com/>). In addition to uploading and downloading files,
    this package includes functions which mirror base R operations for local
    files, (e.g. box_load(), box_save(), box_read(), box_setwd(), etc.), as well
    as 'git' style functions for entire directories (e.g. box_fetch(),
    box_push()).
A comprehensive package to aid in the analysis of blood pressure data of all forms by providing both descriptive and visualization tools for researchers.
Run basic pattern analyses on character sets, digits, or combined
    input containing both characters and numeric digits. Useful for data
    cleaning and for identifying columns containing multiple or nonstandard
    formats.
A comprehensive statistical analysis of the accuracy of blood
         pressure devices based on the method of AAMI/ANSI SP10 standards
         developed by the AAMI Sphygmomanometer Committee for indirect
         measurement of blood pressure, incorporated into IS0 81060-2. The 
         'bpAcc' package gives the exact probability 'of accepting a device D'
         derived from the join distribution of the sample standard deviation
         and a non-linear transformation of the sample mean for a specified
         sample size introduced by Chandel et al. (2023) and by the Association
         for the Advancement of Medical Instrumentation (2003, ISBN:1-57020-183-8).
Implementation of the nonparametric bounds for the average
    causal effect under an instrumental variable model by Balke and Pearl
    (Bounds on Treatment Effects from Studies with Imperfect Compliance,
    JASA, 1997, 92, 439, 1171-1176). The package can calculate bounds for
    a binary outcome, a binary treatment/phenotype, and an instrument with
    either 2 or 3 categories. The package implements bounds for situations
    where these 3 variables are measured in the same dataset (trivariate
    data) or where the outcome and instrument are measured in one study
    and the treatment/phenotype and instrument are measured in another
    study (bivariate data).
Implements biplot (2d and 3d) of multivariate data based
             on principal components analysis and diagnostic tools of the quality of the reduction.
Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data, and for medians [Fay and Brittain <DOI:10.1002/sim.6905>]. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time, and differences in medians [Fay, Proschan, and Brittain <DOI:10.1111/biom.12231>]. Basically, the package gives exact inference methods for one- and two-sample exact inferences for Kaplan-Meier curves (e.g., generalizing Fisher's exact test to allow for right censoring), which are especially important for latter parts of the survival curve, small sample sizes or heavily censored data. Includes mid-p options.
Functions for drawing boxplots for data on (the boundary of) a unit circle (i.e., circular and axial data), from Buttarazzi D., Pandolfo G., Porzio G.C. (2018) <doi:10.1111/biom.12889>.
Model-based clustering using Bayesian parsimonious Gaussian mixture models.
  MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. 
  GREEN et al. (1995) <doi:10.1093/biomet/82.4.711>.
Bayesian purity model to estimate tumor purity using methylation 
    array data (DNA methylation Infinium 450K array data) without reference samples.
Creating, rendering and writing BPMN diagrams <https://www.bpmn.org/>. Functionalities can be used to visualize and export BPMN diagrams created using the 'pm4py' and 'bupaRminer' packages. Part of the 'bupaR' ecosystem.
To visualize the execution data of the processes on 'BPMN' (Business Process Model and Notation) diagrams, using overlays, style customization and interactions, with the 'bpmn-visualization' 'TypeScript' library.
Fitting Bayesian multiple and mixed-effect regression models for 
    circular data based on the projected normal distribution. Both continuous 
    and categorical predictors can be included. Sampling from the posterior is 
    performed via an MCMC algorithm. Posterior descriptives of all parameters, 
    model fit statistics and Bayes factors for hypothesis tests for inequality 
    constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) 
    <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) 
    <doi:10.1016/j.csda.2012.07.025>.
Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio.
Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2023) <doi:10.1080/10618600.2022.2123337>.
Bayesian models to estimate causal effects of biological 
    treatments on time-to-event endpoints in clinical trials with principal
    strata defined by the occurrence of antidrug antibodies. 
    The methodology is based on Frangakis and Rubin (2002) 
    <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997)
    <doi:10.1214/aos/1034276631>, and here adapted to a specific 
    time-to-event setting.
Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures – logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models.
    Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, 11(1): 1-24 <doi: 10.1214/15-BA939>.
    Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447 <doi: 10.1016/S0167-7152(01)00124-9>.
    Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 <doi: 10.2307/1913643>.
    Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. <doi: 10.1080/01621459.1995.10476635>.
    Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. <doi: 10.1198/016214501750332848>.
QTL mapping toolkit for inbred crosses and recombinant
        inbred lines. Includes maximum likelihood and Bayesian tools.
Adjusting the bias due to residual confounding (often called
    treatment selection bias) in estimating the treatment effect in a
    proportional hazard model, as described in Williamson et al.
    (2022) <doi:10.1158/1078-0432.ccr-21-2468>.
Performs brace expansions on strings.  Made popular by Unix shells, brace expansion allows users to concisely generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string.  The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros.
The goal of this method is to identify associations between bacteria and an environmental variable in 16S or other compositional data. The environmental variable is any variable which is measure for each microbiome sample, for example, a butyrate measurement paired with every sample in the data. Microbiome data is compositional, meaning that the total abundance of each sample sums to 1, and this introduces severe statistical distortions. This method takes a Bayesian approach to correcting for these statistical distortions, in which the total abundance is treated as an unknown variable. This package runs the python implementation using reticulate.
Provides access to a range of functions for analyzing, applying and visualizing Bayesian response-adaptive trial designs for a binary endpoint. Includes the predictive probability approach and the predictive evidence value designs for binary endpoints.
Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.
Forecasters predicting the chances of a future event may disagree due to
    differing evidence or noise. To harness the collective evidence of the crowd, 
    Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions"
    <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945> proposes a Bayesian
    aggregator that is regularized by analyzing the forecasters' disagreement and ascribing 
    over-dispersion to noise. This aggregator requires no user intervention and can be computed
    efficiently even for a large numbers of predictions. The author evaluates
    the aggregator on subjective probability predictions collected during
    a four-year forecasting tournament sponsored by the US intelligence community.
    The aggregator improves the accuracy of simple averaging by around 20% and
    other state-of-the-art aggregators by 10-25%. The advantage stems almost
    exclusively from improved calibration. This aggregator -- know as "the revealed
    aggregator" -- inputs a) forecasters' probability predictions (p) of a future binary event
    and b) the forecasters' common prior (p0) of the future event. In this R-package,
    the function sample_aggregator(p,p0,...) allows the user to calculate the revealed
    aggregator. Its use is illustrated with a simple example.
Provides functions to generate, format, and style surface plots for visualizing combined action data.  Also provides functions for reporting on a BRAID analysis, including plotting curve-shifts, calculating IAE values, and producing full BRAID analysis reports.
Contains functions for evaluating, analyzing, and fitting combined action dose response surfaces with the Bivariate Response to Additive Interacting Dose (BRAID) model of combined action.
Deals with the braid groups. Includes creation of some
    specific braids, group operations, free reduction, and Bronfman
    polynomials. Braid theory has applications in fluid mechanics and
    quantum physics. The code is adapted from the 'Haskell' library
    'combinat', and is based on Birman and Brendle (2005)
    <doi:10.48550/arXiv.math/0409205>.
Blind users do not have access to the graphical output from R
    without printing the content of graphics windows to an embosser of
    some kind. This is not as immediate as is required for efficient
    access to statistical output.  The functions here are created so that
    blind people can make even better use of R. This includes the text
    descriptions of graphs, convenience functions to replace the
    functionality offered in many GUI front ends, and experimental
    functionality for optimising graphical content to prepare it for
    embossing as tactile images.
A statistical tool to inference the multi-level partial correlations based on multi-subject time series data, especially for brain functional connectivity. It combines both individual and population level inference by using  the methods of Qiu and Zhou. (2021)<DOI: 10.1080/01621459.2021.1917417> and Genovese and Wasserman. (2006)<DOI: 10.1198/016214506000000339>. It realizes two reliable estimation methods of partial correlation coefficients, using scaled lasso and lasso. It can be used to estimate individual- or population-level partial correlations, identify nonzero ones, and find out unequal partial correlation coefficients between two populations.
A set of tools for performing graph theory analysis of brain MRI
    data. It works with data from a Freesurfer analysis (cortical thickness,
    volumes, local gyrification index, surface area), diffusion tensor
    tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from
    DPABI). It contains a graphical user interface for graph visualization and
    data exploration, along with several functions for generating useful
    figures.
It is designed to calculate connection between (among) brain regions and plot connection lines. Also, the summary function is included to summarize group-level connectivity network. Kang, Jian (2016) <doi:10.1016/j.neuroimage.2016.06.042>.
This includes functions for creating 3D and 4D images using 
    'WebGL', 'rgl', and 'JavaScript' commands.  
    This package relies on the X toolkit ('XTK',
    <https://github.com/xtk/X#readme>).
Performs efficient and scalable glm best 
	subset selection using a novel implementation of a branch and bound algorithm.
	To speed up the model fitting process, a range of optimization
	methods are implemented in 'RcppArmadillo'. Parallel computation 
	is available using 'OpenMP'.
Simulation and parameter estimation of multitype Bienayme - Galton - Watson processes.
Interact with the 'Brandwatch' API <https://developers.brandwatch.com/docs>. 
  Allows you to authenticate to the API and obtain data for projects, queries, query groups tags and categories.
  Also allows you to directly obtain mentions and aggregate data for a specified query or query group.
Tests the parallel regression assumption wit the brant test by Brant (1990) <doi: 10.2307/2532457> for ordinal logit models generated with the function polr() from the package 'MASS'.
Test the robustness of a user's Qualitative Comparative Analysis
    solutions to randomness, using the bootstrapped assessment: baQCA(). This
    package also includes a function that provides recommendations for improving
    solutions to reach typical significance levels: brQCA(). Data included 
    come from McVeigh et al. (2014) <doi:10.1177/0003122414534065>.
Executes BASIC programs from the 1970s, for historical and
    educational purposes. This enables famous examples of early machine
    learning, artificial intelligence, natural language processing, cellular
    automata, and so on, to be run in their original form.
Utilities for Bratteli graphs. A tree is an example of a Bratteli
    graph. The package provides a function which generates a 'LaTeX' file
    that renders the given Bratteli graph. It also provides functions to
    compute the dimensions of the vertices, the intrinsic kernels and the
    intrinsic distances. Intrinsic kernels and distances were introduced
    by Vershik (2014) <doi:10.1007/s10958-014-1958-0>.
Performs Bayesian variable screening and selection for ultra-high dimensional linear regression models.
A compilation of functions to download and processing AWS data of INMET-Brazil, with the purpose of reference evapotranspiration (ETo) estimation. The package aims to make meteorological and agricultural data analysis more parsimonious.
Performs variable ranking based on several measures for the class of copula survival model(s) in high dimensional domain.
    The package is based on the class of copula survival model(s) implemented in the 'GJRM' package.
Fetches zonal statistics from weather indicators that were calculated for each municipality in Brazil using data from the BR-DWGD and TerraClimate projects.
    Zonal statistics such as mean, maximum, minimum, standard deviation, and sum were computed by taking into account the data cells that intersect the boundaries of each municipality and stored in Parquet files. This procedure was carried out for all Brazilian municipalities, and for all available dates, for every indicator available in the weather products (BR-DWGD and TerraClimate projects). This package queries on-line the already calculated statistics on the Parquet files and returns easy-to-use data.frames.
This is an implementation of design methods for binomial reliability demonstration tests (BRDTs) with failure count data. 
    The acceptance decision uncertainty of BRDT has been quantified and the impacts of the uncertainty on related reliability assurance activities such as reliability growth (RG) and warranty services (WS) are evaluated.
    This package is associated with the work from the published paper "Optimal Binomial Reliability Demonstration Tests Design under Acceptance Decision Uncertainty" by Suiyao Chen et al. (2020) <doi:10.1080/08982112.2020.1757703>.
A function to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models.
A simple set of wrapper functions for data.table::fread() that allows subsetting or
    filtering rows and selecting columns of table-formatted files too large for the available RAM.
    'b stands for 'big files'.
    bread makes heavy use of Unix commands like 'grep', 'sed', 'wc', 'awk' and 'cut'. They are available 
    by default in all Unix environments.
    For Windows, you need to install those commands externally in order to simulate a 
    Unix environment and make sure that the executables are in the Windows PATH variable.
    To my knowledge, the simplest ways are to install 'RTools', 'Git' or 'Cygwin'. If they have been 
    correctly installed (with the expected registry entries), they should be detected on loading
    the package and the correct directories will be added automatically to the PATH.
The goal of the package is to provide an easy-to-use method 
  for estimating degrees of relatedness (up to the second degree) 
  for extreme low-coverage data. The package also allows users to 
  quantify and visualise the level of confidence in the estimated 
  degrees of relatedness. A paper describing the method is available 
  at Rohrlach, A. B. et al (2023) <https://tinyurl.com/29t6gbbx>.
Understanding the drivers of microbial diversity is an important frontier of microbial ecology, and investigating the diversity of samples from microbial ecosystems is a common step in any microbiome analysis. 'breakaway' is the premier package for statistical analysis of microbial diversity. 'breakaway' implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) <doi:10.1111/biom.12332>, Willis et al. (2017) <doi:10.1111/rssc.12206>, and Willis (2016) <arXiv:1604.02598>, as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) <doi:10.1214/10-BA527>.
Model agnostic tool for decomposition of predictions from black boxes.
    Break Down Table shows contributions of every variable to a final prediction. 
    Break Down Plot presents variable contributions in a concise graphical way. 
    This package work for binary classifiers and general regression models.
A developing software suite for multiple change-point and change-point-type feature detection/estimation (data segmentation) in data sequences.
Implements the Cross-Entropy (CE) method, which is a model based stochastic optimization technique to estimate both the number and their corresponding locations of break-points in continuous and discrete measurements (Priyadarshana and Sofronov (2015), Priyadarshana and Sofronov (2012a), Priyadarshana and Sofronov (2012b)).
Compute Buishand Range Test, Pettit Test, SNHT, Student t-test, and Mann-Whitney Rank Test, to identify breakpoints in series. For all functions NA is allowed. Since all of the mention methods identify only one breakpoint in a series, a general function to look for N breakpoint is given. Also, the Yamamoto test for climate jump is available. Alexandersson, H. (1986) <doi:10.1002/joc.3370060607>, Buishand, T. (1982) <doi:10.1016/0022-1694(82)90066-X>, Hurtado, S. I., Zaninelli, P. G., & Agosta, E. A. (2020) <doi:10.1016/j.atmosres.2020.104955>, Mann, H. B., Whitney, D. R. (1947) <doi:10.1214/aoms/1177730491>, Pettitt, A. N. (1979) <doi:10.2307/2346729>, Ruxton, G. D., jul (2006) <doi:10.1093/beheco/ark016>, Yamamoto, R., Iwashima, T., Kazadi, S. N., & Hoshiai, M. (1985) <doi:10.2151/jmsj1965.63.6_1157>.
Reads several formats of 13C data (IRIS/Wagner,
    BreathID) and CSV.  Creates artificial sample data for testing.  Fits
    Maes/Ghoos, Bluck-Coward self-correcting formula using 'nls', 'nlme'.
    Methods to fit breath test curves with Bayesian Stan methods are
    refactored to package 'breathteststan'. For a Shiny GUI, see package
    'dmenne/breathtestshiny' on github.
Stan-based curve-fitting function
  for use with package 'breathtestcore' by the same author.
  Stan functions are refactored here for easier testing.
A collection of functions to analyse, visualize and interpret wind data
         and to calculate the potential energy production of wind turbines.
Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.
Implements a templating framework for mixing text and R code
    for report generation. brew template syntax is similar to PHP, Ruby's
    erb module, Java Server Pages, and Python's psp module.
Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'.  More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.
Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic regression).
Interface with the 'Brickset' API
    <https://brickset.com/article/52664/api-version-3-documentation> for
    getting data about LEGO sets. Data sets that
    can be used for teaching and learning without the need of a 'Brickset'
    account and API key are also included. Includes all LEGO since through
    the end of 2023.
An implementation of the bridge distribution with logit-link in
    R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate
    bridge distribution was derived as the distribution of the random intercept that
    'bridged' a marginal logistic regression and a conditional logistic regression.
    The conditional and marginal regression coefficients are a scalar multiple
    of each other. Such is not the case if the random intercept distribution was
    Gaussian.
Produce bridge hands, allowing parameters for hands to offer specific for bidding sequences.
BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells.
    This package provides a series of functions for performing quality check of your BRIC-seq data,
    calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions.
Provides functions for estimating marginal likelihoods, Bayes
    factors, posterior model probabilities, and normalizing constants in general,
    via different versions of bridge sampling (Meng & Wong, 1996, 
    <http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>).
    Gronau, Singmann, & Wagenmakers (2020) <doi:10.18637/jss.v092.i10>.
Build decision trees and random forests for classification and regression. The implementation strikes a balance between minimizing computing efforts and maximizing the expected predictive accuracy, thus scales well to large data sets. Multi-threading is available through 'OpenMP' <https://gcc.gnu.org/wiki/openmp>.
Implementation of the BRIk, FABRIk and FDEBRIk algorithms 
        to initialise k-means. These methods are intended for the 
        clustering of multivariate and functional data, respectively.
        They make use of the Modified Band Depth and bootstrap to 
        identify appropriate initial seeds for k-means, which are 
        proven to be better options than many techniques in the 
        literature. Torrente and Romo (2021) <doi:10.1007/s00357-020-09372-3>
        It makes use of the functions kma and kma.similarity, from the 
        archived package fdakma, by Alice Parodi et al.
An automated graphical exploratory data analysis (EDA) tool that introduces: 
  a.) wideplot graphics for exploring the structure of a dataset through a grid of variables 
      and graphic types. 
  b.) longplot graphics, which present the entire catalog of available graphics for representing 
      a particular variable using a grid of graphic types and variations on these types.
  c.) plotup function, which presents a particular graphic for a specific variable of a dataset. 
      The plotup() function also makes it possible to obtain the code used to generate the graphic, 
      meaning that the user can adjust its properties as needed.
  d.) matrixplot graphics that is a grid of a particular graphic showing bivariate relationships
      between all pairs of variables of a certain(s) type(s) in a multivariate data set.
Functions to handle basic input output, these functions
    always read and write UTF-8 (8-bit Unicode Transformation Format)
    files and provide more explicit control over line endings.
An interface to the 'Briq' API <https://briq.github.io>. 'Briq' is
    a tool that aims to promote employee engagement by helping employees
    recognize and reward each other. Employees can praise and thank one another
    (for achieving a company goal, for example) by giving virtual credits (known
    as 'briqs' or 'bqs') that can be redeemed for various rewards. The 'Briq'
    API lets you create, read, update and delete users, user groups,
    transactions and messages. This package provides functions that simplify
    getting the users, user groups and transactions of your organization into R.
Fits bootstrap with univariate spatial regression models using Bootstrap for Rapid Inference on Spatial Covariances (BRISC) for large datasets using nearest neighbor Gaussian processes detailed in Saha and Datta (2018) <doi:10.1002/sta4.184>.
Quantitative methods for benefit-risk analysis help to condense
    complex decisions into a univariate metric describing the overall benefit
    relative to risk.  One approach is to use the multi-criteria decision
    analysis framework (MCDA), as in Mussen, Salek, and Walker
    (2007) <doi:10.1002/pds.1435>.  Bayesian benefit-risk
    analysis incorporates uncertainty through posterior distributions which are
    inputs to the benefit-risk framework.  The brisk package provides functions
    to assist with Bayesian benefit-risk analyses, such as MCDA.
    Users input posterior samples, utility functions, weights, and the package
    outputs quantitative benefit-risk scores.  The posterior of the benefit-risk
    scores for each group can be compared.  Some plotting capabilities are also
    included.
Implementation of the record linkage methodology proposed by Sadinle (2017) <doi:10.1080/01621459.2016.1148612>.  It handles the bipartite record linkage problem, where two duplicate-free datafiles are to be merged.
Provides two main functions, il() and fil(). The il() function implements the EM algorithm developed by Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068> to estimate the parameters of a logistic regression model with the missing response when the missing data mechanism is nonignorable. The fil() function implements the algorithm proposed by Maity et. al. (2017+) <https://github.com/arnabkrmaity/brlrmr> to reduce the bias produced by the method of Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068>.
Fits novel models for the conditional relative risk, risk difference and odds ratio <doi:10.1080/01621459.2016.1192546>.
Fit Bayesian generalized (non-)linear multivariate multilevel models
    using 'Stan' for full Bayesian inference. A wide range of distributions
    and link functions are supported, allowing users to fit -- among others --
    linear, robust linear, count data, survival, response times, ordinal,
    zero-inflated, hurdle, and even self-defined mixture models all in a
    multilevel context. Further modeling options include both theory-driven and
    data-driven non-linear terms, auto-correlation structures, censoring and 
    truncation, meta-analytic standard errors, and quite a few more. 
    In addition, all parameters of the response distribution can be predicted 
    in order to perform distributional regression. Prior specifications are 
    flexible and explicitly encourage users to apply prior distributions that
    actually reflect their prior knowledge. Models can easily be evaluated and
    compared using several methods assessing posterior or prior predictions. 
    References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; 
    Bürkner (2018) <doi:10.32614/RJ-2018-017>;
    Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017)
    <doi:10.18637/jss.v076.i01>.
The mixed model for repeated measures (MMRM) is a popular
  model for longitudinal clinical trial data with continuous endpoints,
  and 'brms' is a powerful and versatile package for fitting Bayesian
  regression models.  The 'brms.mmrm' R package leverages 'brms' to run
  MMRMs, and it supports a simplified interfaced to reduce difficulty
  and align with the best practices of the life sciences.  References:
  Bürkner (2017) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008)
  <doi:10.1177/009286150804200402>.
Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the 'brms' package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and <doi:10.1111/biom.12707>).
Bayesian regularization for feed-forward neural networks.
Very large numbers in R.  Real numbers are held
        using their natural logarithms, plus a logical flag indicating
        sign.  Functionality for complex numbers is also provided.  The
        package includes a vignette that gives a step-by-step
        introduction to using S4 methods.
Approximates best-subset selection (L0) regression with
   an iteratively adaptive Ridge (L2) penalty for large-scale models.
   This package uses Cyclops for an efficient implementation and the
   iterative method is described in Kawaguchi et al (2020)
   <doi:10.1002/sim.8438> and Li et al (2021)
   <doi:10.1016/j.jspi.2020.12.001>.
Data on multiple individuals through time are often sampled at 
    times that differ between persons. Irregular observation times can severely 
    complicate the statistical analysis of the data. The broken stick model 
    approximates each subject’s trajectory by one or more connected line segments. 
    The times at which segments connect (breakpoints) are identical for all 
    subjects and under control of the user. A well-fitting broken stick model 
    effectively transforms individual measurements made at irregular times into 
    regular trajectories with common observation times. Specification of the 
    model requires three variables: time, measurement and subject. The 
    model is a special case of the linear mixed model, with time as a linear 
    B-spline and subject as the grouping factor. The main assumptions are: 
    subjects are exchangeable, trajectories between consecutive breakpoints are 
    straight, random effects follow a multivariate normal distribution, and 
    unobserved data are missing at random. The package contains functions for 
    fitting the broken stick model to data, for predicting curves in new data 
    and for plotting broken stick estimates. The package supports two 
    optimization methods, and includes options to structure the 
    variance-covariance matrix of the random effects. The analyst may use the 
    software to smooth growth curves by a series of connected straight lines, to 
    align irregularly observed curves to a common time grid, to create synthetic 
    curves at a user-specified set of breakpoints, to estimate the time-to-time 
    correlation matrix and to predict future observations. See 
    <doi:10.18637/jss.v106.i07> for additional documentation on background, 
    methodology and applications.
Miscellaneous R functions, including functions related to
    graphics (mostly for base graphics), permutation tests, running
    mean/median, and general utilities.
Summarizes key information about statistical
    objects in tidy tibbles. This makes it easy to report results, create
    plots and consistently work with large numbers of models at once.
    Broom provides three verbs that each provide different types of
    information about a model. tidy() summarizes information about model
    components such as coefficients of a regression. glance() reports
    information about an entire model, such as goodness of fit measures
    like AIC and BIC. augment() adds information about individual
    observations to a dataset, such as fitted values or influence
    measures.
Provides suite of functions to work with regression model
    'broom::tidy()' tibbles.  The suite includes functions to group
    regression model terms by variable, insert reference and header rows
    for categorical variables, add variable labels, and more.
Convert fitted objects from various R mixed-model packages
    into tidy data frames along the lines of the 'broom' package.
    The package provides three
    S3 generics for each model: tidy(), which summarizes a model's statistical findings such as
    coefficients of a regression; augment(), which adds columns to the original
    data such as predictions, residuals and cluster assignments; and glance(), which
    provides a one-row summary of model-level statistics.
A lossless compressed data format that uses a combination of the
    LZ77 algorithm and Huffman coding. Brotli is similar in speed to deflate (gzip)
    but offers more dense compression.
An R interface for the Brown Dog which allows researchers to leverage Brown Dog Services 
    that provides modules to identify the conversion options for a file, to convert file to appropriate
    format, or to extract data from a file. See <http://browndog.ncsa.illinois.edu/> for more information.
Datasets with yearly (2000 to 2021) Brazilian population estimates from DataSUS/Brazilian Health Ministry, aggregated by state, municipality, sex, and age groups. The data in this package is manually downloaded from the DataSUS website and converted to tibbles.
Bayesian estimation and variable selection for quantile regression models.
Provides the facility to calculate the Brainerd-Robinson similarity coefficient for the rows of an input table, and to calculate the significance of each coefficient based on a permutation approach; a heatmap is produced to visually represent the similarity matrix. Optionally, hierarchical agglomerative clustering can be performed and the silhouette method is used to identify an optimal number of clusters; the results of the clustering can be optionally used to sort the heatmap.
Broadly useful convenient and efficient R functions
    that bring users concise and elegant R data analyses.
    This package includes easy-to-use functions for
    (1) basic R programming
    (e.g., set working directory to the path of currently opened file;
    import/export data from/to files in any format;
    print tables to Microsoft Word);
    (2) multivariate computation
    (e.g., compute scale sums/means/... with reverse scoring);
    (3) reliability analyses and factor analyses;
    (4) descriptive statistics and correlation analyses;
    (5) t-test, multi-factor analysis of variance (ANOVA),
    simple-effect analysis, and post-hoc multiple comparison;
    (6) tidy report of statistical models
    (to R Console and Microsoft Word);
    (7) mediation and moderation analyses (PROCESS);
    and (8) additional toolbox for statistics and graphics.
Fully-interactive R interface to the 'OpenBUGS' software for Bayesian analysis using MCMC sampling.  Runs natively and stably in 32-bit R under Windows.  Versions running on x86Linux and on 64-bit R under Windows are in "beta" status and less efficient.
Provides high-level modeling functions to define and train
    models using the 'torch' R package. Models include linear, logistic,
    and multinomial regression as well as multilayer perceptrons.
Provides the functions for Brunner-Munzel test and
    permuted Brunner-Munzel test,
    which enable to use formula, matrix, and table as argument.
    These functions are based on Brunner and Munzel (2000)
     <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U>
    and Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>,
    and are written with FORTRAN.
Provide real-time access to data from the Regional Securities Exchange SA(<https://www.brvm.org/en>), commonly known as the 'BRVM' stock exchange. The goal is to facilitate data access for users of the R programming language. The package includes a variety of data that can be accessed by calling functions.
When samples contain missing data, are small, or are suspected of bias,
    estimation of scale reliability may not be trustworthy.  A recommended solution
    for this common problem has been Bayesian model estimation.  Bayesian methods
    rely on user specified information from historical data or researcher intuition 
    to more accurately estimate the parameters.  This package provides a user friendly
    interface for estimating test reliability.  Here, reliability is modeled as a beta
    distributed random variable with shape parameters alpha=true score variance and
    beta=error variance (Tanzer & Harlow, 2020) <doi:10.1080/00273171.2020.1854082>.
Allows the user to generate bootstrap cards within
    R markdown documents. Intended for use in conjunction with
    R markdown HTML outputs and other formats that support the 
    bootstrap 4 library.
Make 'Bootstrap 4' Shiny dashboards. Use the full power
    of 'AdminLTE3', a dashboard template built on top of 'Bootstrap 4' 
    <https://github.com/ColorlibHQ/AdminLTE>.
Collection of functions, data sets and code examples 
 for evaluations of field trials with the objective of equivalence assessment.
Tools to fit Bayesian state-space models to animal tracking data. Models are provided for location 
    filtering, location filtering and behavioural state estimation, and their hierarchical versions. 
    The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit 
    to other tracking data types. For Global Positioning System data, consider the 'moveHMM' package. 
    Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged 
    to explore tools available in packages such as 'coda' and 'boa'.
Contains functions to perform Bayesian inference
    using a spectral analysis of Gaussian process priors.
    Gaussian processes are represented with a Fourier series 
    based on cosine basis functions. Currently the package
    includes parametric linear models, partial linear additive
    models with/without shape restrictions, generalized linear
    additive models with/without shape restrictions, and  
    density estimation model. To maximize computational 
    efficiency, the actual Markov chain Monte Carlo sampling 
    for each model is done using codes written in FORTRAN 90.
    This software has been developed using funding supported by
    Basic Science Research Program through the National Research
    Foundation of Korea (NRF) funded by the Ministry of Education
    (no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).
Render SVG as interactive figures to display contextual
   information, with selectable and clickable user interface elements.
   These figures can be seamlessly integrated into 'rmarkdown' and 'Quarto'
   documents, as well as 'shiny' applications, allowing manipulation of elements
   and reporting actions performed on them.
   Additional features include pan, zoom in/out functionality, and the ability
   to export the figures in SVG or PNG formats.
Data sets for book "Basic Statistics and Data Analysis" by
    Larry J. Kitchens.
Exposes the binary search functions of the C++ standard library (std::lower_bound, std::upper_bound) plus other convenience functions, allowing faster lookups on sorted vectors.
Performs goodness of fit test for the Birnbaum-Saunders distribution and provides the maximum likelihood estimate and the method-of-moments estimate. For more details, see Park and Wang (2013) <arXiv:2308.10150>. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).
Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.
A collection of integrated tools designed to seamlessly interact with each other for the analysis of biogenic silica 'bSi' in inland and marine sediments. These tools share common data representations and follow a consistent 'API' design. The primary goal of the 'bSi' package is to simplify the installation process, facilitate data loading, and enable the analysis of multiple samples for biogenic silica fluxes. This package is designed to enhance the efficiency and coherence of the entire 'bSi' analytic workflow, from data loading to model construction and visualization tailored towards reconstructing productivity in aquatic ecosystems.
Easily use 'Bootstrap' icons inside 'Shiny' apps and 'R
    Markdown' documents. More generally, icons can be inserted in any
    'htmltools' document through inline 'SVG'.
A highly scientific and utterly addictive 
  bird point count simulator 
  to test statistical assumptions, aid survey design,
  and have fun while doing it.
  The simulations follow time-removal and distance sampling models 
  based on Matsuoka et al. (2012) <doi:10.1525/auk.2012.11190>,
  Solymos et al. (2013) <doi:10.1111/2041-210X.12106>,
  and Solymos et al. (2018) <doi:10.1650/CONDOR-18-32.1>,
  and sound attenuation experiments by 
  Yip et al. (2017) <doi:10.1650/CONDOR-16-93.1>.
The Super Imposition by Translation and Rotation (SITAR) model 
    is a shape-invariant nonlinear mixed effect model that fits a natural cubic 
    spline mean curve to the growth data, and aligns individual-specific growth 
    curves to the underlying mean curve via a set of random effects (see Cole, 
    2010 <doi:10.1093/ije/dyq115> for details). The non-Bayesian version of the 
    SITAR model can be fit by using an already available R package 'sitar'. 
    While 'sitar' package allows modelling of a single outcome only, the 'bsitar' 
    package offers a great flexibility in fitting models of varying complexities 
    that include joint modelling of multiple outcomes such as height and weight 
    (multivariate model). Also, the 'bsitar' package allows simultaneous analysis 
    of a single outcome separately for sub groups defined by a factor variable such 
    as gender. This is achieved by fitting separate models for each sub group 
    (such as males and females for gender variable). An advantage of such approach 
    is that posterior draws for each sub group are part of a single model object 
    that makes it possible to compare coefficients across groups and test hypotheses. 
    As 'bsitar' package is a front-end to the R package 'brms', it offers an excellent 
    support for post-processing of posterior draws via various functions that are 
    directly available from the 'brms' package. In addition, the 'bsitar' package 
    include various customized functions that allow estimation and visualization 
    growth curves such as distance (increase in size with age) and velocity 
    (change in growth rate as a function of age).
Collect data from and make posts on 'Bluesky' Social
    via the Hypertext Transfer Protocol (HTTP) Application Programming 
    Interface (API), as documented at <https://atproto.com/specs/xrpc>. This 
    further supports broader queries to the Authenticated Transfer (AT) Protocol
    <https://atproto.com/> which 'Bluesky' Social relies on. Data is returned in a
    tidy format and posts can be made using a simple interface.
Bayesian synthetic likelihood (BSL, Price et al. (2018) <doi:10.1080/10618600.2017.1302882>)
    is an alternative to standard, non-parametric approximate Bayesian 
	computation (ABC). BSL assumes a multivariate normal distribution 
	for the summary statistic likelihood and it is suitable when the 
	distribution of the model summary statistics is sufficiently regular. 
	This package provides a Metropolis Hastings Markov chain Monte Carlo 
	implementation of four methods (BSL, uBSL, semiBSL and BSLmisspec) and two 
	shrinkage estimators (graphical lasso and Warton's estimator).
	uBSL (Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) uses 
	an unbiased estimator to the normal density. A semi-parametric version 
	of BSL (semiBSL, An et al. (2018) <arXiv:1809.05800>) is more robust 
	to non-normal summary statistics. BSLmisspec (Frazier et al. 2019 
	<arXiv:1904.04551>) estimates the Gaussian synthetic likelihood whilst 
	acknowledging that there may be incompatibility between the model and the 
	observed summary statistic. Shrinkage estimation can help to decrease the
	number of model simulations when the dimension of the summary statistic is 
	high (e.g., BSLasso, An et al. (2019) <doi:10.1080/10618600.2018.1537928>). 
	Extensions to this package are planned. For a journal article describing how
	to use this package, see An et al. (2022) <doi:10.18637/jss.v101.i11>.
Simplifies custom 'CSS' styling of both 'shiny' and
    'rmarkdown' via 'Bootstrap' 'Sass'. Supports 'Bootstrap' 3, 4 and 5 as
    well as their various 'Bootswatch' themes. An interactive widget is
    also provided for previewing themes in real time.
Bayes screening and model discrimination follow-up designs.
Functions for training an optimal decision tree classifier, making predictions and generating latex code for plotting. Works for two-class and multi-class classification problems. The algorithm seeks the optimal Boolean rule consisting of multiple variables to split a node, resulting in shorter trees. Use bsnsing() to build a tree, predict() to make predictions and plot() to plot the tree into latex and PDF. See Yanchao Liu (2022) <arXiv:2205.15263> for technical details. Source code and more data sets are at <https://github.com/profyliu/bsnsing/>.
The purpose of this package is to fit the three Spatial Econometric Models proposed
  in Anselin (1988, ISBN:9024737354) in the homoscedastic and the heteroscedatic case. The fit is made through MCMC algorithms
  and observational working variables approach.
Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS). BSP-BSS is designed for spatially dependent signals in high dimensional and large-scale data, such as neuroimaging. The method assumes the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, and constructs a Bayesian nonparametric prior by thresholding Gaussian processes. Details can be found in our paper: Wu et al. (2022+) "Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process" <doi:10.1080/01621459.2022.2123336>.
Provides functions which perform Bayesian estimations of 
        a covariance matrix for multivariate normal data. Assumes that
        the covariance matrix is sparse or band matrix and positive-definite. 
        This software has been developed using funding supported by
        Basic Science Research Program through the National Research
        Foundation of Korea ('NRF') funded by the Ministry of Education
        ('RS-2023-00211979', 'NRF-2022R1A5A7033499', 'NRF-2020R1A4A1018207' 
        and 'NRF-2020R1C1C1A01013338').
Bayesian inference on the (discrete) power spectrum of time series.
Build and use B-splines for interpolation and regression.
  In case of regression, equality constraints as well as monotonicity
  and/or positivity of B-spline weights can be imposed. Moreover, 
  knot positions (not only spline weights) can be part of 
  optimized parameters too. For this end, 'bspline' is able to calculate
  Jacobian of basis vectors as function of knot positions. User is provided with 
  functions calculating spline values at arbitrary points. These 
  functions can be differentiated and integrated to obtain B-splines calculating 
  derivatives/integrals at any point. B-splines of this package can 
  simultaneously operate on a series of curves sharing the same set of 
  knots. 'bspline' is written with concern about computing 
  performance that's why the basis and Jacobian calculation is implemented in C++.
  The rest is implemented in R but without notable impact on computing speed.
Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series.  The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018). <doi.org/10.1007/s11222-017-9796-9>.
The Bootstrap framework lets you add some JavaScript functionality to your web site by
  adding attributes to your HTML tags - Bootstrap takes care of the JavaScript
  <https://getbootstrap.com/docs/3.3/javascript/>. If you are using R Markdown or Shiny, you can
  use these functions to create collapsible sections, accordion panels, modals, tooltips,
  popovers, and an accordion sidebar framework (not described at Bootstrap site).
  Please note this package was designed for Bootstrap 3.3.
Enables binary package installations on Linux distributions.
    Provides functions to manage packages via the distribution's package
    manager. Also provides transparent integration with R's install.packages()
    and a fallback mechanism. When installed as a system package, interacts
    with the system's package manager without requiring administrative
    privileges via an integrated D-Bus service; otherwise, uses sudo.
    Currently, the following backends are supported: DNF, APT, ALPM.
The main functions carry out Gibbs' sampler routines for nonparametric and semiparametric Bayesian models for random effects meta-analysis.
Fit Bayesian models with a focus on the spatial econometric models.
Efficient simulation of Brownian semistationary (BSS) processes using the hybrid simulation scheme, as described in 
    Bennedsen, Lunde, Pakkannen (2017) <arXiv:1507.03004v4>, as well as functions to fit BSS processes
    to data, and functions to estimate the stochastic volatility process of a BSS process.
Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.
Efficient methods for Bayesian inference of state space models 
    via Markov chain Monte Carlo (MCMC) based on parallel 
    importance sampling type weighted estimators 
    (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), 
    particle MCMC, and its delayed acceptance version. 
    Gaussian, Poisson, binomial, negative binomial, and Gamma
    observation densities and basic stochastic volatility models 
    with linear-Gaussian state dynamics, as well as general non-linear Gaussian 
    models and discretised diffusion models are supported. 
    See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.
It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function, moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter Birnbaum-Saunders model based on Skew-Normal Distribution. Also, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution. Additionally, we incorporate the EM algorithm based on the assumption that the error term follows a finite mixture of Sinh-normal distributions.
Provides functions for blind source separation over multivariate spatial data, and useful statistics for evaluating performance of estimation on mixing matrix. 'BSSoverSpace' is based on an eigen analysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and thus can handle moderately high-dimensional random fields. This package is an implementation of the method described in Zhang, Hao and Yao (2022)<arXiv:2201.02023>.
Whitening is the first step of almost all blind source separation (BSS) methods. A fast implementation of whitening for BSS is implemented to serve as a lightweight dependency for packages providing BSS methods.
Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.
Functions are pre-configured to utilize Bootstrap 5 classes and 
  HTML structures to create Bootstrap-styled HTML quickly and easily. Includes 
  functions for creating common Bootstrap elements such as containers, rows, cols,
  navbars, etc. Intended to be used with the html5 package. Learn more at 
  <https://getbootstrap.com/>.
Perform record linkage on streaming files using recursive Bayesian updating.
Time series regression using dynamic linear models fit using
  MCMC. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many
  other sources.
It submits R code/R scripts/shell commands to 'LSF cluster' 
  (<https://en.wikipedia.org/wiki/Platform_LSF>, the 'bsub' system) without 
  leaving R. There is also an interactive 'shiny' app for monitoring the job status.
Efficient algorithms for Bayesian estimation of Structural Vector Autoregressive (SVAR) models via Markov chain Monte Carlo methods. A wide range of SVAR models is considered, including homo- and heteroskedastic specifications and those with non-normal structural shocks. The heteroskedastic SVAR model setup is similar as in Woźniak & Droumaguet (2015) <doi:10.13140/RG.2.2.19492.55687> and Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>. The sampler of the structural matrix follows Waggoner & Zha (2003) <doi:10.1016/S0165-1889(02)00168-9>, whereas that for autoregressive parameters follows Chan, Koop, Yu (2022) <https://www.joshuachan.org/papers/OISV.pdf>. The specification of Markov switching heteroskedasticity is inspired by Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>, and that of Stochastic Volatility model by Kastner & Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002>.
Implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints.
Performs (Adaptive) Boosting Trees for Poisson distributed response variables, using log-link function.
  The code approach is similar to the one used in 'gbm'/'gbm3'. Moreover, each tree in the expansion is built thanks to the 'rpart' package.
  This package is based on following books and articles
  Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-25820-7>
  Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-57556-4>
  Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-25827-6>
  Denuit, M., Hainaut, D., Trufin, J. (2022) <doi:10.1080/03461238.2022.2037016>
  Denuit, M., Huyghe, J., Trufin, J. (2022) <https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A244325/datastream/PDF_01/view>
  Denuit, M., Trufin, J., Verdebout, T. (2022) <https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A268577>.
The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. 
        The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid.
        The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid.
        The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid.
        The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid.
        Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>, 
        Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.
We utilize the Bradley-Terry Model to estimate the abilities of teams using paired comparison data. For dynamic approximation of current rankings, we employ the Exponential Decayed Log-likelihood function, and we also apply the Lasso penalty for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.
Performs 'BTLLasso' as described by Schauberger and Tutz (2019) <doi:10.18637/jss.v088.i09> and Schauberger and Tutz (2017) <doi:10.1177/1471082X17693086>. BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.
Biterm Topic Models find topics in collections of short texts. 
    It is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns which are called biterms.
    This in contrast to traditional topic models like Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis 
    which are word-document co-occurrence topic models.
    A biterm consists of two words co-occurring in the same short text window.  
    This context window can for example be a twitter message, a short answer on a survey, a sentence of a text or a document identifier. 
    The techniques are explained in detail in the paper 'A Biterm Topic Model For Short Text' by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013) <https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTM-WWW13.pdf>.
Provides advanced Bayesian methods to estimate
	     abundance and run-timing from temporally-stratified
	     Petersen mark-recapture experiments. Methods include
	     hierarchical modelling of the capture probabilities
  	     and spline smoothing of the daily run size. Theory
  	     described in Bonner and Schwarz (2011)
         <doi:10.1111/j.1541-0420.2011.01599.x>.
Simulate, estimate and forecast a wide range of regression based dynamic models for bounded time series, covering the most commonly applied models in the literature. The main calculations are done in 'FORTRAN', which translates into very fast algorithms. The main references are 
   Bayer et al. (2017)  <doi:10.1016/j.jhydrol.2017.10.006>, 
   Pumi et al. (2019) <doi:10.1016/j.jspi.2018.10.001>, 
   Pumi et al. (2021) <doi:10.1111/sjos.12439> and 
	 Pumi et al. (2022) <arXiv:2211.02097>.
Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties.
  The package implements the Barigozzi and Trapani (2022) <doi:10.1080/07350015.2021.1901719> test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors.
  Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) <doi:10.1016/j.jeconom.2003.10.022> that provide a complementary measure for the number of factors.
Functions for data preparation, parameter estimation, scoring, and plotting for the 
    BG/BB (Fader, Hardie, and Shang 2010 <doi:10.1287/mksc.1100.0580>), 
    BG/NBD (Fader, Hardie, and Lee 2005 <doi:10.1287/mksc.1040.0098>) and 
    Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 <doi:10.1509/jmkr.2005.42.4.415>) models.
Provides advanced statistical methods to describe and predict customers'
  purchase behavior in a non-contractual setting. It uses historic transaction records to fit a
  probabilistic model, which then allows to compute quantities of managerial interest on a cohort-
  as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.). This
  package complements the BTYD package by providing several additional buy-till-you-die models, that
  have been published in the marketing literature, but whose implementation are complex and non-trivial.
  These models are: NBD [Ehrenberg (1959) <doi:10.2307/2985810>], MBG/NBD [Batislam et al (2007) 
  <doi:10.1016/j.ijresmar.2006.12.005>], (M)BG/CNBD-k [Reutterer et al (2020) 
  <doi:10.1016/j.ijresmar.2020.09.002>], Pareto/NBD (HB) [Abe (2009) <doi:10.1287/mksc.1090.0502>] 
  and Pareto/GGG [Platzer and Reutterer (2016) <doi:10.1287/mksc.2015.0963>].
Plotting package based on the grid system, combining elements of 
    a bubble plot and heatmap to conveniently display two numerical variables, 
    (represented by color and size) grouped by categorical variables on the x 
    and y axes. This is a useful alternative to a forest plot when the data can 
    be grouped in two dimensions, such as predictors x outcomes. It has 
    particular advantages for visualising the metabolic measures produced by 
    the 'Nightingale Health' metabolomics platform, and templates are included 
    for automatically generating figures from these datasets.
Creates bubbles within 'shiny' and 'rmarkdown' backgrounds using the 'bubbly-bg' 'JavaScript' library.
Provides functions for various statistical techniques commonly used in the social sciences, including functions to compute clustered robust standard errors, combine results across multiply-imputed data sets, and simplify the addition of robust and clustered robust standard errors.
Bias- and Uncertainty-Corrected Sample Size. BUCSS implements a method of correcting for publication bias and
    uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562).
Statistical classification and regression have been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades: see, e.g, Kazemi and Mirroshandel (2018) <DOI:10.1016/j.artmed.2017.12.001>. It is a veritable technique which was originally designed for the classification, and hence, the Buddle package can provide sublime solutions to various challenging classification and regression problems encountered in the clinical trials. The Buddle package is based on the back-propagation algorithm - together with various powerful techniques such as batch normalization and dropout - which performs a multi-layer feed-forward neural network: see Krizhevsky et. al (2017) <DOI:10.1145/3065386>, Schmidhuber (2015) <DOI:10.1016/j.neunet.2014.09.003> and LeCun et al. (1998) <DOI:10.1109/5.726791> for more details. This package contains two main functions: TrainBuddle() and FetchBuddle(). TrainBuddle() builds a feed-forward neural network model and trains the model. FetchBuddle() recalls the trained model which is the output of TrainBuddle(), classifies or regresses given data, and make a final prediction for the data.
Finds the largest possible regression model that will still converge
    for various types of regression analyses (including mixed models and generalized
    additive models) and then optionally performs stepwise elimination similar to the
    forward and backward effect-selection methods in SAS, based on the change in
    log-likelihood or its significance, Akaike's Information Criterion, the Bayesian
    Information Criterion, the explained deviance, or the F-test of the change in R².
Working with reproducible reports or any other
    similar projects often require to run the script that builds the
    output file in a specified way. 'buildr' can help you organize, modify
    and comfortably run those scripts. The package provides a set of
    functions that interactively guides you through the process and that
    are available as 'RStudio' Addin, meaning you can set up the keyboard
    shortcuts, enabling you to choose and run the desired build script
    with one keystroke anywhere anytime.
A build system based on 'GNU make' that creates and
    maintains (simply) make files in an R session and provides
    GUI debugging support through 'Microsoft Visual Code'.
Buckley-James regression for right-censoring survival data with high-dimensional covariates. Implementations for survival data include boosting with componentwise linear least squares, componentwise smoothing splines, regression trees and MARS. Other high-dimensional tools include penalized regression for survival data. See Wang and Wang (2010) <doi:10.2202/1544-6115.1550>.
Given an expression matrix from a bulk sequencing experiment,
        pre-processes it and creates a shiny app for interactive data 
        analysis and visualisation. The app contains quality checks,
        differential expression analysis, volcano and cross plots,
        enrichment analysis and gene regulatory network inference,
        and can be customised to contain more panels by the user.
Multicenter randomized trials involve the collection and analysis of data from numerous study participants across multiple sites. Outliers may be present. To identify outliers, this package examines data at the individual level (univariate and multivariate) and site-level (with and without covariate adjustment). Methods are outlined in further detail in Rigdon et al (to appear).
Designed to simplify and streamline the process of reading
    and processing large volumes of data in R, this package offers a
    collection of functions tailored for bulk data operations. It enables
    users to efficiently read multiple sheets from Microsoft Excel and
    Google Sheets workbooks, as well as various CSV files from a
    directory. The data is returned as organized data frames, facilitating
    further analysis and manipulation. Ideal for handling extensive data
    sets or batch processing tasks, bulkreadr empowers users to manage
    data in bulk effortlessly, saving time and effort in data preparation
    workflows. Additionally, the package seamlessly works with labelled
    data from SPSS and Stata.
Provides functionality to automatically detect groove locations via a Bayesian changepoint detection method to be used in the data preprocessing step
    of forensic bullet matching algorithms. The methods in this package are based on those in Stephens (1994) <doi:10.2307/2986119>. Bayesian changepoint detection will simply be an option
    in the function from the package 'bulletxtrctr' which identifies the groove locations.
Analyze bullet lands using nonparametric methods. We provide a
    reading routine for x3p files (see <http://www.openfmc.org> for more
    information) and a host of analysis functions designed to assess the
    probability that two bullets were fired from the same gun barrel.
Stock, Options and Futures Trading Strategies for Traders and Investors with Bullish Outlook are represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).
The bullwhipgame is an educational game that has as purpose the illustration and exploration of the bullwhip effect,i.e, the increase in demand variability along the supply chain. Marchena Marlene (2010) <arXiv:1009.3977>.
Bumblebee colonies grow during worker production, then decline after switching to production of reproductive individuals (drones and gynes).  This package provides tools for modeling and visualizing this pattern by identifying a switchpoint with a growth rate before and a decline rate after the switchpoint. The mathematical models fit by bumbl are described in Crone and Williams (2016) <doi:10.1111/ele.12581>.
A simple tool to quantify the amount of transmission
   of an infectious disease of interest occurring within and between 
   population groups. 'bumblebee' uses counts of observed directed 
   transmission pairs, identified phylogenetically from deep-sequence data or 
   from epidemiological contacts, to quantify transmission flows within and 
   between population groups accounting for sampling heterogeneity. Population 
   groups might include: geographical areas (e.g. communities, regions), 
   demographic groups (e.g. age, gender) or arms of a randomized clinical 
   trial. See the 'bumblebee' website for statistical theory, documentation 
   and examples <https://magosil86.github.io/bumblebee/>.
Implementation of the bunching estimator for kinks and notches. 
        Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). 
        It produces publication-ready plots in the style followed since Chetty et al. (2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.
View and analyze data where bunching is expected. Estimate counter-
    factual distributions. For earnings data, estimate the compensated
    elasticity of earnings w.r.t. the net-of-tax rate.
Download data from the time-series
  databases of the Bundesbank, the German central
  bank. See the overview at the Bundesbank website
  (<https://www.bundesbank.de/en/statistics/time-series-databases>)
  for available series. The package provides only a
  single function, getSeries(), which supports both
  traditional and real-time datasets; it will also
  download meta data if available. Downloaded data
  can automatically be arranged in various formats,
  such as data frames or 'zoo' series. The data
  may optionally be cached, so as to avoid repeated
  downloads of the same series.
All final tables of Germany's highest football (soccer!) league, the Bundesliga. Contains data from 1964 to 2016.
Typically, models in 'R' exist in memory and can be saved via
    regular 'R' serialization. However, some models store information in
    locations that cannot be saved using 'R' serialization alone. The goal
    of 'bundle' is to provide a common interface to capture this
    information, situate it within a portable object, and restore it for
    use in new settings.
Comprehensive Business Process Analysis toolkit. Creates S3-class for event log objects, and related handler functions. Imports related packages for filtering event data, computation of descriptive statistics, handling of 'Petri Net' objects and visualization of process maps. See also packages 'edeaR','processmapR', 'eventdataR' and 'processmonitR'.
The 'bupaverse' is an open-source, integrated suite of R-packages for handling and analysing business process data,
    developed by the Business Informatics research group at Hasselt University, Belgium. Profoundly inspired by the 'tidyverse' package,
    the 'bupaverse' package is designed to facilitate the installation and loading of multiple 'bupaverse' packages in a single step.
    Learn more about 'bupaverse' at the <https://bupar.net> homepage.
Tools to read, write, parse, and analyze forest fire history data (e.g. FHX). Described in Malevich et al. (2018) <doi:10.1016/j.dendro.2018.02.005>.
Call the data wrappers for Bursa Metropolitan Municipality's Open Data Portal <https://acikyesil.bursa.bel.tr/>. This will return all datasets stored in different formats.
A suite of functions for finance, including the estimation
	of variance matrices via a statistical factor model or
	Ledoit-Wolf shrinkage.
Script search, corner, genetic optimization, permutation tests, write expect test.
An implementation of Jon Kleinberg's burst detection algorithm (Kleinberg (2003) <doi:10.1023/A:1024940629314>).  Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results.
Get a current financial year, start of current
    month, End of current month, start of financial year and end of it.
    Allow for offset from the date.
Calculates business duration between two dates. This excluding weekends, public holidays and non-business hours.
A collection of S4 classes, methods and functions to create
        and visualize business plans. Different types of cash flows can be
        defined, which can then be used and tabulated to create profit and
        loss statements, cash flow plans, investment and depreciation
        schedules, loan amortization schedules, etc. The methods are
        designed to produce handsome tables in both PDF and HTML using
        'RMarkdown' or 'Shiny'.
Provides a set of S3 generics to axe components of fitted
    model objects and help reduce the size of model objects saved to disk.
Trading of Butterfly Options Strategies is represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).
Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring. GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better than a random observation drawn from the other group (Mann-Whitney parameter). The net benefit and win ratio statistics, i.e. the difference and ratio between the probabilities relative to the intervention and control groups, can then also be estimated. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>), non-parametric bootstrap, or permutations. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.
Calculates B-value and empirical equivalence bound. B-value is defined as the maximum magnitude of a confidence interval; and the empirical equivalence bound is the minimum B-value at a certain level. A new two-stage procedure for hypothesis testing is proposed, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using the introduced empirical equivalence bound. See Zhao et al. (2019) "B-Value and Empirical Equivalence Bound: A New Procedure of Hypothesis Testing" <arXiv:1912.13084> for details.
Estimation of hierarchical Bayesian vector autoregressive models
    following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>.
    Implements hierarchical prior selection for conjugate priors in the fashion
    of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>.
    Functions to compute and identify impulse responses, calculate forecasts,
    forecast error variance decompositions and scenarios are available.
    Several methods to print, plot and summarise results facilitate analysis.
R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.
Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) and error correction (VEC) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).
Functions to prepare tidy objects from estimated models via 'BVAR'
    (see Kuschnig & Vashold, 2019 <doi:10.13140/RG.2.2.25541.60643>) and
    visualisation thereof. Bridges the gap between estimating models with 'BVAR'
    and plotting the results in a more sophisticated way with 'ggplot2' as well
    as passing them on in a tidy format.
Tools to research Bayesian Vector heterogeneous autoregressive (VHAR) model,
    referring to Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>).
    'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.
An R interface to the Stark-Parker implementation of an
        algorithm for bounded-variable least squares
Implements the EM algorithm with one-step Gradient Descent method to estimate
             the parameters of the Block-Basu bivariate Pareto distribution with location
             and scale. We also found parametric bootstrap and asymptotic confidence 
             intervals based on the observed Fisher information of scale and shape parameters,
             and exact confidence intervals for location parameters. Details are in 
             Biplab Paul and Arabin Kumar Dey (2023) <doi:10.48550/arXiv.1608.02199>
             "An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto
             distribution with location and scale"; 
             E L Lehmann and George Casella (1998) <doi:10.1007/b98854> "Theory of Point Estimation";
             Bradley Efron and R J Tibshirani (1994) <doi:10.1201/9780429246593> 
             "An Introduction to the Bootstrap"; 
             A P Dempster, N M Laird and D B Rubin
             (1977) <www.jstor.org/stable/2984875> "Maximum Likelihood from Incomplete
             Data via the EM Algorithm".
Variable/Feature selection in high or ultra-high dimensional
    settings has gained a lot of attention recently specially in cancer genomic
    studies. This package provides a Bayesian approach to tackle this problem,
    where it exploits mixture of point masses at zero and nonlocal priors to
    improve the performance of variable selection and coefficient estimation.
    product moment (pMOM) and product inverse moment (piMOM) nonlocal priors
    are implemented and can be used for the analyses. This package performs
    variable selection for binary response and survival time response datasets
    which are widely used in biostatistic and bioinformatics community.
    Benefiting from parallel computing ability, it reports necessary outcomes
    of Bayesian variable selection such as Highest Posterior Probability Model
    (HPPM), Median Probability Model (MPM) and posterior inclusion probability
    for each of the covariates in the model. The option to use Bayesian Model
    Averaging (BMA) is also part of this package that can be exploited for
    predictive power measurements in real datasets.
Implements a backward procedure for single and multiple change point detection proposed by Shin et al. <arXiv:1812.10107>. The backward approach is particularly useful to detect short and sparse signals which is common in copy number variation (CNV) detection.
Whole-genome regression methods on Bayesian framework fitted via EM
    or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>),
    with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).
Package for Breed Wheat Genomic Selection Pipeline. 
    The R package 'BWGS' is developed by Louis Gautier Tran <louis.gautier.tran@gmail.com> and Gilles Charmet <gilles.charmet@inra.fr>.
    This repository is forked from original repository <https://forgemia.inra.fr/umr-gdec/bwgs>
    and modified as a R package.
A computational tool to describe patterns in black and white images from natural structures. 'bwimage' implemented functions for exceptionally broad subject. For instance, 'bwimage' provide examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure.
Bandwidth selectors for local linear quantile regression, including cross-validation and plug-in methods. The local linear quantile regression estimate is also implemented.
An interface to the Bayesian Weighted Sums model implemented in 'RStan'.
    It estimates the summed effect of multiple, often moderately to highly correlated,
    continuous predictors. Its applications can be found in analysis of exposure mixtures.
    The model was proposed by Hamra, Maclehose, Croen, Kauffman, and
    Newschaffer (2021) <doi:10.3390/ijerph18041373>.
    This implementation includes an extension to model binary outcome.
Performs the 'Baumgartner-Weiss-Schindler' two-sample test of equal
   probability distributions, <doi:10.2307/2533862>. Also performs
   similar rank-based tests for equal probability distributions due to
   Neuhauser <doi:10.1080/10485250108832874> and
   Murakami <doi:10.1080/00949655.2010.551516>.
Tools to design best-worst scaling designs (i.e., balanced incomplete block designs) and
    to analyze data from these designs, using aggregate and individual methods such as: difference 
    scores, Louviere, Lings, Islam, Gudergan, & Flynn (2013) <doi:10.1016/j.ijresmar.2012.10.002>; 
    analytical estimation, Lipovetsky & Conklin (2014) <doi:10.1016/j.jocm.2014.02.001>; empirical 
    Bayes, Lipovetsky & Conklin (2015) <doi:10.1142/S1793536915500028>; Elo, Hollis (2018) 
    <doi:10.3758/s13428-017-0898-2>; and network-based measures.
Functions to get and download city bike data from 
    the website and API service of each city bike service in Norway. The
    package aims to reduce time spent on getting Norwegian city bike data,
    and lower barriers to start analyzing it. The data is retrieved from
    Oslo City Bike, Bergen City Bike, and Trondheim City Bike. The data is 
    made available under NLOD 2.0 <https://data.norge.no/nlod/en/2.0>.
Shows statistics about bytes contained in a file 
  as a circle graph of deviations from mean in sigma increments. 
  The function can be useful for statistically analyze the content of files 
  in a glimpse: text files are shown as a green centered crown, compressed 
  and encrypted files should be shown as equally distributed variations with 
  a very low CV (sigma/mean), and other types of files can be classified between 
  these two categories depending on their text vs binary content, which can be 
  useful to quickly determine how information is stored inside them (databases, 
  multimedia files, etc).
Provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters. Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details.
The c060 package provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models.
Dataset containing cumulative COVID-19 deaths (absolute and per 100,000 pop) at the regional level (mostly NUTS 3) for 31 EU/EFTA countries.
Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12.
Compare two classifications or clustering solutions that may or may
    not have the same number of classes, and that might have hard or soft
    (fuzzy, probabilistic) membership. Calculate various metrics to assess how
    the clusters compare to each other. The calculations are simple, but provide
    a handy tool for users unfamiliar with matrix multiplication. This package
    is not geared towards traditional accuracy assessment for classification/
    mapping applications - the motivating use case is for comparing a
    probabilistic clustering solution to a set of reference or existing class
    labels that could have any number of classes (that is, without having to
    degrade the probabilistic clustering to hard classes).
'Cristin' to 'Zotero' ('c2z') aims at obtaining total
    dominion over 'Cristin' ('Current Research Information SysTem in
    Norway') and 'Zotero'. The package enables manipulating 'Zotero'
    libraries using 'R'.  Import references from 'Cristin', 'Regjeringen',
    'CRAN', 'ISBN' ('Alma', 'LoC'), and 'DOI' ('CrossRef', 'DataCite') to
    a 'Zotero' library. Add, edit, copy, or delete items, including
    attachments and collections, and export references to 'BibLaTeX' (and
    other formats).
Create interactive charts with the 'C3.js' <http://c3js.org/> charting library. All plot 
    types in 'C3.js' are available and include line, bar, scatter, and mixed geometry plots. Plot 
    annotations, labels and axis are highly adjustable. Interactive web based charts can be embedded 
    in R Markdown documents or Shiny web applications.
Allows inferring gene regulatory networks
        with direct physical interactions from microarray expression
        data using C3NET.
Get insight into a forest of classification trees, by calculating similarities between the trees, and subsequently clustering them. Each cluster is represented by it's most central cluster member. The package implements the methodology described in Sies & Van Mechelen (2020) <doi:10.1007/s00357-019-09350-4>.
C5.0 decision trees and rule-based models for pattern
    recognition that extend the work of Quinlan (1993,
    ISBN:1-55860-238-0).
Computation and visualization of simple, multiple and joint correspondence analysis.
Provides four variants of three-way correspondence analysis (ca):
 three-way symmetrical ca, three-way non-symmetrical ca, three-way ordered symmetrical ca
 and three-way ordered non-symmetrical ca.
Creates project specific directory and file templates that are 
 written to a .Rprofile file. Upon starting a new R session, these templates 
 can be used to streamline the creation of new directories that are 
 standardized to the user's preferences and can include the initiation of a 
 git repository, an RStudio R project, and project-local dependency management 
 with the 'renv' package.
Performs simple correspondence analysis on a two-way contingency table, 
    or multiple correspondence analysis (homogeneity analysis) 
    on data with p categorical variables,
    and produces bootstrap-based elliptical confidence regions around the 
    projected coordinates for the category points. 
    Includes routines to plot the results in a variety of styles. 
    Also reports the standard numerical output for correspondence analysis.
A set of functions to conduct Conjunctive Analysis of Case Configurations (CACC) as described in Miethe, Hart, and Regoeczi (2008) <doi:10.1007/s10940-008-9044-8>, and identify and quantify situational clustering in dominant case configurations as described in Hart (2019) <doi:10.1177/0011128719866123>. Initially conceived as an exploratory technique for multivariate analysis of categorical data, CACC has developed to include formal statistical tests that can be applied in a wide variety of contexts. This technique allows examining composite profiles of different units of analysis in an alternative way to variable-oriented methods.
Easily cache and retrieve computation results. The package works seamlessly across interactive R sessions, R scripts and Rmarkdown documents.
Key-value stores with automatic pruning. Caches can limit
    either their total size or the age of the oldest object (or both),
    automatically pruning objects to maintain the constraints.
A toolkit to perform cross-species analysis based on scRNA-seq data. This package contains 5 main features. (1) identify Markers in each cluster. (2) Cell type annotation (3) identify conserved markers. (4) identify conserved cell types. (5) identify conserved modules of regulatory networks.
Computes classification accuracy and consistency indices under Item Response Theory. Implements the total score IRT-based methods in Lee, Hanson & Brennen (2002) and Lee (2010), the IRT-based methods in Rudner (2001, 2005), and the total score nonparametric methods in Lathrop & Cheng (2014). For dichotomous and polytomous tests.
Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.
Hansen's (1995) Covariate-Augmented
        Dickey-Fuller (CADF) test. The only required argument is y, the
        Tx1 time series to be tested. If no stationary covariate X is
        passed to the procedure, then an ordinary ADF test is
        performed. The p-values of the test are computed using the
        procedure illustrated in Lupi (2009).
Encrypts and decrypts strings using either the Caesar cipher or a
    pseudorandom number generation (using set.seed()) method.
Simulate plasma caffeine concentrations using population pharmacokinetic model described in Lee, Kim, Perera, McLachlan and Bae (2015) <doi:10.1007/s00431-015-2581-x>.
A time series usually does not have a uniform growth rate. Compound Annual Growth Rate measures the average annual growth over a given period. More details can be found in Bardhan et al. (2022) <DOI:10.18805/ag.D-5418>.
Provides functions for the estimation of the conditional Akaike
    information in generalized mixed-effect models fitted with (g)lmer() 
    from 'lme4', lme() from 'nlme' and gamm() from 'mgcv'.
    For a manual on how to use 'cAIC4', see Saefken et al. (2021) <doi:10.18637/jss.v099.i08>.
Allows to plot a number of information related to the interpretation of Correspondence Analysis' results. It provides the facility to plot the contribution of rows and columns categories to the principal dimensions, the quality of points display on selected dimensions, the correlation of row and column categories to selected dimensions, etc. It also allows to assess which dimension(s) is important for the data structure interpretation by means of different statistics and tests. The package also offers the facility to plot the permuted distribution of the table total inertia as well as of the inertia accounted for by pairs of selected dimensions. Different facilities are also provided that aim to produce interpretation-oriented scatterplots. Reference: Alberti 2015 <doi:10.1016/j.softx.2015.07.001>.
R graphics device using cairographics library that can be used to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG,JPEG,TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don't have Cairo's capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported.
Functions for performing experimental comparisons of algorithms 
             using adequate sample sizes for power and accuracy. Implements the 
             methodology originally presented in Campelo and Takahashi (2019) 
             <doi:10.1007/s10732-018-9396-7> 
             for the comparison of two algorithms, and later generalised in 
             Campelo and Wanner (Submitted, 2019) <arxiv:1908.01720>.
Implements several string comparison algorithms, including calACS (count all common subsequences), lenACS (calculate the lengths of all common subsequences), and lenLCS (calculate the length of the longest common subsequence). Some algorithms differentiate between the more strict definition of subsequence, where a common subsequence cannot be separated by any other items, from its looser counterpart, where a common subsequence can be interrupted by other items. This difference is shown in the suffix of the algorithm (-Strict vs -Loose). For example, q-w is a common subsequence of q-w-e-r and q-e-w-r on the looser definition, but not on the more strict definition. calACSLoose Algorithm from Wang, H. All common subsequences (2007) IJCAI International Joint Conference on Artificial Intelligence, pp. 635-640.
A first-principle, phylogeny-aware comparative genomics tool for 
             investigating associations between terms used to annotate genomic
             components (e.g., Pfam IDs, Gene Ontology terms,) with quantitative 
             or rank variables such as number of cell types, genome size, or 
             density of specific genomic elements. See the project website for 
             more information, documentation and examples, and 
             <doi:10.1016/j.patter.2023.100728> for the full paper.
Contains functions which can be used to calculate Pesticide Risk Metric 
    values in aquatic environments from concentrations of multiple pesticides with known species sensitive 
    distributions (SSDs). Pesticides provided by this package have all be validated
    however if the user has their own pesticides with SSD values they can append them
    to the pesticide_info table to include them in estimates.
Arithmetic operations scalar multiplication, addition, subtraction, multiplication and division of LR fuzzy numbers (which are on the basis of extension principle) have a complicate form for using in fuzzy Statistics, fuzzy Mathematics, machine learning, fuzzy data analysis and etc. Calculator for LR Fuzzy Numbers package relieve and aid applied users to achieve a simple and closed form for some complicated operator based on LR fuzzy numbers and also the user can easily draw the membership function of the obtained result by this package.
Efficient C++ optimized functions for numerical and symbolic calculus as described in Guidotti (2022) <doi:10.18637/jss.v104.i05>. It includes basic arithmetic, tensor calculus, Einstein summing convention, fast computation of the Levi-Civita symbol and generalized Kronecker delta, Taylor series expansion, multivariate Hermite polynomials, high-order derivatives, ordinary differential equations, differential operators (Gradient, Jacobian, Hessian, Divergence, Curl, Laplacian) and numerical integration in arbitrary orthogonal coordinate systems: cartesian, polar, spherical, cylindrical, parabolic or user defined by custom scale factors.
This is a one-function package that will pass only unique values to a computationally-expensive function that returns an output of the same length as the input.
    In importing and working with tidy data, it is common to have index columns, often including time stamps that are far from unique. Some functions to work with these such as text conversion to other variable types (e.g. as.POSIXct()), various grep()-based functions, and often the cut() function are relatively slow when working with tens of millions of rows or more.
Provides function to create, read, write, and work with
    'iCalander' files (which typically have '.ics' or '.ical' extensions),
    and the scheduling data, calendars and timelines of people,
    organisations and other entities that they represent.  'iCalendar' is
    an open standard for exchanging calendar and scheduling information
    between users and computers, described at <https://icalendar.org/>.
Contains the function calendR() for creating fully customizable monthly and yearly calendars (colors, fonts, formats, ...) and even heatmap calendars. In addition, it allows saving the calendars in ready to print A4 format PDF files.
Fork of 'calendR' R package to generate ready to print
  calendars with 'ggplot2' (see <https://r-coder.com/calendar-plot-r/>)
  with additional features (backwards compatible).
  'calendRio' provides a 'calendR()' function that serves as a drop-in
  replacement for the upstream version but allows for additional
  parameters unlocking extra functionality.
Contains greedy algorithms for coarse approximation linear functions.
Functions to impute using random forest under full conditional specifications (multivariate imputation by chained equations). The methods are described in Shah and others (2014) <doi:10.1093/aje/kwt312>.
Assess the calibration of an existing (i.e. previously developed) multistate
  model through calibration plots. 
  Calibration is assessed using one of three methods. 1) Calibration methods for 
  binary logistic regression models applied at a fixed time point in conjunction 
  with inverse probability of censoring weights. 2) Calibration methods for 
  multinomial logistic regression models applied at a fixed time point in conjunction 
  with inverse probability of censoring weights. 3) Pseudo-values estimated using 
  the Aalen-Johansen estimator of observed risk. All methods are applied in conjunction
  with landmarking when required. These calibration plots evaluate the calibration 
  (in a validation cohort of interest) of the transition probabilities estimated from an 
  existing multistate model. While package development has focused on multistate 
  models, calibration plots can be produced for any model which utilises information 
  post baseline to update predictions (e.g. dynamic models); competing risks models; 
  or standard single outcome survival models, where predictions can be made at 
  any landmark time. The underpinning methodology is currently undergoing peer review; see Pate et al. (2023) <arXiv:2308.13394>
  and Pate et al. (2023) <https://alexpate30.github.io/calibmsm/articles/Overview.html>.
General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as base::optim. 
  It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients. 
  Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided.  
  See <https://roliveros-ramos.github.io/calibrar/> for more details.
Package for drawing calibrated scales with tick marks on (non-orthogonal) 
             variable vectors in scatterplots and biplots. Also provides some functions for biplot creation and
	     for multivariate analysis such as principal coordinate analysis.
Performs the calibration procedure proposed by Sung et al. (2018+) <arXiv:1806.01453>. This calibration method is particularly useful when the outputs of both computer and physical experiments are binary and the estimation for the calibration parameters is of interest.
Functions to calculate weights, estimates of changes and corresponding variance estimates for panel data with non-response. Partially overlapping samples are handled. Initially, weights are calculated by linear calibration. By default, the survey package is used for this purpose. It is also possible to use ReGenesees, which can be installed from <https://github.com/DiegoZardetto/ReGenesees>. Variances of linear combinations (changes and averages) and ratios are calculated from a covariance matrix based on residuals according to the calibration model. The methodology was presented at the conference, The Use of R in Official Statistics, and is described in Langsrud (2016) <http://www.revistadestatistica.ro/wp-content/uploads/2016/06/RRS2_2016_A021.pdf>.
Package to assess the calibration of probabilistic classifiers using confidence bands for monotonic functions. Besides testing the classical goodness-of-fit null hypothesis of perfect calibration, the confidence bands calculated within that package facilitate inverted goodness-of-fit tests whose rejection allows for a sought-after conclusion of a sufficiently well-calibrated model. The package creates flexible graphical tools to perform these tests.  For construction details see also Dimitriadis, Dümbgen, Henzi, Puke, Ziegel (2022) <arXiv:2203.04065>.
Plots calibration curves and computes statistics for assessing calibration performance. See De Cock Campo (2023) <arXiv:2309.08559> and Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>.
Performs Bayesian calibration of computer models as per
 Kennedy and O'Hagan 2001.  The package includes routines to find the
 hyperparameters and parameters; see the help page for stage1() for a
 worked example using the toy dataset.  A tutorial is provided in the
 calex.Rnw vignette; and a suite of especially simple one dimensional
 examples appears in inst/doc/one.dim/.
Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0). Please cite this paper: Schwarz J and Heider D, Bioinformatics 2019, 35(14):2458-2465.
Assesses the quality of estimates made by complex sample designs,
  following the methodology developed by the National Institute of Statistics Chile (2020, <https://www.ine.cl/docs/default-source/institucionalidad/buenas-pr%C3%A1cticas/clasificaciones-y-estandares/est%C3%A1ndar-evaluaci%C3%B3n-de-calidad-de-estimaciones-publicaci%C3%B3n-27022020.pdf>)
  and by Economic Commission  for Latin America and
  Caribbean (2020, <https://repositorio.cepal.org/bitstream/handle/11362/45681/1/S2000293_es.pdf>).
It is sometimes useful to perform a computation in a separate
    R process, without affecting the current R process at all.  This
    packages does exactly that.
Intended to analyse recordings from multiple microphones (e.g., backpack
  microphones in captive setting). It allows users to align recordings even if there is non-linear 
  drift of several minutes between them. A call detection and assignment pipeline can be used 
  to find vocalisations and assign them to the vocalising individuals (even if the vocalisation
  is picked up on multiple microphones). The tracing and measurement functions allow for detailed
  analysis of the vocalisations and filtering of noise. Finally, the package includes a function
  to run spectrographic cross correlation, which can be used to compare vocalisations. It also 
  includes multiple other functions related to analysis of vocal behaviour.
The CalMaTe method calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina.
Implementations of canonical associative learning models, with tools to run experiment simulations, estimate model parameters, and compare model representations. Experiments and results are represented using S4 classes and methods.
Implements methods for querying data from CalPASS using its API.
 CalPASS Plus.  MMAP API V1. <https://mmap.calpassplus.org/docs/index.html>.
Generates the calibration simplex (a generalization of the reliability diagram) for three-category probability forecasts, as proposed by Wilks (2013) <doi:10.1175/WAF-D-13-00027.1>.
Tools for the analysis of finite semiparametric mixtures.
        These are useful when data is heterogeneous, e.g. in
        pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms
        (flexible support size) and EM algorithms (fixed support size)
        are provided for univariate (Bohning et al., 1992; 
        <doi:10.2307/2532756>) and bivariate data (Schlattmann et 
        al., 2015; <doi:10.1016/j.jclinepi.2014.08.013>).
Record and generate a 'gif' of your 'R' sessions plots. When creating 
    a visualization, there is inevitably iteration and refinement that occurs. 
    Automatically save the plots made to a specified directory, previewing them 
    as they would be saved. Then combine all plots generated into a 'gif'
    to show the plot refinement over time.
Implements the board game 'CamelUp' for use in introductory statistics classes using a Shiny app.
Quick and easy access to datasets that let you replicate the
    empirical examples in Cameron and Trivedi (2005) "Microeconometrics: Methods and
    Applications" (ISBN: 9780521848053).The data are available as soon as you install
    and load the package (lazy-loading) as data frames. The documentation includes
    reference to chapter sections and page numbers where the datasets are used.
Creates multi-label cell-types for single-cell RNA-sequencing data based on weighted VAM scoring of cell-type specific gene sets. Schiebout, Frost (2022) <https://psb.stanford.edu/psb-online/proceedings/psb22/schiebout.pdf>.
Collect  marketing data from Campaign Manager using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Explore and normalize American campaign finance
    data. Created by the Investigative Reporting Workshop to facilitate
    work on The Accountability Project, an effort to collect public data
    into a central, standard database that is more easily searched:
    <https://publicaccountability.org/>.
A generic, easy-to-use and intuitive
    pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on R
    packages 'rxode2', 'RxODE' and 'mrgsolve'. CAMPSIS provides an
    abstraction layer over the underlying processes of writing a PK/PD
    model, assembling a custom dataset and running a simulation. CAMPSIS
    has a strong dependency to the R package 'campsismod', which allows to
    read/write a model from/to files and adapt it further on the fly in
    the R environment. Package 'campsis' allows the user to assemble a
    dataset in an intuitive manner. Once the user’s dataset is ready, the
    package is in charge of preparing the simulation, calling 'rxode2',
    'RxODE' or 'mrgsolve' (at the user's choice) and returning the
    results, for the given model, dataset and desired simulation settings.
A generic, easy-to-use and expandable implementation of a
    pharmacokinetic (PK) / pharmacodynamic (PD) model based on the S4
    class system. This package allows the user to read/write a
    pharmacometric model from/to files and adapt it further on the fly in
    the R environment. For this purpose, this package provides an
    intuitive API to add, modify or delete equations, ordinary
    differential equations (ODE's), model parameters or compartment
    properties (like infusion duration or rate, bioavailability and
    initial values). Finally, this package also provides a useful export
    of the model for use with simulation packages 'rxode2' and 'mrgsolve'.
    This package is designed and intended to be used with package
    'campsis', a PK/PD simulation platform built on top of 'rxode2' and
    'mrgsolve'.
Copernicus Atmosphere Monitoring Service (CAMS) radiations service 
    provides time series of global, direct, and diffuse irradiations on horizontal
    surface, and direct irradiation on normal plane for the actual weather 
    conditions as well as for clear-sky conditions.
    The geographical coverage is the field-of-view of the Meteosat satellite,
    roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage
    of data is from 2004-02-01 up to 2 days ago. Data are available with a time step
    ranging from 15 min to 1 month. For license terms and to create an account,
    please see <http://www.soda-pro.com/web-services/radiation/cams-radiation-service>.
Management of and data extraction from camera trap data in wildlife studies. The package provides a workflow for storing and sorting camera trap photos (and videos), tabulates records of species and individuals, and creates detection/non-detection matrices for occupancy and spatial capture-recapture analyses with great flexibility. In addition, it can visualise species activity data and provides simple mapping functions with GIS export.
Terrestrial maps with simplified topologies for Census Divisions,
    Agricultural Regions, Economic Regions, Federal Electoral Divisions and
    Provinces.
Simple and seamless access to a variety of 'StatCan' shapefiles for mapping Canadian provinces, regions, forward sortation areas, census divisions, and subdivisions using the popular 'ggplot2' package.
Provides functions to analyze the spatial distribution of
    biodiversity, in particular categorical analysis of neo- and paleo-endemism
    (CANAPE) as described in Mishler et al (2014) <doi:10.1038/ncomms5473>.
    'canaper' conducts statistical tests to determine the types of endemism that
    occur in a study area while accounting for the evolutionary relationships of
    species.
Integrated, convenient, and uniform access to Canadian
    Census data and geography retrieved using the 'CensusMapper' API. This package produces analysis-ready 
    tidy data frames and spatial data in multiple formats, as well as convenience functions
    for working with Census variables, variable hierarchies, and region selection. API
    keys are freely available with free registration at <https://censusmapper.ca/api>.
    Census data and boundary geometries are reproduced and distributed on an "as
    is" basis with the permission of Statistics Canada (Statistics Canada 2001; 2006;
    2011; 2016; 2021).
Generates tree plots with precise branch lengths, gene annotations, and cellular prevalence. The package handles complex tree structures (angles, lengths, etc.) and can be further refined as needed by the user.
Functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network).  Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. <doi:10.1038/ncomms5828>.
Predicts anticancer peptides using random forests trained on the
    n-gram encoded peptides. The implemented algorithm can be accessed from
    both the command line and shiny-based GUI. The CancerGram model is too large 
    for CRAN and it has to be downloaded separately from the repository:
    <https://github.com/BioGenies/CancerGramModel>. For more information see: 
    Burdukiewicz et al. (2020) <doi:10.3390/pharmaceutics12111045>.
Retrieve cancer screening data for cervical, breast and colorectal
    cancers from the Kenya Health Information System <https://hiskenya.org> in a 
    consistent way.
Functions for computing and visualizing 
	generalized canonical discriminant analyses and canonical correlation analysis
	for a multivariate linear model.
	Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA'
	design and is equivalent to canonical correlation analysis between a set of quantitative
	response variables and a set of dummy variables coded from the factor variable.
	The 'candisc' package generalizes this to higher-way 'MANOVA' designs
	for all factors in a multivariate linear model,
	computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) 
	visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are
	now provided for canonical correlation analysis when all predictors are quantitative.
Non-linear/linear hybrid method for batch-effect correction
    that uses Mutual Nearest Neighbors (MNNs) to identify similar cells between 
    datasets. Reference: Loza M. et al. (NAR Genomics and Bioinformatics, 2020) <doi:10.1093/nargab/lqac022>.
A statistical framework and computational procedure for identifying
  the sub-populations within a tumor, determining the mutation profiles of each 
  subpopulation, and inferring the tumor's phylogenetic history. The input are 
  variant allele frequencies (VAFs) of somatic single nucleotide alterations 
  (SNAs) along with allele-specific coverage ratios between the tumor and matched
  normal sample for somatic copy number alterations (CNAs). These quantities can
  be directly taken from the output of existing software. Canopy provides a 
  general mathematical framework for pooling data across samples and sites to 
  infer the underlying parameters. For SNAs that fall within CNA regions, Canopy
  infers their temporal ordering and resolves their phase.  When there are 
  multiple evolutionary configurations consistent with the data, Canopy outputs 
  all configurations along with their confidence assessment.
Chemical analysis of proteins based on their amino acid
  compositions. Amino acid compositions can be read from FASTA files and used to
  calculate chemical metrics including carbon oxidation state and stoichiometric
  hydration state, as described in Dick et al. (2020) <doi:10.5194/bg-17-6145-2020>. 
  Other properties that can be calculated include protein length, grand average of
  hydropathy (GRAVY), isoelectric point (pI), molecular weight (MW), standard
  molal volume (V0), and metabolic costs (Akashi and Gojobori, 2002
  <doi:10.1073/pnas.062526999>; Wagner, 2005 <doi:10.1093/molbev/msi126>;
  Zhang et al., 2018 <doi:10.1038/s41467-018-06461-1>). A database of amino acid
  compositions of human proteins derived from UniProt is provided.
Searches for, accesses, and retrieves new-format and old-format Statistics Canada data 
    tables, as well as individual vectors, as tidy data frames. This package deals with encoding issues, allows for 
    bilingual English or French language data retrieval, and bundles convenience functions 
    to make it easier to work with retrieved table data. Optional caching features are provided.
Extract CANSIM (Statistics Canada) tables and transform them into readily usable data in panel (wide) format. It can also extract more than one table at a time and produce the resulting merge by time period and geographical region.
Enables creation of visualizations using the CanvasXpress framework
    in R. CanvasXpress is a standalone JavaScript library for reproducible research
    with complete tracking of data and end-user modifications stored in a single
    PNG image that can be played back. See <https://www.canvasxpress.org> for more
    information.
Contains the prepared data that is needed for the 'shiny' application examples in the 
    'canvasXpress' package.  This package also includes datasets used for automated 'testthat' tests.
    Scotto L, Narayan G, Nandula SV, Arias-Pulido H et al. (2008) <doi:10.1002/gcc.20577>.
    Davis S, Meltzer PS (2007) <doi:10.1093/bioinformatics/btm254>.
Performs Covariate Assisted Principal (CAP) Regression for covariance matrix outcomes. The method identifies the optimal projection direction which maximizes the log-likelihood function of the log-linear heteroscedastic regression model in the projection space. See Zhao et al. (2018), Covariate Assisted Principal Regression for Covariance Matrix Outcomes, <doi:10.1101/425033> for details.
Combined Analysis of Pleiotropy and Epistasis infers predictive 
    networks between genetic variants and phenotypes. It can be used with 
    standard two-parent populations as well as multi-parent populations, such 
    as the Diversity Outbred (DO) mice, Collaborative Cross (CC) mice, or the 
    multi-parent advanced generation intercross (MAGIC) population of Arabidopsis
    thaliana. It uses complementary information of pleiotropic gene variants across
    different phenotypes to resolve models of epistatic interactions between alleles. 
    To do this, cape reparametrizes main effect and interaction coefficients from
    pairwise variant regressions into directed influence parameters. These
    parameters describe how alleles influence each other, in terms of suppression 
    and enhancement, as well as how gene variants influence phenotypes. All of the 
    final interactions are reported as directed interactions between pairs of 
    parental alleles. For detailed descriptions of the methods used in this package 
    please see the following references.
    Carter, G. W., Hays, M., Sherman, A. & Galitski, T. (2012) <doi:10.1371/journal.pgen.1003010>.
    Tyler, A. L., Lu, W., Hendrick, J. J., Philip, V. M. & Carter, G. W. (2013) <doi:10.1371/journal.pcbi.1003270>.
Functions for performing phylogenetic comparative analyses.
Provides Capital Budgeting Analysis functionality and the essential Annuity loan functions.
    Also computes Loan Amortization Schedules including schedules with irregular payments.
A toolkit for computing and visualizing CAPL-2
    (Canadian Assessment of Physical Literacy, Second Edition;
    <https://www.capl-eclp.ca>) scores and interpretations from raw data.
Performs Correspondence Analysis on the given dataframe and plots the results in a scatterplot that emphasizes the geometric interpretation aspect of the analysis, following Borg-Groenen (2005) and Yelland (2010). It is particularly useful for highlighting the relationships between a selected row (or column) category and the column (or row) categories. See Borg-Groenen (2005, ISBN:978-0-387-28981-6); Yelland (2010) <doi:10.3888/tmj.12-4>.
Quantitative analysis to support companion animal population
    management. Some functions assist survey sampling tasks (calculate sample 
    size for simple and complex designs, select sampling units and estimate 
    population parameters) while others assist the modelling of population 
    dynamics. For demographic characterizations and population management 
    evaluations see: "Baquero, et al." (2018), 
    <doi:10.1016/j.prevetmed.2018.07.006>. For modelling of 
    population dynamics see: "Baquero et al." (2016),
    <doi:10.1016/j.prevetmed.2015.11.009>. For sampling methods
    see: "Levy PS & Lemeshow S" (2013), "ISBN-10: 0470040076"; 
    "Lumley" (2010), "ISBN: 978-0-470-28430-8".
Implements approximation methods for natural capital asset prices suggested by Fenichel and Abbott (2014) <doi:10.1086/676034> in Journal of the Associations of Environmental and Resource Economists (JAERE), Fenichel et al. (2016) <doi:10.1073/pnas.1513779113> in Proceedings of the National Academy of Sciences (PNAS), and Yun et al. (2017) in PNAS (accepted), and their extensions: creating Chebyshev polynomial nodes and grids, calculating basis of Chebyshev polynomials, approximation and their simulations for: V-approximation (single and multiple stocks, PNAS), P-approximation (single stock, PNAS), and Pdot-approximation (single stock, JAERE). Development of this package was generously supported by the Knobloch Family Foundation.
Explore calcium (Ca) and phosphate (Pi) homeostasis with two novel 'Shiny' apps, 
    building upon on a previously published mathematical model written in C, 
    to ensure efficient computations. The underlying model is accessible
    here <https://pubmed.ncbi.nlm.nih.gov/28747359/)>.
    The first application explores the fundamentals of Ca-Pi homeostasis, 
    while the second provides interactive case studies for in-depth exploration of the topic, 
    thereby seeking to foster student engagement and an integrative understanding of Ca-Pi regulation. 
    These applications are hosted at <https://rinterface.com/AppsPhysiol.html>.
Get text from images of text using Captricity Optical Character
    Recognition (OCR) API. Captricity allows you to get text from handwritten
    forms --- think surveys --- and other structured paper documents. And it can
    output data in form a delimited file keeping field information intact. For more
    information, read <https://shreddr.captricity.com/developer/overview/>.
Calibration of penalized criteria for model selection. The calibration methods available are based on the slope heuristics.
Functions to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage, 2019.
Computer algebra via the 'SymPy' library (<https://www.sympy.org/>). 
  This makes it possible to solve equations symbolically, 
  find symbolic integrals, symbolic sums and other important quantities.
Multi-objective optimizer initially developed for the calibration of hydrological models.
     The algorithm is a hybrid of the MEAS algorithm (Efstratiadis and Koutsoyiannis (2005) <doi:10.13140/RG.2.2.32963.81446>) by using the directional search method based on the simplexes of the objective space
     and the epsilon-NGSA-II algorithm with the method of classification of the parameter vectors archiving management by epsilon-dominance (Reed and Devireddy <doi:10.1142/9789812567796_0004>).
Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. See Ma W, Ye X, Tu F, and Hu F (2023) <doi: 10.18637/jss.v107.i02> for details.
Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, <doi:10.1007/BF00116466>) and Leroux model (Leroux et al., 2000, <doi:10.1007/978-1-4612-1284-3_4>). Additionally,  a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.
Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. Most of the data relate to the set of 271 Intermediate Zones (IZ)  that make up the 2001 definition of the  Greater Glasgow and Clyde health board.
Implements a class of univariate and multivariate spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by  random effects, which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including models similar to Rushworth et al. (2014) <doi:10.1016/j.sste.2014.05.001>. Full details are given in the vignette accompanying this package. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council  (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1.
Create beautiful images of source code using
    'carbon.js'<https://carbon.now.sh/about>.
Performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) <doi:10.1111/rssc.12599>). 
  Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points.
The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.
Tools that can aid in the assessment of the autonomic regulation 
		of cardiovascular physiology. The aims of this package are to: 1) study 
		electrocardiography (both intervals and morphology) as extensions 
		of signal processing, 2) study circadian rhythms and how it effects 
		autonomic physiology, 3) assess clinical risk of autonomic dysfunction on 
		cardiovascular health through the perspective of epidemiology and causality.
		The analysis of circadian rhythms through cosinor analysis are built upon 
		the methods by Cornelissen (2014) <doi:10.1186/1742-4682-11-16> and 
		Refinetti, Cornelissen, Halberg (2014) <doi:10.1080/09291010600903692>.
Datasets to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage (2019).
Identification of cardinal dates
             (begin, time of maximum, end of mass developments)
             in ecological time series using fitted Weibull functions.
A collection of simple simulation datasets designed for
    generating Nonlinear Dimension Reduction representations
    techniques such as t-distributed Stochastic Neighbor Embedding,
    and Uniform Manifold Approximation and Projection. These
    datasets serve as a valuable resource for understanding the
    reliability of Nonlinear Dimension Reduction representations in various contexts.
Construct Clinical Data Interchange Standards Consortium
    (CDISC) compliant Analysis Results Data objects. These objects are
    used and re-used to construct summary tables, visualizations, and
    written reports. The package also exports utilities for working with
    these objects and creating new Analysis Results Data objects.
Create extra Analysis Results Data (ARD) summary objects.
    The package supplements the simple ARD functions from the 'cards'
    package, exporting functions to put statistical results in the ARD
    format. These objects are used and re-used to construct summary
    tables, visualizations, and written reports.
Implements the regression approach 
  of Zuber and Strimmer (2011) "High-dimensional regression and variable 
  selection using CAR scores" SAGMB 10: 34, <DOI:10.2202/1544-6115.1730>.
  CAR scores measure the correlation between the response and the 
  Mahalanobis-decorrelated  predictors.  The squared CAR score is a 
  natural measure of variable importance and provides a canonical 
  ordering of variables. This package provides functions for estimating 
  CAR scores, for variable selection using CAR scores, and for estimating 
  corresponding regression coefficients. Both shrinkage as well as 
  empirical estimators are available.
A simulation model and accompanying functions that support 
    assessing silvicultural concepts on the forest estate level with a focus on 
    the CO2 uptake by wood growth and CO2 emissions by forest operations. For 
    achieving this, a virtual forest estate area is split into the areas covered
    by typical phases of the silvicultural concept of interest. Given initial 
    area shares of these phases, the dynamics of these areas is simulated. The 
    typical carbon stocks and flows which are known for all phases are 
    attributed post-hoc to the areas and upscaled to the estate level. CO2 
    emissions by forest operations are estimated based on the amounts and 
    dimensions of the harvested timber. Probabilities of damage events are taken
    into account.
Provides API access to the Government of Canada Vehicle Recalls 
    Database <https://tc.api.canada.ca/en/detail?api=VRDB> used by the Defect 
    Investigations and Recalls Division for vehicles, tires, and child car 
    seats. The API wrapper provides access to recall summary information 
    searched using make, model, and year range, as well as detailed recall 
    information searched using recall number.
When taking online surveys, participants sometimes respond to items
    without regard to their content. These types of responses, referred to as 
    careless or insufficient effort responding, constitute significant problems 
    for data quality, leading to distortions in data analysis and hypothesis 
    testing, such as spurious correlations. The 'R' package 'careless' provides 
    solutions designed to detect such careless / insufficient effort responses 
    by allowing easy calculation of indices proposed in the literature. It 
    currently supports the calculation of longstring, even-odd consistency, 
    psychometric synonyms/antonyms, Mahalanobis distance, and intra-individual 
    response variability (also termed inter-item standard deviation). 
    For a review of these methods, see Curran (2016) <doi:10.1016/j.jesp.2015.07.006>.
Performs a Correspondence Analysis (CA) on a contingency table and creates a scatterplot of the row and column points on the selected dimensions. Optionally, the function can add segments to the plot to visualize significant associations between row and column categories on the basis of positive (unadjusted) standardized residuals larger than a given threshold.
Misc functions for training and plotting classification and
    regression models.
Functions for creating ensembles of caret models: caretList()
    and caretStack().  caretList() is a convenience function for fitting multiple
    caret::train() models to the same dataset. caretStack() will make linear or
    non-linear combinations of these models, using a caret::train() model as a
    meta-model, and caretEnsemble() will make a robust linear combination of
    models using a GLM.
Conformal time series forecasting using the caret infrastructure. 
  It provides access to state-of-the-art machine learning models for forecasting
  applications. The hyperparameter of each model is selected based on time 
  series cross-validation, and forecasting is done recursively.
We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via both frequentist and Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005) <doi:10.1111/j.1467-9868.2005.00522.x> and it involves p+q+2 unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. Also, the model can account for heteroscedastic measurement errors, if the information about measurement error standard deviations is known. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces posterior samples of the model parameters via Metropolis-Hastings within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times.
A framework is provided to develop R packages using 'Rust' <https://www.rust-lang.org/> with
 minimal overhead, and more wrappers are easily added. Help is provided to use 'Cargo' <https://doc.rust-lang.org/cargo/> in a manner
 consistent with CRAN policies. 'Rust' code can also be embedded directly in an R script. The package is not official, affiliated with,
 nor endorsed by the Rust project.
Estimation of population size of migratory caribou herds based on large scale 
             aggregations monitored by radio telemetry. It implements the methodology found 
             in the article by Rivest et al. (1998) about caribou abundance estimation. It 
             also includes a function based on the Lincoln-Petersen Index as applied to 
             radio telemetry data by White and Garrott (1990).
Algorithms to fit Bayesian Conditional Autoregressive LASSO with automatic and adaptive shrinkage described in Shen and Solis-Lemus (2020) <arXiv:2012.08397>.
Includes commands for bootstrapping and permutation tests, a
    command for created grouped bar plots, and a demo of the
    quantile-normal plot for data drawn from different distributions.
Evaluation of the Carlson elliptic integrals and the
    incomplete elliptic integrals with complex arguments. The
    implementations use Carlson's algorithms <doi:10.1007/BF02198293>.
    Applications of elliptic integrals include probability distributions,
    geometry, physics, mechanics, electrodynamics, statistical mechanics,
    astronomy, geodesy, geodesics on conics, and magnetic field
    calculations.
In randomized controlled trial (RCT), balancing covariate is often one of the most important concern. CARM package provides functions to balance the covariates and generate allocation sequence by covariate-adjusted Adaptive Randomization via Mahalanobis-distance (ARM) for RCT. About what ARM is and how it works please see Y. Qin, Y. Li, W. Ma, H. Yang, and F. Hu (2022). "Adaptive randomization via Mahalanobis distance" Statistica Sinica. <doi:10.5705/ss.202020.0440>. In addition, the package is also suitable for the randomization process of multi-arm trials. For details, please see Yang H, Qin Y, Wang F, et al. (2023). "Balancing covariates in multi-arm trials via adaptive randomization" Computational Statistics & Data Analysis.<doi:10.1016/j.csda.2022.107642>.
'Stan' based functions to estimate CAR-MM models. These models allow to estimate Generalised Linear Models with CAR (conditional autoregressive) spatial random effects for spatially and temporally misaligned data, provided a suitable Multiple Membership matrix. The main references are Gramatica, Liverani and Congdon (2023) <doi:10.1214/23-BA1370>, Petrof, Neyens, Nuyts, Nackaerts, Nemery and Faes (2020) <doi:10.1002/sim.8697> and Gramatica, Congdon and Liverani <doi:10.1111/rssc.12480>.
Emulation of an application originally created by Paul Pukite. Computer Aided Rate Modeling and Simulation.  Jan Pukite and Paul Pukite, (1998, ISBN 978-0-7803-3482), William J. Stewart, (1994, ISBN: 0-691-03699-3).
Compute covariate-adjusted specificity at controlled sensitivity level, or covariate-adjusted sensitivity at controlled specificity level, or covariate-adjust receiver operating characteristic curve, or covariate-adjusted thresholds at controlled sensitivity/specificity level. All statistics could also be computed for specific sub-populations given their covariate values. Methods are described in Ziyi Li, Yijian Huang, Datta Patil, Martin G. Sanda (2021+) "Covariate adjustment in continuous biomarker assessment".
The caroline R library contains dozens of functions useful
    for: database migration (dbWriteTable2), database style joins &
    aggregation (nerge, groupBy & bestBy), data structure
    conversion (nv, tab2df), legend table making (sstable &
    leghead), plot annotation (labsegs & mvlabs), data
    visualization (pies, sparge, & raPlot), character string
    manipulation (m & pad), file I/O (write.delim), batch scripting
    and more.  The package's greatest contributions lie in the 
    database style merge, aggregation and interface functions 
    as well as in it's extensive use and propagation of 
    row, column and vector names in most functions.
Mainly used to build tables that are commonly presented for
    bio-medical/health research, such as basic characteristic tables or
    descriptive statistics.
Sending functions to remote processes can be wasteful of
    resources because they carry their environments with them. With the
    carrier package, it is easy to create functions that are isolated from
    their environment. These isolated functions, also called crates, print
    at the console with their total size and can be easily tested locally
    before being sent to a remote.
Predicts categorical or continuous outcomes while concentrating on a number of key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> , Rhemtulla et al. (2012) <doi:10.1037/a0029315>, Riley et al. (2018) <doi:10.1002/sim.7993>, Riley et al. (2019) <doi:10.1002/sim.7992>.
Contains functions to estimate the Correlation-Adjusted Regression Survival (CARS) Scores. The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, <arXiv:1802.08178>.
Functions to prepare and filter an origin-destination matrix for thematic flow mapping purposes.   
             This comes after Bahoken, Francoise (2016), Mapping flow matrix a contribution, PhD in Geography - Territorial sciences. See Bahoken (2017) <doi:10.4000/netcom.2565>.
Construct continuous and non-contiguous area cartograms.
Procedures for making continuous cartogram. Procedures available are:
     flow based cartogram (Gastner & Newman (2004) <doi:10.1073/pnas.0400280101>),
     fast flow based cartogram (Gastner, Seguy & More (2018) <doi:10.1073/pnas.1712674115>),
     rubber band based cartogram (Dougenik et al. (1985)
                        <doi:10.1111/j.0033-0124.1985.00075.x>).
A tool for easily matching spatial data when you have a list of
    place/region names. You might have a data frame that came from a
    spreadsheet tracking some data by suburb or state. This package can
    convert it into a spatial data frame ready for plotting. The actual map
    data is provided by other packages (or your own code).
Create and integrate maps in your R workflow. This package helps 
    to design cartographic representations such as proportional symbols, 
    choropleth, typology, flows or discontinuities maps. It also offers several 
    features that improve the graphic presentation of maps, for instance, map 
    palettes, layout elements (scale, north arrow, title...), labels or legends. 
    See Giraud and Lambert (2017) <doi:10.1007/978-3-319-57336-6_13>.
It provides real-time data from the Casablanca Stock Exchange. The objective is to facilitate access to data for all users of the R programming language. 
             It includes a variety of data accessible just by function call.
A modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.
These experimental expression data (5 leukemic 'CLL' B-lymphocyte of aggressive form from 'GSE39411', <doi:10.1073/pnas.1211130110>), after B-cell receptor stimulation, are used as examples by packages such as the 'Cascade' one, a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.
Provides a cascade select widget for usage in 'Shiny'
    applications. This is useful for selection of hierarchical choices
    (e.g. continent, country, city). It is taken from the 'JavaScript'
    library 'PrimeReact'.
Functions for implementing the novel algorithm CASCORE, which is designed to detect latent community structure in graphs with node covariates. This algorithm can handle models such as the covariate-assisted degree corrected stochastic block model (CADCSBM). CASCORE specifically addresses the disagreement between the community structure inferred from the adjacency information and the community structure inferred from the covariate information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2022) <arXiv:2306.15616>. 
    In addition to CASCORE, this package includes several classical community detection algorithms that are compared to CASCORE in our paper. These algorithms are: Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA, network-based clustering, covariates-based clustering and covariate-assisted spectral clustering (CASC). By providing these additional algorithms, the package enables users to compare their performance with CASCORE in community detection tasks.
It is an open source insurance claim simulation engine sponsored
    by the Casualty Actuarial Society. It generates individual insurance claims 
	including open claims, reopened claims, incurred but not reported claims 
	and future claims. It also includes claim data fitting functions to help set 
	simulation assumptions. It is useful for claim level reserving analysis.
	Parodi (2013) <https://www.actuaries.org.uk/documents/triangle-free-reserving-non-traditional-framework-estimating-reserves-and-reserve-uncertainty>.
Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple 
    competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms 
    of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the 
    fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival 
    curves for a given covariate profile. This approach accommodates any log-linear hazard function of 
    prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide 
    a plot method for visualizing incidence density via population time plots. Based on the case-base sampling 
    approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, 
    and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.
Case-based reasoning is a problem-solving methodology that involves solving a new problem by referring to the solution of a similar problem in a large set of previously solved problems. The key aspect of Case Based Reasoning is to determine the problem that "most closely" matches the new problem at hand. This is achieved by defining a family of distance functions and using these distance functions as parameters for local averaging regression estimates of the final result. The optimal distance function is chosen based on a specific error measure used in regression estimation. This approach allows for efficient problem-solving by leveraging past experiences and adapting solutions from similar cases. The underlying concept is inspired by the work of Dippon J. (2002) <doi:10.1016/S0167-9473(02)00058-0>.
Cox model inference for relative hazard and covariate-specific pure risk estimated 
             from stratified and unstratified case-cohort data as described in 
             Etievant, L., Gail, M.H. (2023) <arXiv:2304.03396>.
Allows users to identify similar cases for qualitative case studies using statistical matching methods.
Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity. (Westphal, Max, and Antonia Zapf. (2021). "Statistical Inference for Diagnostic Test Accuracy Studies with Multiple Comparisons." <arXiv:2105.13469>.)
Play casino games in the R console,
  including poker, blackjack, and a slot machine.
  Try to build your fortune before you succumb to the gambler's ruin!
A significant pattern mining-based toolbox for region-based genome-wide association studies and higher-order epistasis analyses, implementing the methods described in Llinares-López et al. (2017) <doi:10.1093/bioinformatics/btx071>.
Contains Coverage Adjusted Standardized Mutual Information ('CASMI')-based functions. 'CASMI' is a fundamental concept of a series of methods. For more information about 'CASMI' and 'CASMI'-related methods, please refer to the corresponding publications (e.g., a feature selection method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.3390/e21121179>, and a dataset quality measurement method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.1109/ICHI.2019.8904553>) or contact the package author for the latest updates.
Computes a range of scatterplot diagnostics (scagnostics) on pairs
  of numerical variables in a data set. A range of scagnostics, including graph
  and association-based scagnostics described by Leland Wilkinson and Graham
  Wills (2008) <doi:10.1198/106186008X320465> and association-based
  scagnostics described by Katrin Grimm (2016,ISBN:978-3-8439-3092-5) can be 
  computed. Summary and plotting functions are provided.
Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; Milà et al. (2022) <doi:10.1111/2041-210X.13851>; Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>; Linnenbrink et al. (2023) <doi:10.5194/egusphere-2023-1308>. The package is described in detail in Meyer et al. (2024) <doi:10.48550/arXiv.2404.06978>.
Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.
Performs analysis of categorical-variable with missing values. Implements methods from Schafer, JL, Analysis of Incomplete Multivariate Data, Chapman and Hall.
Unifying an inconsistently coded categorical variable between two different time points in accordance with a mapping table. 
    The main rule is to replicate the observation if it could be assigned to a few categories. 
    Then using frequencies or statistical methods to approximate the probabilities of being assigned to each of them. 
    This procedure was invented and implemented in the paper by Nasinski, Majchrowska, and Broniatowska (2020) <doi:10.24425/cejeme.2020.134747>.
Functions for analyzing check-all-that-apply (CATA) data from consumer and sensory tests. Cochran's Q test, McNemar's test, and Penalty-Lift analysis provided, as described in for CATA data analysis by Meyners, Castura & Carr (2013) <doi:10.1016/j.foodqual.2013.06.010>. Cluster analysis can be performed using b-cluster analysis. The quality of cluster analysis solutions can be evaluated using various measures. The methods related to b-cluster analysis are described in a manuscript by Castura, Meyners, Varela & Næs (2022) <doi:10.1016/j.foodqual.2022.104564>. Methods are adapted to product-related hedonic responses by Castura, Meyners, Pohjanheimo, Varela & Næs (2023) <doi:10.1111/joss.12860>.
Gain access to the 'Spark Catalog' API making use of the 'sparklyr' API. 'Catalog' <https://spark.apache.org/docs/2.4.3/api/java/org/apache/spark/sql/catalog/Catalog.html> is the interface for managing a metastore (aka metadata catalog) of relational entities (e.g. database(s), tables, functions, table columns and temporary views).
Access public spatial data available under the 'INSPIRE'
    directive. Tools for downloading references and addresses of
    properties, as well as map images.
Performs classification and variable selection on high-dimensional tensors (multi-dimensional arrays) after adjusting for additional covariates (scalar or vectors) as CATCH model in Pan, Mai and Zhang (2018) <arXiv:1805.04421>. The low-dimensional covariates and the high-dimensional tensors are jointly modeled to predict a categorical outcome in a multi-class discriminant analysis setting. The Covariate-Adjusted Tensor Classification in High-dimensions (CATCH) model is fitted in two steps: (1) adjust for the covariates within each class; and (2) penalized estimation with the adjusted tensor using a cyclic block coordinate descent algorithm. The package can provide a solution path for tuning parameter in the penalized estimation step. Special case of the CATCH model includes linear discriminant analysis model and matrix (or tensor) discriminant analysis without covariates.
Methods and utilities for testing, identifying, selecting and  
    mutating objects as categorical or continous types. These functions work on both 
    atomic vectors as well as recursive objects: data.frames, data.tables, 
    tibbles, lists, etc..
Categorical data analysis by AIC. The methodology is described in
 Sakamoto (1992) <ISBN 978-0-7923-1429-5>.
This R-package contains examples from the book "Regression for Categorical Data", Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.
Datasets used in the book "Categorical Data Analysis"
   by Agresti (2012, ISBN:978-0-470-46363-5) but not printed in the book.
   Datasets and help pages were automatically produced from the source
   <https://users.stat.ufl.edu/~aa/cda/data.html> by the R script foo.R,
   which can be found in the GitHub repository.
Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 100 types of models of increasing complexity, and 72 likelihood models for the data.
Provides several methods for factor analysis in high dimension (both n,p >> 1) and methods to adjust for possible confounders in multiple hypothesis testing.
Simple, fast, and automatic encodings for category data using 
             a data.table backend. Most of the methods are an implementation 
             of "Sufficient Representation for Categorical Variables" by
             Johannemann, Hadad, Athey, Wager (2019) <arXiv:1908.09874>,
             particularly their mean, sparse principal component analysis, 
             low rank representation, and multinomial logit encodings.
Contains some commonly used categorical variable encoders, such as 'LabelEncoder' and 'OneHotEncoder'. Inspired by the encoders implemented in Python 'sklearn.preprocessing' package (see <http://scikit-learn.org/stable/modules/preprocessing.html>).
Includes wrapper functions around existing functions for the analysis of categorical data and introduces functions for calculating risk differences and matched odds ratios. R currently supports a wide variety of tools for the analysis of categorical data. However, many functions are spread across a variety of packages with differing syntax and poor compatibility with each another. prop_test() combines the functions binom.test(), prop.test() and BinomCI() into one output. prop_power() allows for power and sample size calculations for both balanced and unbalanced designs. riskdiff() is used for calculating risk differences and matched_or() is used for calculating matched odds ratios. For further information on methods used that are not documented in other packages see Nathan Mantel and William Haenszel (1959) <doi:10.1093/jnci/22.4.719> and Alan Agresti (2002) <ISBN:0-471-36093-7>.
Detect and quantify community assembly processes using trait values of individuals or populations, the T-statistics and other metrics, and dedicated null models.
Functions designed to simulate data that conform to basic
          unidimensional IRT models (for now 3-parameter binary response models
          and graded response models) along with Post-Hoc CAT simulations of
          those models given various item selection methods, ability estimation
          methods, and termination criteria. See
          Wainer (2000) <doi:10.4324/9781410605931>,
          van der Linden & Pashley (2010) <doi:10.1007/978-0-387-85461-8_1>,
          and Eggen (1999) <doi:10.1177/01466219922031365> for more details.
Formal psychological models of categorization and learning, independently-replicated data sets against which to test them, and simulation archives.
Although many software tools can perform meta-analyses on genetic case-control data,
    none of these apply to combined case-control and family-based (TDT) studies. This package conducts
    fixed-effects (with inverse variance weighting) and random-effects [DerSimonian and Laird (1986)
    <DOI:10.1016/0197-2456(86)90046-2>] meta-analyses on combined genetic data. Specifically, this package
    implements a fixed-effects model [Kazeem and Farrall (2005) <DOI:10.1046/j.1529-8817.2005.00156.x>]
    and a random-effects model [Nicodemus (2008) <DOI:10.1186/1471-2105-9-130>] for combined studies.
Methods and plotting functions for displaying categorical data on an 
             interactive heatmap using 'plotly'. Provides functionality for strictly 
             categorical heatmaps, heatmaps illustrating categorized continuous data 
             and annotated heatmaps. Also, there are various options to interact with the x-axis
             to prevent overlapping axis labels, e.g. via simple sliders or range sliders. 
             Besides the viewer pane, resulting plots can be saved as a standalone HTML file, 
             embedded in 'R Markdown' documents or in a 'Shiny' app.
Contains several basic utility functions including: moving
        (rolling, running) window statistic functions, read/write for
        GIF and ENVI binary files, fast calculation of AUC, LogitBoost
        classifier, base64 encoder/decoder, round-off-error-free sum
        and cumsum, etc.
Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. I Barrio, I Arostegui, MX Rodriguez-Alvarez, JM Quintana (2015) <doi:10.1177/0962280215601873>. I Barrio,  MX Rodriguez-Alvarez, L Meira-Machado, C Esteban, I Arostegui (2017) <https://www.idescat.cat/sort/sort411/41.1.3.barrio-etal.pdf>.
Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) <doi:10.18637/jss.v076.c01>).
Computes solutions for linear and logistic regression models with potentially high-dimensional categorical predictors. This is done by applying a nonconvex penalty (SCOPE) and computing solutions in an efficient path-wise fashion. The scaling of the solution paths is selected automatically. Includes functionality for selecting tuning parameter lambda by k-fold cross-validation and early termination based on information criteria. Solutions are computed by cyclical block-coordinate descent, iterating an innovative dynamic programming algorithm to compute exact solutions for each block.
Cohort plAtform Trial Simulation whereby every cohort consists of two arms, 
             control and experimental treatment. Endpoints are co-primary binary endpoints and
             decisions are made using either Bayesian or frequentist decision rules. 
             Realistic trial trajectories are simulated and the operating characteristics of the 
             designs are calculated.
Provides the tools to produce catseye plots, principally
    by catseyesplot() function which calls R's standard plot() function internally, or alternatively
    by the catseyes() function to overlay the catseye plot onto an existing
    R plot window. Catseye plots illustrate the normal distribution of the mean (picture a 
    normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence
    interval; they are an intuitive way of illustrating and comparing normally distributed estimates,
    and are arguably a superior alternative to standard confidence intervals, since they show the full
    distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require
    pre-calculated means and standard errors (or standard deviations), provided as numeric vectors;
    this allows the flexibility of obtaining this information from a variety of sources, such as
    direct calculation or prediction from a model.  Catseye plots, as illustrations of the
    normal distribution of the means, are described in Cumming (2013 & 2014).
    Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629.
Computes a structural similarity metric (after the style of
    MS-SSIM for images) for binary and categorical 2D and 3D images. Can be
    based on accuracy (simple matching), Cohen's kappa, Rand index, adjusted
    Rand index, Jaccard index, Dice index, normalized mutual information, or
    adjusted mutual information. In addition, has fast computation
    of Cohen's kappa, the Rand indices, and the two mutual informations.
    Implements the methods of Thompson and Maitra (2020) <arXiv:2004.09073>.
Provides methods of computerized adaptive testing for survey researchers.  See Montgomery and Rossiter (2020) <doi:10.1093/jssam/smz027>. Includes functionality for data fit with the classic item response methods including the latent trait model, Birnbaum`s three parameter model, the graded response, and the generalized partial credit model.  Additionally, includes several ability parameter estimation and item selection routines.  During item selection, all calculations are done in compiled C++ code.
This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.
Provides functions for computing the one-sided p-values of the Cochran-Armitage trend test
  statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test
  is performed using an algorithm following an idea by Mehta, et al. (1992) <doi:10.2307/1390598>.
An omnibus test of change-point Cox regression models to improve the statistical power of detecting signals of non-proportional hazards patterns. The technical details can be found in Hong Zhang, Qing Li, Devan Mehrotra and Judong Shen (2021) <arXiv:2101.00059>. Extensive simulation studies demonstrate that, compared to existing tests under non-proportional hazards, the proposed CauchyCP test 1) controls the type I error better at small alpha levels; 2) increases the power of detecting time-varying effects; and 3) is more computationally efficient.
A new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. <doi:10.1007/s11222-023-10328-x>.
The Cauchy Process can model pulsed continuous trait evolution
    on phylogenies. The likelihood is tractable, and is used for parameter
    inference and ancestral trait reconstruction.
    See Bastide and Didier (2023) <doi:10.1093/sysbio/syad053>.
Accelerate Bayesian analytics workflows in 'R' through interactive modelling,
    visualization, and inference. Define probabilistic graphical models using directed
    acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, 
    and programmers. This package relies on interfacing with the 'numpyro' python package.
We implement causal decomposition analysis using the methods proposed by Park, Lee, and Qin (2020) and Park, Kang, and Lee (2021+) <arXiv:2109.06940>. This package allows researchers to use the multiple-mediator-imputation, single-mediator-imputation, and product-of-coefficients regression methods to estimate the initial disparity, disparity reduction, and disparity remaining. It also allows to make the inference conditional on baseline covariates. We also implement sensitivity analysis for the causal decomposition analysis using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023).
Software which provides numerous functionalities for detecting and removing group-level effects from high-dimensional scientific data which, when combined with additional assumptions, allow for causal conclusions, as-described in our manuscripts Bridgeford et al. (2024) <doi:10.1101/2021.09.03.458920> and Bridgeford et al. (2023) <arXiv:2307.13868>. Also provides a number of useful utilities for generating simulations and balancing covariates across multiple groups/batches of data via matching and propensity trimming for more than two groups.
Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) <doi:10.1038/s41467-022-35157-w>.
Example data sets to run the example
    problems from causal inference textbooks. Currently, contains data
    sets for Huntington-Klein, Nick (2021) "The Effect" <https://theeffectbook.net>,
    Cunningham, Scott (2021, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", 
    and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" 
    <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>.
Various tools for inferring causal models from observational data. The package 
    includes an implementation of the temporal Peter-Clark (TPC) algorithm. Petersen, Osler 
    and Ekstrøm (2021) <doi:10.1093/aje/kwab087>. It also includes general tools
    for evaluating differences in adjacency matrices, which can be used for evaluating
    performance of causal discovery procedures.
Functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments.  A description of the methods is given in Galagate (2016) <https://drum.lib.umd.edu/handle/1903/18170>.
Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) <http://ftp.cs.ucla.edu/pub/stat_ser/r329-uai.pdf>, an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>. All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) <http://ftp.cs.ucla.edu/pub/stat_ser/r309.pdf>.
Implements various estimators for average
  treatment effects - an inverse probability weighted (IPW) estimator, 
  an augmented inverse probability weighted (AIPW) estimator, and a standard
  regression estimator - that make use of generalized additive models for
  the treatment assignment model and/or outcome model. See: Glynn, Adam N.
  and Kevin M. Quinn. 2010. "An Introduction to the Augmented Inverse
  Propensity Weighted Estimator." Political Analysis. 18: 36-56.
Provides a framework for estimating causal effects of a continuous 
    exposure using observational data, and implementing matching and weighting 
    on the generalized propensity score.
    Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. 
    Matching on generalized propensity scores with continuous exposures. Journal 
    of the American Statistical Association, pp.1-29.
Draws causal hypergraph plots from models output by configurational comparative methods such as Coincidence Analysis (CNA) or Qualitative Comparative Analysis (QCA).
Implements a Bayesian approach to causal impact estimation in time
  series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>.
  See the package documentation on GitHub
  <https://google.github.io/CausalImpact/> to get started.
Infers the causal effect of an intervention on a multivariate response through the use of Multivariate 
    Bayesian Structural Time Series models (MBSTS) as described in Menchetti & Bojinov (2020) <arXiv:2006.12269>. 
    The package also includes functions for model building and forecasting.
Provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset, including those of Dahabreh et al. (2019) <doi:10.1111/biom.13716> and Robertson et al. (2021) <arXiv:2104.05905>. The multi-source data can be a collection of trials, observational studies, or a combination of both, which have the same data structure (outcome, treatment, and covariates). The target population can be based on an internal dataset or an external dataset where only covariate information is available. The causal estimands available are average treatment effects and subgroup treatment effects.
Provides an array of statistical models common in causal inference such as 
  standardization, IP weighting, propensity matching, outcome regression, and doubly-robust 
  estimators. Estimates of the average treatment effects from each model are given with the 
  standard error and a 95% Wald confidence interval (Hernan, Robins (2020) <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>).
When causal quantities are not identifiable from the observed data, it still may be possible 
            to bound these quantities using the observed data. We outline a class of problems for which the 
            derivation of tight bounds is always a linear programming problem and can therefore, at least 
            theoretically, be solved using a symbolic linear optimizer. We extend and generalize the 
            approach of Balke and Pearl (1994) <doi:10.1016/B978-1-55860-332-5.50011-0> and we provide 
            a user friendly graphical interface for setting up such problems via directed acyclic 
            graphs (DAG), which only allow for problems within this class to be depicted. The user can 
            then define linear constraints to further refine their assumptions to meet their specific 
            problem, and then specify a causal query using a text interface. The program converts this 
            user defined DAG, query, and constraints, and returns tight bounds. The bounds can be 
            converted to R functions to evaluate them for specific datasets, and to latex code for 
            publication. The methods and proofs of tightness and validity of the bounds are described in
            a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) 
            <doi:10.1080/10618600.2022.2071905>.
Uses optimal transport distances to find probabilistic 
    matching estimators for causal inference.
    These methods are described in Dunipace, Eric (2021) <arXiv:2109.01991>.
    The package will build the weights, estimate treatment effects, and
    calculate confidence intervals via the methods described in the paper.
    The package also supports several other methods as described in the help 
    files.
Calculates population attributable fraction causal effects.
    The 'causalPAF' package contains a suite of functions for causal
    analysis calculations of population attributable fractions (PAF) given
    a causal diagram which apply both: Pathway-specific population
    attributable fractions (PS-PAFs) O’Connell and Ferguson (2022)
    <doi:10.1093/ije/dyac079> and Sequential population attributable
    fractions Ferguson, O’Connell, and O’Donnell (2020)
    <doi:10.1186/s13690-020-00442-x>.  Results are presentable in both
    table and plot format.
Users can declare binary causal models, update beliefs about causal types given data and calculate arbitrary estimands.  Model definition makes use of 'dagitty' functionality. Updating is implemented in 'stan'. The approach used in 'CausalQueries' is a generalization of the 'biqq' models described in "Mixing Methods: A Bayesian Approach" (Humphreys and Jacobs, 2015, <DOI:10.1017/S0003055415000453>). The conceptual extension makes use of work on probabilistic causal models described in Pearl's Causality (Pearl, 2009, <DOI:10.1017/CBO9780511803161>).
The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects.
Several causal effects are measured using least squares regressions and basis function approximations. Backward and forward selection methods based on different criteria are used to select the basis functions.
Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) <doi:10.1016/j.jeconom.2006.06.004>, Huber (2012) <doi:10.3102/1076998611411917>, Huber (2014) <doi:10.1080/07474938.2013.806197>, Huber (2014) <doi:10.1002/jae.2341>, Froelich and Huber (2017) <doi:10.1111/rssb.12232>, Hsu, Huber, Lee, and Lettry (2020)  <doi:10.1002/jae.2765>, and others.
This tool performs pairwise correlation analysis and estimate causality.
    Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria.
Design and use of control charts for detecting mean changes 
 based on a delayed updating of the in-control parameter estimates.
 See Capizzi and Masarotto (2019) <doi:10.1080/00224065.2019.1640096> for the
 description of the method.
Provides six variants of two-way correspondence analysis (ca):
   simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca,
   singly ordered non symmetrical ca, and doubly ordered non symmetrical
   ca.
Cluster analysis is performed using pairwise distance information and a random partition distribution. The method is
             implemented for two random partition distributions. It draws samples and then obtains and plots clustering estimates.
             An implementation of a selection algorithm is provided for the mass parameter of the partition distribution. Since 
             pairwise distances are the principal input to this procedure, it is most comparable to the hierarchical and k-medoids
             clustering methods. The method is Dahl, Andros, Carter (2022+) <doi:10.1002/sam.11602>.
Provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis. 
  Details are in Jeong et al. (2019) <doi:10.1101/gr.245571.118> and Baggerly et al. (2003) <doi:10.1093/bioinformatics/btg173>.
Implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.
A set of functions that helps you to generate descriptive statistics based on the variable types.
Fit multiclass Classification version of Bayesian Adaptive Smoothing Splines (CBASS) to data using reversible jump MCMC. The multiclass classification problem consists of a response variable that takes on unordered categorical values with at least three levels, and a set of inputs for each response variable. The CBASS model consists of a latent multivariate probit formulation, and the means of the latent Gaussian random variables are specified using adaptive regression splines. The MCMC alternates updates of the latent Gaussian variables and the spline parameters. All the spline parameters (variables, signs, knots, number of interactions), including the number of basis functions used to model each latent mean, are inferred. Functions are provided to process inputs, initialize the chain, run the chain, and make predictions. Predictions are made on a probabilistic basis, where, for a given input, the probabilities of each categorical value are produced. See Marrs and Francom (2023) "Multiclass classification using Bayesian multivariate adaptive regression splines" Under review.
Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis.
Design and evaluate choice-based conjoint survey experiments. Generate a variety of survey designs, including random designs, full factorial designs, orthogonal designs, D-optimal designs, and Bayesian D-efficient designs as well as designs with "no choice" options and "labeled" (also known as "alternative specific") designs. Conveniently inspect the design balance and overlap, and simulate choice data for a survey design either randomly or according to a multinomial or mixed logit utility model defined by user-provided prior parameters. Conduct a power analysis for a given survey design by estimating the same model on different subsets of the data to simulate different sample sizes. Full factorial and orthogonal designs are obtained using the 'DoE.base' package (Grömping, 2018) <doi:10.18637/jss.v085.i05>. D-optimal designs are obtained using the 'AlgDesign' package (Wheeler, 2022) <https://CRAN.R-project.org/package=AlgDesign>. Bayesian D-efficient designs are obtained using the 'idefix' package (Traets et al, 2020) <doi:10.18637/jss.v096.i03>. Choice simulation and model estimation in power analyses are handled using the 'logitr' package (Helveston, 2023) <doi:10.18637/jss.v105.i10>.
Implementation of the d/p/q/r family of functions for a continuous analog to the standard discrete binomial with continuous size parameter and continuous support with x in [0, size + 1], following Ilienko (2013) <arXiv:1303.5990>.
Provides R users with direct access to genomic and clinical data from the 'cBioPortal' web resource via user-friendly
    functions that wrap 'cBioPortal's' existing API endpoints <https://www.cbioportal.org/api/swagger-ui/index.html>. Users can browse and query genomic data on mutations,
    copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'),
    microsatellite instability status ('MSI'), 'FACETS' and select clinical data points (depending on the study). 
    See <https://www.cbioportal.org/> and Gao et al., (2013) <doi:10.1126/scisignal.2004088> for more information on the cBioPortal web resource.
Methods for learning causal relationships among a set of
    foreground variables X based on signals from a (potentially much
    larger) set of background variables Z, which are known non-descendants
    of X. The confounder blanket learner (CBL) uses sparse regression
    techniques to simultaneously perform many conditional independence
    tests, with complementary pairs stability selection to guarantee
    finite sample error control. CBL is sound and complete with respect to
    a so-called "lazy oracle", and works with both linear and nonlinear
    systems. For details, see Watson & Silva (2022) <arXiv:2205.05715>.
A function that facilitates fitting three types of models 
    for contrast-based Bayesian Network Meta Analysis.  The first model is that which
    is described in Lu and Ades (2006) <doi:10.1198/016214505000001302>.  The other two 
    models are based on a Bayesian nonparametric methods that permit ties when comparing 
    treatment or for a treatment effect to be exactly equal to zero. In addition to the 
    model fits, the package provides a summary of the interplay  between treatment 
    effects based on the procedure described in Barrientos, Page, and Lin (2023)
    <doi:10.48550/arXiv.2207.06561>.
Implements the covariate balancing propensity score (CBPS) proposed
    by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. The propensity score is
    estimated such that it maximizes the resulting covariate balance as well as the
    prediction of treatment assignment. The method, therefore, avoids an iteration
    between model fitting and balance checking.  The package also implements optimal
    CBPS from Fan et al. (in-press) <DOI:10.1080/07350015.2021.2002159>,  
    several extensions of the CBPS beyond the cross-sectional, binary treatment setting.
    They include the CBPS for longitudinal settings so that it can be used in 
    conjunction with marginal structural models from Imai and Ratkovic (2015) 
    <DOI:10.1080/01621459.2014.956872>, treatments with three- and four-valued treatment 
    variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018) 
    <DOI:10.1214/17-AOAS1101>, propensity score estimation with a large number of 
    covariates from Ning, Peng, and Imai (2020) <DOI:10.1093/biomet/asaa020>, and the situation 
    with multiple distinct binary treatments administered simultaneously. In the future 
    it will be extended to other settings including the generalization of experimental 
    and instrumental variable estimates.
Estimates conditional binary quantile models developed by Lu (2020) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.
The data and meta data from Statistics
    Netherlands (<https://www.cbs.nl>) can be browsed and downloaded. The client uses
    the open data API of Statistics Netherlands.
Uses monotonically constrained Cubic Bezier Splines (CBS) to approximate latent utility functions in intertemporal choice and risky choice data. For more information, see Lee, Glaze, Bradlow, and Kable <doi:10.1007/s11336-020-09723-4>.
The Confidence Bound Target (CBT) algorithm is designed for infinite arms bandit problem. It is shown that CBT algorithm achieves the regret lower bound for general reward distributions. Reference: Hock Peng Chan and Shouri Hu (2018) <arXiv:1805.11793>.
Tools for creating and visualizing statistical process control charts. Control charts are used for monitoring measurement processes, such as those occurring in manufacturing.  The objective is to monitor the history of such processes and flag outlying measurements: out-of-control signals.  Montgomery, D. (2009, ISBN:978-0-470-16992-6) contains an extensive discussion of the methodology.
Provides a set of functions that extend the
        'cancor' function with new numerical and graphical outputs. It
        also include a regularized extension of the canonical
        correlation analysis to deal with datasets with more variables
        than observations.
Loads and creates spatial data, including layers and tools that are relevant
    to the activities of the Commission for the Conservation of Antarctic Marine Living 
    Resources. Provides two categories of functions: load functions and create functions.
    Load functions are used to import existing spatial layers from the online CCAMLR GIS
    such as the ASD boundaries. Create functions are used to create layers from user data
    such as polygons and grids.
Canonical correlation analysis and maximum correlation via
    projection pursuit, as well as fast implementations of correlation
    estimators, with a focus on robust and nonparametric methods.
Class Cover Catch Digraphs, neighborhood graphs, and
        relatives.
Calculates the credit debt for the next period based on the available data using the cross-classification credibility model.
Routines for solving convex optimization problems with cone constraints by means of interior-point methods. The implemented algorithms are partially ported from CVXOPT, a Python module for convex optimization (see <https://cvxopt.org> for more information).
Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. It also includes the estimation of the one-way intraclass correlation coefficient also known as reliability index. The estimation approaches implemented are variance components and U-statistics approaches. Description of methods can be found in Fleiss (1986) <doi:10.1002/9781118032923> and Carrasco et al. (2013) <doi:10.1016/j.cmpb.2012.09.002>.
Implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. <doi:10.1016/j.envsoft.2014.01.010>.
Access chemical, hazard, and bioactivity data from the Center for
   Computational Toxicology and Exposure ('CCTE') APIs 
   <https://api-ccte.epa.gov/docs/>. 'ccdR' was developed to streamline the 
   process of accessing the information available through the 'CCTE' APIs 
   without requiring prior knowledge of how to use APIs. All data is also 
   available on the CompTox Chemical Dashboard ('CCD')
   <https://comptox.epa.gov/dashboard/>.
Contains a function, also called 'cchs', that calculates Estimator III of Borgan et al (2000), <DOI:10.1023/A:1009661900674>. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling.
Supporting the use of the Canadian Community Health Survey 
             (CCHS) by transforming variables from each cycle into harmonized, 
             consistent versions that span survey cycles (currently, 2001 to 
             2018). CCHS data used in this library is accessed and adapted in 
             accordance to the Statistics Canada Open Licence Agreement. This 
             package uses rec_with_table(), which was developed from 'sjmisc' 
             rec(). Lüdecke D (2018). "sjmisc: Data and Variable Transformation 
             Functions". Journal of Open Source Software, 3(26), 754. 
             <doi:10.21105/joss.00754>.
Provides efficient implementation of the Cross-Covariance
    Isolate Detect (CCID) methodology for the estimation of the number
    and location of multiple change-points in the second-order
    (cross-covariance or network) structure of multivariate, possibly
    high-dimensional time series. The method is motivated by the detection
    of change points in functional connectivity networks for functional
    magnetic resonance imaging (fMRI), electroencephalography (EEG),
    magentoencephalography (MEG) and electrocorticography (ECoG) data. The
    main routines in the package have been extensively tested on fMRI data. 
    For details on the CCID methodology, please see Anastasiou et
    al (2022), Cross-covariance isolate detect: A new change-point method for
    estimating dynamic functional connectivity. Medical Image Analysis, Volume
    75.
Convex Clustering methods, including K-means algorithm,
  On-line Update algorithm (Hard Competitive Learning) and Neural Gas
  algorithm (Soft Competitive Learning), and calculation of several
  indexes for finding the number of clusters in a data set.
Classification method described in Dancik et al (2011) <doi:10.1158/0008-5472.CAN-11-2427> that classifies a sample according to the class with the maximum mean (or any other function of) correlation between the test and training samples with known classes.
Functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020)<doi:10.1017/pan.2019.31>.
Consensus clustering, also called meta-clustering or cluster ensembles, has been increasingly 
    used in clinical data. Current consensus clustering methods tend to ensemble a number of different 
    clusters from mathematical replicates with similar sample coverage. As the fact of common variety
    of sample coverage in the real-world data, a new consensus clustering strategy dealing with
    such biological replicates is required. This is a two-step consensus clustering package, which
    is used to input multiple predictive labels with different sample coverage (missing labels).
Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision).
Implements the convex clustering through majorization-minimization (CCMM) algorithm described in Touw, Groenen, and Terada (2022) <arXiv:2211.01877> to perform minimization of the convex clustering loss function.
Provides a set of functions to perform queries against the 'CCM' API <https://mohcontacttracing.my.salesforce.com>.
Cases are matched to controls in an efficient, optimal and computationally flexible way. It uses 
             the idea of sub-sampling in the level of the case, by creating pseudo-observations of controls. The 
             user can select between replacement and without replacement, the number of controls, and several covariates 
             to match upon. See Mamouris (2021) <doi:10.1186/s12874-021-01256-3> for an overview.
Implementation of estimators for inferring 
    the mean of censored cost data. Including the estimators
    BT from Bang and Tsiatis (2000) <doi:10.1093/biomet/87.2.329> and 
    ZT from Zhao and Tian (2001) <doi:10.1111/j.0006-341X.2001.01002.x>.
Significance tests are provided for canonical correlation analysis, including asymptotic tests and a Monte Carlo method.
With the development of new cross-cultural methods this package is intended to combine multiple functions automating and simplifying functions providing a unified analysis approach for commonly employed methods.
Implements a method for identifying and removing
				the cell-cycle effect from scRNA-Seq data. The description of the 
				method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing 
				the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. 
				Different from previous methods, ccRemover implements a mechanism that
				formally tests whether a component is cell-cycle related or not, and thus
				while it often thoroughly removes the cell-cycle effect, it preserves
				other features/signals of interest in the data.
Functions for performing Correcting and Clustering response-style-biased preference data (CCRS). The main functions are correct.RS() for correcting for response styles, and ccrs() for simultaneously correcting and content-based clustering. The procedure begin with making rank-ordered boundary data from the given preference matrix using a function called create.ccrsdata(). Then in correct.RS(), the response style is corrected as follows: the rank-ordered boundary data are smoothed by I-spline functions, the given preference data are transformed by the smoothed functions. The resulting data matrix, which is considered as bias-corrected data, can be used for any data analysis methods. If one wants to cluster respondents based on their indicated preferences (content-based clustering), ccrs() can be applied to the given (response-style-biased) preference data, which simultaneously corrects for response styles and clusters respondents based on the contents. Also, the correction result can be checked by plot.crs() function.
A set of radiative transfer models to quantitatively describe the absorption, reflectance and transmission of solar energy in vegetation, and model remotely sensed spectral signatures of vegetation at distinct spatial scales (leaf,canopy and stand). The main principle behind ccrtm is that many radiative transfer models can form a coupled chain, basically models that feed into each other in a linked chain (from leaf, to canopy, to stand, to atmosphere). It allows the simulation of spectral datasets in the solar spectrum (400-2500nm) using leaf models as PROSPECT5, 5b, and D which can be coupled with canopy models as 'FLIM', 'SAIL' and 'SAIL2'. Currently, only a simple atmospheric model ('skyl') is implemented. Jacquemoud et al 2008 provide the most comprehensive overview of these models <doi:10.1016/j.rse.2008.01.026>.
Draws systematic samples from a population that follows linear trend. The function returns a matrix comprising of the required samples as its column vectors. The samples produced are highly efficient and the inter sampling variance is minimum. The scheme will be useful in various field like Bioinformatics where the samples are expensive and must be precise in reflecting the population by possessing least sampling variance.
CUR/CX decomposition factorizes a matrix into two factor matrices and Multidimensional CX Decomposition factorizes a tensor into a core tensor and some factor matrices. See the reference section of GitHub README.md <https://github.com/rikenbit/ccTensor>, for details of the methods.
Implements the uniform scaled beta distribution and
  the continuous convolution kernel density estimator.
Consensus analysis, model-based clustering, and cultural consensus theory applications to response data (e.g. questionnaires). The models are applied using hierarchical Bayesian inference. The current package version supports binary, ordinal, and continuous data formats.
Automated assessment and selection of weighting factors for accurate quantification using linear calibration curve. 
 In addition, a 'shiny' App is provided, allowing users to analyze their data using an interactive graphical user interface, without any programming requirements.
Duplicated music data (pre-processed and formatted) for entity resolution. The total size of the data set is 9763. There are respective gold standard records that are labeled and can be considered as a unique identifier.
Supplies higher-order coordinatized data specification and fluid transform operators that include pivot and anti-pivot as special cases. 
    The methodology is describe in 'Zumel', 2018, "Fluid data reshaping with 'cdata'", <https://winvector.github.io/FluidData/FluidDataReshapingWithCdata.html> , <DOI:10.5281/zenodo.1173299> .
    This package introduces the idea of explicit control table specification of data transforms.
    Works on in-memory data or on remote data using 'rquery' and 'SQL' database interfaces.
Likelihood-based estimation and data generation from a class of models used to estimate peer effects on count data by controlling for the network endogeneity. This class includes count data models with social interactions (Houndetoungan 2023; <doi:10.2139/ssrn.3721250>), spatial tobit models (Xu and Lee 2015; <doi:10.1016/j.jeconom.2015.05.004>), and spatial linear-in-means models (Lee 2004; <doi:10.1111/j.1468-0262.2004.00558.x>).
Provides a set of functions for conducting cognitive diagnostic computerized adaptive testing applications (Chen, 2009) <DOI:10.1007/s11336-009-9123-2>). It includes different item selection rules such us the global discrimination index (Kaplan, de la Torre, and Barrada (2015) <DOI:10.1177/0146621614554650>) and the nonparametric selection method (Chang, Chiu, and Tsai (2019) <DOI:10.1177/0146621618813113>), as well as several stopping rules. Functions for generating item banks and responses are also provided. To guide item bank calibration, model comparison at the item level can be conducted using the two-step likelihood ratio test statistic by Sorrel, de la Torre, Abad and Olea (2017) <DOI:10.1027/1614-2241/a000131>.
Allows users to seamlessly query several 'CDC PLACES' APIs (<https://data.cdc.gov/browse?q=PLACES%20&sortBy=relevance>)
    by geography, state, measure, and release year. This package also contains a
    function to explore the available measures for each release year.
Conditional distance correlation <doi:10.1080/01621459.2014.993081> is a novel conditional dependence measurement of two multivariate random variables given a confounding variable. This package provides conditional distance correlation, performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data <http://www3.stat.sinica.edu.tw/statistica/J28N1/J28N114/J28N114.html>, and conducts conditional distance covariance test for conditional independence assumption of two multivariate variable.
Connect to the California Data Exchange Center (CDEC) 
    Web Service <http://cdec.water.ca.gov/>. 'CDEC' provides a centralized 
    database to store, process, and exchange real-time hydrologic information 
    gathered by various cooperators throughout California. The 'CDEC' Web Service 
    <http://cdec.water.ca.gov/dynamicapp/wsSensorData> provides a data download 
    service for accessing historical records.
Employs a two-parameter family of
    distributions for modelling random variables on the (0, 1) interval by
    applying the cumulative distribution function (cdf) of one parent
    distribution to the quantile function of another.
Statistical downscaling and bias correction (model output statistics) method based on cumulative distribution functions (CDF) transformation. See Michelangeli, Vrac, Loukos (2009) Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophysical Research Letters, 36, L11708, <doi:10.1029/2009GL038401>. ; and Vrac, Drobinski, Merlo, Herrmann, Lavaysse, Li, Somot (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat. Hazards Earth Syst. Sci., 12, 2769-2784, www.nat-hazards-earth-syst-sci.net/12/2769/2012/, <doi:10.5194/nhess-12-2769-2012>.
The framework of causal decomposition of group disparities developed by
    Yu and Elwert (2023)
    <doi:10.48550/arXiv.2306.16591>.
    This package implements the decomposition estimators
    that are based on efficient influence functions. For the
    nuisance functions of the estimators, both parametric and
    nonparametric options are provided, as well as manual options in case
    the default models are not satisfying.
The concept of cause-deleted life expectancy improvement is statistic designed to quantify the increase in life expectancy if a certain cause of death is removed. See Adamic, P. (2015) (<https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2689352>).
Downloads USDA National Agricultural Statistics Service (NASS) 
    cropscape data for a specified state. Utilities for fips, abbreviation, 
    and name conversion are also provided. Full functionality requires an 
    internet connection, but data sets can be cached for later off-line use.
Functions for cognitive diagnosis modeling and multidimensional item response modeling 
    for dichotomous and polytomous item responses. This package enables the estimation of 
    the DINA and DINO model (Junker & Sijtsma, 2001, <doi:10.1177/01466210122032064>),
    the multiple group (polytomous) GDINA model (de la Torre, 2011, 
    <doi:10.1007/s11336-011-9207-7>), the multiple choice DINA model (de la Torre, 2009, 
    <doi:10.1177/0146621608320523>), the general diagnostic model (GDM; von Davier, 2008, 
    <doi:10.1348/000711007X193957>), the structured latent class model (SLCA; Formann, 1992, 
    <doi:10.1080/01621459.1992.10475229>) and regularized latent class analysis 
    (Chen, Li, Liu, & Ying, 2017, <doi:10.1007/s11336-016-9545-6>). 
    See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) <doi:10.18637/jss.v074.i02> 
    or Robitzsch and George (2019, <doi:10.1007/978-3-030-05584-4_26>)     
    for further details on estimation and the package structure.
    For tutorials on how to use the CDM package see 
    George and Robitzsch (2015, <doi:10.20982/tqmp.11.3.p189>) as well as
    Ravand and Robitzsch (2015).
Provides tools for working with observational health data in the 
  Observational Medical Outcomes Partnership (OMOP) Common Data Model format with a pipe friendly syntax.
  Common data model database table references are stored in a single compound object along with metadata.
Provides useful tools for cognitive diagnosis modeling (CDM). The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, <doi:10.1111/bmsp.12228>) and the discrete factor loading method (Wang, Song, & Ding, 2018, <doi:10.1007/978-3-319-77249-3_29>). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, <doi:10.3389/fpsyg.2021.614470>). Other relevant methods and features for CDM applications, such as the restricted DINA model (Nájera et al., 2023; <doi:10.3102/10769986231158829>), the general nonparametric classification method (Chiu et al., 2018; <doi:10.1007/s11336-017-9595-4>), and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022; <doi:10.3758/s13428-022-01967-5>) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices.
Wrapper functions to model and extract various quantitative information from absorption spectra of chromophoric dissolved organic matter (CDOM).
Parallel coordinate plotting with resolutions for large data sets
 and missing values.
A wrapper for the 'CDRC' 'API' that returns data frames or 'sf' of 'CDRC' data. The 'API' web reference is:<https://api.cdrc.ac.uk/swagger/index.html>.
This is an implementation of constrained dual scaling for
    detecting response styles in categorical data, including utility functions. The
    procedure involves adding additional columns to the data matrix representing the
    boundaries between the rating categories. The resulting matrix is then doubled
    and analyzed by dual scaling. One-dimensional solutions are sought which provide
    optimal scores for the rating categories. These optimal scores are constrained
    to follow monotone quadratic splines. Clusters are introduced within which the
    response styles can vary. The type of response style present in a cluster can
    be diagnosed from the optimal scores for said cluster, and this can be used to
    construct an imputed version of the data set which adjusts for response styles.
Provides interface to the 'Copernicus Data Space Ecosystem' API 
    <https://dataspace.copernicus.eu/analyse/apis>, mainly for searching the catalog of available
    data from Copernicus Sentinel missions and obtaining the images for just the area of interest 
    based on selected spectral bands. The package uses the 'Sentinel Hub' REST API interface
    <https://dataspace.copernicus.eu/analyse/apis/sentinel-hub> that provides access to various 
    satellite imagery archives. It allows you to access raw satellite data, rendered images, 
    statistical analysis, and other features.
Deriving skill structures from skill assignment 
    data for courses (sets of learning objects).
Cuddy-Della valle index gives the degree of instability present in the data by accommodating the effect of a trend. The adjusted R squared value of the best fitted model is chosen. The index is obtained by multiplying the coefficient of variation with square root of one minus the adjusted R-squared value. This package has been developed using concept of Shankar et al. (2022)<doi:10.3389/fsufs.2023.1208898>.
Provides tools for sampling from a conditional copula density decomposed via 
     Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a 
     sampling are those which sample as first the conditioning variables (when following the 
     sampling algorithms shown in Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>). 
     The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) 
     <DOI:10.5194/hess-2016-652>, and it is a modified version of that from Aas et al. (2009) 
     <DOI:10.1016/j.insmatheco.2007.02.001>. A function is available to select the best vine 
     (based on information criteria) among those which allow for such a conditional sampling. 
     The package includes a function to compare scatterplot matrices and pair-dependencies of 
     two multivariate datasets.
Splits data into Gaussian type clusters using the Cross-Entropy 
    Clustering ('CEC') method. This method allows for the simultaneous use of 
    various types of Gaussian mixture models, for performing the reduction of 
    unnecessary clusters, and for discovering new clusters by splitting them. 
    'CEC' is based on the work of Spurek, P. and Tabor, J. (2014) 
    <doi:10.1016/j.patcog.2014.03.006>.
Provides analytical methods for analyzing CRISPR screen data
    at different levels of gene expression. Multi-component normal mixture models
    and EM algorithms are used for modeling.
Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.
Create and learn Chain Event Graph (CEG) models using a Bayesian 
    framework. It provides us with a Hierarchical Agglomerative algorithm to 
    search the CEG model space.
    The package also includes several facilities for visualisations of the
    objects associated with a CEG. The CEG class can represent a range of
    relational data types, and supports arbitrary vertex, edge and graph
    attributes. A Chain Event Graph is a tree-based graphical model that
    provides a powerful graphical interface through which domain experts can
    easily translate a process into sequences of observed events using plain
    language. CEGs have been a useful class of graphical model especially to
    capture context-specific conditional independences. References: Collazo R, 
    Gorgen C, Smith J. Chain Event Graph. CRC Press, ISBN 9781498729604, 2018
    (forthcoming); and Barday LM, Collazo RA, Smith JQ, Thwaites PA, Nicholson AE. 
    The Dynamic Chain Event Graph. Electronic Journal of Statistics, 9 (2) 2130-2169
    <doi:10.1214/15-EJS1068>.
Model building, surrogate model
    based optimization and Efficient Global Optimization in combinatorial
    or mixed search spaces.
Contains a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations.
Data from statistical agencies and other institutions often
    need to be protected before they can be published. This package 
    can be used to perturb statistical tables in a consistent way. The
    main idea is to add - at the micro data level - a record key for each
    unit. Based on these keys, for any cell in a statistical table a
    cell key is computed as a function on the record keys contributing to
    a specific cell. Values that are added to the cell in order to perturb
    it are derived from a lookup-table that maps values of cell keys to 
    specific perturbation values. The theoretical basis for the methods 
    implemented can be found in Thompson, Broadfoot and Elazar 
    (2013) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2013/Topic_1_ABS.pdf>
    which was extended and enhanced by Giessing and Tent 
    (2019) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2019/mtg1/SDC2019_S2_Germany_Giessing_Tent_AD.pdf>.
Finds the most likely originating tissue(s) and developmental stage(s) of tissue-specific RNA sequencing data. The package identifies both pure transcriptomes and mixtures of transcriptomes. The most likely identity is found through comparisons of the sequencing data with high-throughput in situ hybridisation patterns. Typical uses are the identification of cancer cell origins, validation of cell culture strain identities, validation of single-cell transcriptomes, and validation of identity and purity of flow-sorting and dissection sequencing products.
Annotate single-cell RNA sequencing data manually based on
    marker gene thresholds.
    Find cell type rules (gene+threshold) through exploration,
    use the popular piping operator '%>%' to reconstruct complex
    cell type hierarchies.
    'cellpypes' models technical noise to find positive and negative cells for
    a given expression threshold and returns cell type labels or pseudobulks.
    Cite this package as Frauhammer (2022) <doi:10.5281/zenodo.6555728> and
    visit <https://github.com/FelixTheStudent/cellpypes> for tutorials and newest
    features.
Helper functions to work with spreadsheets and the "A1:D10" style
    of cell range specification.
Methods for analyzing (cell) motion in two or three dimensions.
	Available measures include displacement, confinement ratio, autocorrelation,
	straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks,
	steps, or subtracks with varying length. While the methodology has been developed for
	cell trajectory analysis, it is applicable to anything that moves including animals,
	people, or vehicles.
	Some of the methodology implemented in this packages was described by: 
	Beauchemin, Dixit, and Perelson (2007) <doi:10.4049/jimmunol.178.9.5505>, 
	Beltman, Maree, and de Boer (2009) <doi:10.1038/nri2638>,
	Gneiting and Schlather (2004) <doi:10.1137/S0036144501394387>,
	Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) <doi:10.1371/journal.pone.0080808>,
	Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) <doi:10.1016/j.immuni.2012.05.014>,
	Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) <doi:10.1073/pnas.1102288108>,
  Textor, Sinn, and de Boer (2013) <doi:10.1186/1471-2105-14-S6-S10>,
  Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) <doi:10.1371/journal.pcbi.1003752>.
Implements a methodology for using cell volume
        distributions to estimate cell growth rates and division times
        that is described in the paper entitled, "Cell Volume
        Distributions Reveal Cell Growth Rates and Division Times", by
        Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro
        Tona and Anne L. Plant, which is in press in the Journal of
        Theoretical Biology.  In order to reproduce the analysis used
        to obtain Table 1 in the paper, execute the command
        "example(fitVolDist)".
Tools for detecting cellwise outliers and robust methods to analyze
    data which may contain them. Contains the implementation of the algorithms described in
    Rousseeuw and Van den Bossche (2018) <doi:10.1080/00401706.2017.1340909> (open access)
    Hubert et al. (2019) <doi:10.1080/00401706.2018.1562989> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.1080/00401706.2019.1677270> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.1007/s10994-021-05960-5> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.52933/jdssv.v1i3.18> (open access),
    Raymaekers and Rousseeuw (2022) <arXiv:2207.13493> (open access)
    Rousseeuw (2022) <doi:10.1016/j.ecosta.2023.01.007> (open access).
    Examples can be found in the vignettes:
    "DDC_examples", "MacroPCA_examples", "wrap_examples", "transfo_examples",
    "DI_examples", "cellMCD_examples" , "Correspondence_analysis_examples",
    and "cellwise_weights_examples".
Implementation of the Coarsened Exact Matching algorithm discussed 
	along with its properties in
  Iacus, King, Porro (2011) <DOI:10.1198/jasa.2011.tm09599>;
	Iacus, King, Porro (2012) <DOI:10.1093/pan/mpr013> and
	Iacus, King, Porro (2019) <DOI:10.1017/pan.2018.29>.
'CemCO' algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) <arXiv:2004.02333>).
Broken adaptive ridge estimator for censored data is used to
  select variables and estimate their coefficients in the semi-parametric 
  accelerated failure time model for right-censored survival data.
The network analysis plays an important role in numerous application domains including biomedicine. 
             Estimation of the number of communities is a fundamental and critical issue in network analysis. Most existing studies assume that the number of communities is known a priori, or lack of rigorous theoretical guarantee on the estimation consistency. This method proposes a regularized network embedding model to simultaneously estimate the community structure and the number of communities in a unified formulation. 
	           The proposed model equips network embedding with a novel composite regularization term, which pushes the embedding vector towards its center and collapses similar community centers with each other. A rigorous theoretical analysis is conducted, establishing asymptotic consistency in terms of community detection and estimation of the number of communities. 
	           Reference: 
             Ren, M., Zhang S. and Wang J. (2022). "Consistent Estimation of the Number of Communities via Regularized Network Embedding". Biometrics, <doi:10.1111/biom.13815>.
Implementation of Tobit type I and type II families for censored regression using the 'mgcv' package, based on methods detailed in Woods (2016) <doi:10.1080/01621459.2016.1180986>.
Contains functions to estimate a smoothed and a non-smoothed (empirical) time-dependent 
             receiver operating characteristic curve and the corresponding area under the receiver 
             operating characteristic curve and the optimal cutoff point for the right and interval 
             censored survival data. See Beyene and El Ghouch (2020)<doi:10.1002/sim.8671> and Beyene 
             and El Ghouch (2022) <doi:10.1002/bimj.202000382>.
Creates a common framework for organizing, naming, and gathering 
    population, age, race, and ethnicity data from the Census Bureau. Accesses 
    the API <https://www.census.gov/data/developers/data-sets.html>. Provides 
    tools for adding information to existing data to line up with Census data.
Implementations of threshold regression approaches for linear
	     regression models with a covariate subject to random censoring,
	     including deletion threshold regression and completion threshold regression.
	     Reverse survival regression, which flip the role of response variable and the
	     covariate, is also considered.
Provee un acceso conveniente a mas de 17 millones de registros
    de la base de datos del Censo 2017. Los datos fueron importados desde
    el DVD oficial del INE usando el Convertidor REDATAM creado por Pablo De
    Grande. Esta paquete esta documentado intencionalmente en castellano
    asciificado para que funcione sin problema en diferentes plataformas.
    (Provides convenient access to more than 17 million records from the
    Chilean Census 2017 database. The datasets were imported from the official
    DVD provided by the Chilean National Bureau of Statistics by using the
    REDATAM converter created by Pablo De Grande and in addition it includes the
    maps accompanying these datasets.)
Download data from Brazil's population census. The package is built 
             on top of the 'Arrow' platform <https://arrow.apache.org/docs/r/>, 
             which allows users to work with larger-than-memory census data using 
             'dplyr' familiar functions. <https://arrow.apache.org/docs/r/articles/arrow.html#analyzing-arrow-data-with-dplyr>.
Implement an interval censor method 
 to break ties when using data with ties to fitting a 
 bivariate copula.
Engines for survival models from the 'parsnip' package. These
    include parametric models (e.g., Jackson (2016)
    <doi:10.18637/jss.v070.i08>), semi-parametric (e.g., Simon et al
    (2011) <doi:10.18637/jss.v039.i05>), and tree-based models (e.g.,
    Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>).
Maximum Likelihood estimation of censored regression (Tobit) models
   with cross-sectional and panel data.
It fits linear regression models for censored spatial data. It provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. It also offers graphical tools for assessing the fitted model. More details can be found in Ordonez et al. (2018) <doi:10.1016/j.spasta.2017.12.001>.
Contains selected variables from the time series profiles for statistical areas level 2 from the 2006, 2011, and 2016 censuses of population and housing, Australia. Also provides methods for viewing the questions asked for convenience during analysis.
A wrapper for the U.S. Census Bureau APIs that returns data frames of 
	Census data and metadata. Available datasets include the 
	Decennial Census, American Community Survey, Small Area Health Insurance Estimates,
	Small Area Income and Poverty Estimates, Population Estimates and Projections, and more.
Use the US Census API to collect summary data tables
    for SF1 and ACS datasets at arbitrary geographies.
Calculates centrality indices additional to the 'igraph' package centrality functions.
Find the numbers of test tubes that can be balanced in centrifuge
    rotors and show various ways to load them. Refer to Pham (2020)
    <doi:10.31224/osf.io/4xs38> for more information on package functionality.
Easy and convenient access to the datasets of the
  "Centre d'Estudis d'Opinio", the Catalan institution for polling and public
  opinion. The package uses the data stored in the servers of the CEO and returns
  it in a tidy format.
Optimization solver based on the Cross-Entropy method.
It aims to find significant pathways through 
    network topology information. It has several advantages compared 
    with current pathway enrichment tools. First, pathway node instead 
    of single gene is taken as the basic unit when analysing networks 
    to meet the fact that genes must be constructed into complexes to 
    hold normal functions. Second, multiple network centrality measures are 
    applied simultaneously to measure importance of nodes from different 
    aspects to make a full view on the biological system. CePa extends 
    standard pathway enrichment methods, which include both over-representation 
    analysis procedure and gene-set analysis procedure. 
    <doi:10.1093/bioinformatics/btt008>.
Provides data on countries and their main city or agglomeration and
    the different distance measures and dummy variables indicating whether two
    countries are contiguous, share a common language or a colonial
    relationship. The reference article for these datasets is Mayer and Zignago
    (2011) <http://www.cepii.fr/CEPII/en/publications/wp/abstract.asp?NoDoc=3877>.
Functions and Data to support Context Driven Exploratory Projection Pursuit.
Retorna detalhes de dados de CEPs brasileiros, bairros, logradouros 
    e tal. (Returns info of Brazilian postal codes, city names, addresses 
    and so on.)
Read Condensed Cornell Ecology Program ('CEP') and legacy
    'CANOCO' files into R data frames.
Provides functions and data files to help CE Public-Use
    Microdata (PUMD) users calculate annual estimated expenditure means,
    standard errors, and quantiles according to the methods used by the CE with
    PUMD. For more information on the CE please visit <https://www.bls.gov/cex>.
    For further reading on CE estimate calculations please see the CE
    Calculation section of the U.S. Bureau of Labor Statistics (BLS) Handbook of
    Methods at <https://www.bls.gov/opub/hom/cex/calculation.htm>. For
    further information about CE PUMD please visit
    <https://www.bls.gov/cex/pumd.htm>.
Perform censored quantile regression of Huang (2010) <doi:10.1214/09-AOS771>, and restore monotonicity respecting via adaptive interpolation for dynamic regression of Huang (2017) <doi:10.1080/01621459.2016.1149070>. The monotonicity-respecting restoration applies to general dynamic regression models including (uncensored or censored) quantile regression model, additive hazards model, and dynamic survival models of Peng and Huang (2007) <doi:10.1093/biomet/asm058>, among others.
Download imagery tiles to a standard cache and load the data into raster objects. 
 Facilities for 'AWS' terrain <https://registry.opendata.aws/terrain-tiles/> terrain and 'Mapbox' 
 <https://www.mapbox.com/> servers are provided.
The 'vctrs' package provides a concept of vector prototype
    that can be especially useful when deploying models and code.  Serialize 
    these object prototypes to 'JSON' so they can be used to check and coerce 
    data in production systems, and deserialize 'JSON' back to the correct 
    object prototypes.
Fits a Causal Effect Random Forest of Interaction Tress (CERFIT) which is a modification of the Random Forest algorithm where each split is chosen to maximize subgroup treatment heterogeneity. Doing this allows it to estimate the individualized treatment effect for each observation in either randomized controlled trial (RCT) or observational data. For more information see X. Su, A. T. Peña, L. Liu, and R. A. Levine (2018) <doi:10.48550/arXiv.1709.04862>.
Implements the iterated RMCD method of Cerioli (2010)
	for multivariate outlier detection via robust Mahalanobis distances. Also
	provides the finite-sample RMCD method discussed in the paper, as well as 
	the methods provided in Hardin and Rocke (2005) <doi:10.1198/106186005X77685> 
	and Green and Martin (2017).
Provides several functions to identify and analyse miRNA sponge, including
             popular methods for identifying miRNA sponge interactions, two types 
             of global ceRNA regulation prediction methods and four types of context-specific 
             prediction methods( Li Y et al.(2017) <doi:10.1093/bib/bbx137>), which are based 
             on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, 
             respectively. In addition, For predictive ceRNA relationship pairs, this package 
             provides several downstream analysis algorithms, including regulatory network 
             analysis and functional annotation analysis, as well as survival prognosis analysis
             based on expression of ceRNA ternary pair.
Compute the certainty equivalents and premium risks
              as tools for risk-efficiency analysis. For more technical information, please refer to: Hardaker, Richardson, Lien, & Schumann (2004) <doi:10.1111/j.1467-8489.2004.00239.x>, and Richardson, & Outlaw (2008) <doi:10.2495/RISK080231>.
Perform Nonlinear Mixed-Effects (NLME) Modeling using Certara's
    NLME-Engine. Access the same Maximum Likelihood engines used in the Phoenix
    platform, including algorithms for parametric methods, individual, and pooled
    data analysis <https://www.certara.com/app/uploads/2020/06/BR_PhoenixNLME-v4.pdf>.
    The Quasi-Random Parametric Expectation-Maximization Method (QRPEM) is also
    supported <https://www.page-meeting.org/default.asp?abstract=2338>. Execution
    is supported both locally or on remote machines. Remote execution includes
    support for Linux Sun Grid Engine (SGE), Terascale Open-source Resource and
    Queue Manager (TORQUE) grids, Linux and Windows multicore, and individual runs.
A convenient set of wrapper functions to install
    pharmacometric packages and Shiny applications developed by Certara
    PMX and Integrated Drug Development (iDD). The functions ensure the
    successful installation of packages from non-standard repositories.
Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.
Makes accessing and loading the Canadian Election Study (<http://www.ces-eec.ca/>, <https://ces-eec.arts.ubc.ca/>, <https://search1.odesi.ca/#/>) surveys into the R workspace more efficient by 
  downloading a requested survey and loading it as a data object. This removes the need to locate, download, load, 
  and change working directories when working with the Canadian Election Study surveys.
Collection of perceptually uniform colour maps made by Peter Kovesi
    (2015) "Good Colour Maps: How to Design Them" <arXiv:1509.03700> 
    at the Centre for Exploration Targeting (CET).
Ceteris Paribus Profiles (What-If Plots) are designed to present model 
    responses around selected points in a feature space. 
    For example around a single prediction for an interesting observation. 
    Plots are designed to work in a model-agnostic fashion, they are working 
    for any predictive Machine Learning model and allow for model comparisons.
    Ceteris Paribus Plots supplement the Break Down Plots from 'breakDown' package.
Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner's test using a conventional and an accelerated algorithm.
Cellular cooperation compromises the plating efficiency-based 
    analysis of clonogenic survival data. This tool provides functions that 
    enable a robust analysis of colony formation assay (CFA) data in presence 
    or absence of cellular cooperation. 
    The implemented method has been described 
    in Brix et al. (2020). (Brix, N., Samaga, D., Hennel, R. et al. 
    "The clonogenic assay: robustness of plating efficiency-based analysis is 
    strongly compromised by cellular cooperation." Radiat Oncol 15, 248 (2020).
    <doi:10.1186/s13014-020-01697-y>)
        Power regression for parameter estimation, calculation of survival
    fractions, uncertainty analysis and plotting functions are provided.
Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions,
  with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for
  CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C++. For 
  mathematical details and software tutorial, see Mahani and Sharabiani (2019) <DOI:10.18637/jss.v089.i09>.
Package for the analysis of categorical functional data.
  The main purpose is to compute an encoding (real functional variable) for each state <doi:10.3390/math9233074>.
  It also provides functions to perform basic statistical analysis on categorical functional data.
Provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) <doi:10.4054/MPIDR-WP-2019-004> for more information.
A simple, fast algorithm to find the neighbors and similarities of users in user-based filtering systems,
             to break free from the complex computation of existing similarity formulas and the ability to solve big data.
This project provides a group of new functions to calculate
    the outputs of the two main components of the Canadian Forest Fire
    Danger Rating System (CFFDRS) Van Wagner and Pickett (1985)
    <https://cfs.nrcan.gc.ca/publications?id=19973>) at various time
    scales: the Fire Weather Index (FWI) System Wan Wagner (1985)
    <https://cfs.nrcan.gc.ca/publications?id=19927> and the Fire Behaviour
    Prediction (FBP) System Forestry Canada Fire Danger Group (1992)
    <https://cfs.nrcan.gc.ca/pubwarehouse/pdfs/10068.pdf>. Some functions
    have two versions, table and raster based.
The Citation File Format version 1.2.0
    <doi:10.5281/zenodo.5171937> is a human and machine readable file
    format which provides citation metadata for software. This package
    provides core utilities to generate and validate this metadata.
Facilitates the identification of counterfactual queries in
 structural causal models via the ID* and IDC* algorithms
 by Shpitser, I. and Pearl, J. (2007, 2008) <arXiv:1206.5294>, 
 <https://jmlr.org/papers/v9/shpitser08a.html>. 
 Provides a simple interface for defining causal diagrams and counterfactual 
 conjunctions. Construction of parallel worlds graphs and counterfactual graphs 
 is carried out automatically based on the counterfactual query and the causal 
 diagram. See Tikka, S. (2023) <doi:10.32614/RJ-2023-053> for a tutorial of 
 the package.
The collaborative Filtering methodology has been widely used in recommendation systems, which
    uses similarities between users or items to make recommendations. A class called CF was implemented,
    where it is structured by matrices and composed of recommendation and database manipulation functions.
    See Aggarwal (2016) <doi:10.1007/978-3-319-29659-3> for an overview.
Performs causal functional mediation analysis (CFMA) for functional treatment, functional mediator, and functional outcome. This package includes two functional mediation model types: (1) a concurrent mediation model and (2) a historical influence mediation model. See Zhao et al. (2018), Functional Mediation Analysis with an Application to Functional Magnetic Resonance Imaging Data, <arXiv:1805.06923> for details.
Allows clinicians to predict survival probabilities over the next two years for cystic fibrosis patients, based on the clinical prediction models published in Stanojevic et al. (2019) <doi:10.1183/13993003.00224-2019>.
In phase I clinical trials, the primary objective is to ascertain the maximum tolerated dose (MTD) corresponding to a specified target toxicity rate. The 'CFO' package facilitates the implementation of dose-finding trials by utilizing calibration-free odds type (CFO-type) designs. Specifically, it encompasses the calibration-free odds (CFO) (Jin and Yin (2022) <doi:10.1177/09622802221079353>), two-dimensional CFO (2dCFO) (Wang et al. (2023) <doi:10.3389/fonc.2023.1294258>), time-to-event CFO (TITE-CFO) (Jin and Yin (2023) <doi:10.1002/pst.2304>), fractional CFO (fCFO), accumulative CFO (aCFO), TITE-aCFO, and f-aCFO designs. The ‘CFO' package accommodates diverse CFO-type designs, allowing users to tailor the approach based on factors such as dose information inclusion, handling of late-onset toxicity, and the nature of the target drug (single-drug or drug-combination). The functionalities embedded in 'CFO' package include the determination of the dose level for the next cohort, the selection of the MTD for a real trial, and the execution of single or multiple simulations to obtain operating characteristics. Moreover, these functions are equipped with early stopping and dose elimination rules to address safety considerations. Users have the flexibility to choose different distributions, thresholds, and cohort sizes among others for their specific needs. The output of the 'CFO' package can be summary statistics as well as various plots for better visualization.
Performs forward model selection, using the C-index/concordance
  in survival analysis models.
Estimate the severity of a disease and ascertainment of cases, as discussed in Nishiura et al. (2009) <doi:10.1371/journal.pone.0006852>.
Developed as a collaboration between Earth lab and the North Central Climate Adaptation Science Center to help users gain insights from available climate data. Includes tools and instructions for downloading climate data via a 'USGS' API and then organizing those data for visualization and analysis that drive insight. Web interface for 'USGS' API can be found at <http://thredds.northwestknowledge.net:8080/thredds/reacch_climate_CMIP5_aggregated_macav2_catalog.html>.
Support for all calendars as specified in the Climate and Forecast 
    (CF) Metadata Conventions for climate and forecasting data. The CF Metadata 
    Conventions is widely used for distributing files with climate observations 
    or projections, including the Coupled Model Intercomparison Project (CMIP) 
    data used by climate change scientists and the Intergovernmental Panel on
    Climate Change (IPCC). This package specifically allows the user to work 
    with any of the CF-compliant calendars (many of which are not compliant with 
    POSIXt). The CF time coordinate is formally defined in the CF Metadata 
    Conventions document available at <https://cfconventions.org/Data/cf-conventions/cf-conventions-1.10/cf-conventions.html#time-coordinate>.
Comprehensive data analysis software, and the name "cg" stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research and development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized.
Fits constrained groupwise additive index models and provides functions for inference and interpretation of these models. The method is described in Masselot, Chebana, Campagna, Lavigne, Ouarda, Gosselin (2022) "Constrained groupwise additive index models" <doi:10.1093/biostatistics/kxac023>.
'CGAL' is a C++ library that aims to provide easy access to efficient and
  reliable algorithms in computational geometry. Since its version 4, 'CGAL' can be used
  as standalone header-only library and is available under a double GPL-3|LGPL license.
  <https://www.cgal.org/>.
A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions.  It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Liao X, Meyer MC (2019)<doi:10.18637/jss.v089.i05> for more details.
The cgAUC can calculate the AUC-type measure of Obuchowski(2006) when gold standard is continuous, and find the optimal linear combination of variables with respect to this measure.
Developing general equilibrium models, computing general equilibrium and simulating economic dynamics with structural dynamic models in LI (2019, ISBN: 9787521804225) "General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press". When developing complex general equilibrium models, GE package should be used in addition to this package.
Run computer experiments using the adaptive composite grid
    algorithm with a Gaussian process model.
    The algorithm works best when running an experiment that can evaluate thousands
    of points from a deterministic computer simulation.
    This package is an implementation of a forthcoming paper by Plumlee,
    Erickson, Ankenman, et al. For a preprint of the paper,
    contact the maintainer of this package.
Conditional graphical lasso estimator is an extension of the graphical lasso proposed to estimate the conditional dependence structure of a set of p response variables given q predictors. This package provides suitable extensions developed to study datasets with censored and/or missing values. Standard conditional graphical lasso is available as a special case. Furthermore, the package provides an integrated set of core routines for visualization, analysis, and simulation of datasets with censored and/or missing values drawn from a Gaussian graphical model. Details about the implemented models can be found in Augugliaro et al. (2023) <doi: 10.18637/jss.v105.i01>, Augugliaro et al. (2020b) <doi: 10.1007/s11222-020-09945-7>, Augugliaro et al. (2020a) <doi: 10.1093/biostatistics/kxy043>, Yin et al. (2001) <doi: 10.1214/11-AOAS494> and Stadler et al. (2012) <doi: 10.1007/s11222-010-9219-7>.
Estimates the ratio of the regression coefficients and the dispersion parameter in conditional generalized linear models for clustered data.
This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with 'Dexcom', 'iPro 2', 'Diasend', 'Libre', or 'Carelink' data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file in either rows or columns. The column format of this spreadsheet is compatible with REDCap data upload. cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots. Please visit <https://github.com/childhealthbiostatscore/R-Packages/> to download the new-user guide.
Contains all of the functions necessary for the complete analysis of a continuous glucose monitoring study and can be applied to data measured by various existing 'CGM' devices such as 'FreeStyle Libre', 'Glutalor', 'Dexcom' and 'Medtronic CGM'. It reads a series of data files, is able to convert various formats of time stamps, can deal with missing values, calculates both regular statistics and nonlinear statistics, and conducts group comparison. It also displays results in a concise format. Also contains two unique features new to 'CGM' analysis: one is the implementation of strictly standard mean difference and the class of effect size; the other is the development of a new type of plot called antenna plot. It corresponds to 'Zhang XD'(2018)<doi:10.1093/bioinformatics/btx826>'s article 'CGManalyzer: an R package for analyzing continuous glucose monitoring studies'.
Continuous glucose monitoring (CGM) systems provide real-time, 
  dynamic glucose information by tracking interstitial glucose values 
  throughout the day. Glycemic variability, also known as glucose variability, 
  is an established risk factor for hypoglycemia (Kovatchev) and 
  has been shown to be a risk factor in diabetes complications. 
  Over 20 metrics of glycemic variability have been identified. 
  Here, we provide functions to calculate glucose summary metrics, 
  glucose variability metrics (as defined in clinical publications), 
  and visualizations to visualize trends in CGM data.
  Cho P, Bent B, Wittmann A, et al. (2020) <https://diabetes.diabetesjournals.org/content/69/Supplement_1/73-LB.abstract>
  American Diabetes Association (2020) <https://professional.diabetes.org/diapro/glucose_calc>
  Kovatchev B (2019) <doi:10.1177/1932296819826111>
  Kovdeatchev BP (2017) <doi:10.1038/nrendo.2017.3>
  Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017>
  Umpierrez GE, P. Kovatchev B (2018) <doi:10.1016/j.amjms.2018.09.010>.
Find multiple solutions of a nonlinear least squares problem.  Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) <doi:10.1007/s11081-020-09571-2>. Cluster Gauss–Newton method. Optimization and Engineering, 1-31.  Please cite the following paper when profile likelihood plot is drawn with this software and used in your research: Aoki and Sugiyama (2024) <doi:10.1002/psp4.13055>. Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models. CPT Pharmacometrics Syst Pharmacol.13(1):54-67.
Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary.  Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP.
Miscellaneous functions useful for teaching statistics as well as actually practicing the art. They typically are not new methods but rather wrappers around either base R or other packages.
The compound growth rate indicates the percentage change of a specific variable over a defined period. It is calculated using non-linear models, particularly the exponential model. To estimate the compound growth rates, the growth model is first converted to semilog form and then analyzed using Ordinary Least Squares (OLS) regression. This package has been developed using concept of Shankar et al. (2022)<doi:10.3389/fsufs.2023.1208898>.
Functions for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc.  The functions 'seqle' and 'reverse.seqle' mimic the base 'rle' but can search for linear sequences. The function 'splatnd' allows the user to generate zero-argument commands without the need for 'makeActiveBinding' .  Functions provided to convert from any base to any other base, and to find the n-th greatest max or n-th least min.  In addition, functions which mimic Unix shell commands, including 'head', 'tail' ,'pushd' ,and 'popd'. Various other goodies included as well.
The solution to some common problems is proposed, 
     as well as a summary of some small functions. In particular, 
     it provides a useful function for some problems in chemistry.
     For example, monoa(), monob() and mono() function can be used 
     to calculate The pH of weak acid/base. 
     The ggpng() function can save the PNG format with transparent background. 
     The period_table() function will show the periodic table. Also the
     show_ruler() function will show the ruler.
     The show_color() function is funny and easier to show colors.
      I also provide the symb() function to generate multiple symbols at once.
     The csv2vcf() function provides an easy method to generate a file.
     The sym2poly() and sym2coef() function can extract coefficients from 
     polynomials.
Various statistical methods and models which are
    typically used for the estimation of outstanding claims reserves
    in general insurance, including those to estimate the claims
    development result as required under Solvency II.
Assign distinct colors to arbitrary multi-dimensional data, considering its structure.
Performs adjustments of a user-supplied independence loglikelihood 
    function using a robust sandwich estimator of the parameter covariance 
    matrix, based on the methodology in Chandler and Bate (2007) 
    <doi:10.1093/biomet/asm015>.  This can be used for cluster correlated data 
    when interest lies in the parameters of the marginal distributions or for 
    performing inferences that are robust to certain types of model 
    misspecification.  Functions for profiling the adjusted loglikelihoods are 
    also provided, as are functions for calculating and plotting confidence 
    intervals, for single model parameters, and confidence regions, for pairs 
    of model parameters.  Nested models can be compared using an adjusted 
    likelihood ratio test.
Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data.  Many popular non-parametric and frequentist methods are included.  The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
Implements the high-dimensional changepoint detection method GeomCP and the related mappings used for changepoint detection. These methods view the changepoint problem from a geometrical viewpoint and aim to extract relevant geometrical features in order to detect changepoints. The geomcp() function should be your first point of call. References: Grundy et al. (2020) <doi:10.1007/s11222-020-09940-y>.
Allows users to input their data, segmentation and function used for the segmentation (and additional arguments) and the package calculates the influence of the data on the changepoint locations, see Wilms et al. (2022) <doi:10.1080/10618600.2021.2000873>.  Currently this can only be used with the changepoint package functions to identify changes, but we plan to extend this.  There are options for different types of graphics to assess the influence.
Implements the multiple changepoint algorithm PELT with a nonparametric cost function based on the empirical distribution of the data. This package extends the changepoint package (see Killick, R and Eckley, I (2014) <doi:10.18637/jss.v058.i03> ).
Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: <doi:10.1214/20-EJS1710>, <arXiv:2006.03283>; 2) univariate polynomials: <doi:10.1214/21-EJS1963>; 3) univariate and multivariate nonparametric settings: <doi:10.1214/21-EJS1809>, <doi:10.1109/TIT.2021.3130330>; 4) high-dimensional covariances: <doi:10.3150/20-BEJ1249>; 5) high-dimensional networks with and without missing values: <doi:10.1214/20-AOS1953>, <arXiv:2101.05477>, <arXiv:2110.06450>; 6) high-dimensional linear regression models: <arXiv:2010.10410>, <arXiv:2207.12453>; 7) high-dimensional vector autoregressive models: <arXiv:1909.06359>; 8) high-dimensional self exciting point processes: <arXiv:2006.03572>; 9) dependent dynamic nonparametric random dot product graphs: <arXiv:1911.07494>; 10) univariate mean against adversarial attacks: <arXiv:2105.10417>.
Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, <doi:10.1080/03610918.2011.592248>.
A multiple testing procedure for clustered alternative hypotheses. 
    It is assumed that the p-values under the null hypotheses follow U(0,1) and 
    that the distributions of p-values from the alternative hypotheses are 
    stochastically smaller than U(0,1). By aggregating information, this 
    method is more sensitive to detecting signals of low magnitude than 
    standard methods. Additionally, sporadic small p-values appearing 
    within a null hypotheses sequence are avoided by averaging on the 
    neighboring p-values.
Change point tests for joint distributions and copulas using pseudo-observations with multipliers or bootstrap. The processes used here have been defined in Bucher, Kojadinovic, Rohmer & Segers <doi:10.1016/j.jmva.2014.07.012> and Nasri & Remillard <doi:10.1016/j.jmva.2019.03.002>.
Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task.
Facilitates workflows to reproducibly transform estimates of
    species’ distributions into metrics relevant for conservation. For
    example, combining predictions from species distribution models with other
    maps of environmental data to characterize the proportion of a species’
    range that is under protection, calculating metrics used under the
    International Union for Conservation of Nature (IUCN) Criteria A and B
    guidelines (Area of Occupancy and Extent of Occurrence), and calculating
    more general metrics such as taxonomic and phylogenetic diversity, as well
    as endemism. Also facilitates temporal comparisons among biodiversity
    metrics to inform efforts towards complementarity and consideration of
    future scenarios in conservation decisions. 'changeRangeR' also provides
    tools to determine the effects of modeling decisions through sensitivity
    tests.
Estimation of changepoints using an "S-curve"
   approximation. Formation of confidence intervals for changepoint
   locations and magnitudes. Both abrupt and gradual changes can be
   modeled.
Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++.
Shiny Web Application for the Multichannel Attribution Problem. It is a user-friendly graphical interface for package 'ChannelAttribution'.
Adjusts the loglikelihood of common econometric models for
    clustered data based on the estimation process suggested in 
    Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, using the 'chandwich' 
    package <https://cran.r-project.org/package=chandwich>, and provides 
    convenience functions for inference on the adjusted models.
Computes and visualize the results of the 0-1 test for chaos proposed
    by Gottwald and Melbourne (2004) <DOI:10.1137/080718851>. The algorithm is
    available in parallel for the independent values of parameter c. Additionally,
    fast RQA is added to distinguish chaos from noise.
The main objective of the package is to enter a word of at least two letters based on which an Iterated Function System with Probabilities is constructed, and a two-dimensional fractal containing the chosen word infinitely often is generated via the Chaos Game. Additionally, the package allows to project the two-dimensional fractal on several three-dimensional surfaces and to transform the fractal into another fractal with uniform marginals.
Make fake data, supporting addresses, person names, dates,
    times, colors, coordinates, currencies, digital object identifiers
    ('DOIs'), jobs, phone numbers, 'DNA' sequences, doubles and integers
    from distributions and within a range.
Provides a very simple syntax for the user to generate custom plot(s) without having to remember complicated 'ggplot2' syntax. The 'chartql' package uses 'ggplot2' and manages all the syntax complexities internally. As an example, to generate a bar chart of company sales faceted by product category further faceted by season of the year, we simply write: "CHART bar X category, season Y sales".
The Large Language Model (LLM) represents a groundbreaking advancement 
    in data science and programming, and also allows us to extend the world of R. 
    A seamless interface for integrating the 'OpenAI' Web APIs into R is provided in this package. 
    This package leverages LLM-based AI techniques, enabling efficient knowledge discovery and data analysis 
    (see 'OpenAI' Web APIs details <https://openai.com/blog/openai-api>).
    The previous functions such as seamless translation and image generation have been moved 
    to other packages 'deepRstudio' and 'stableDiffusion4R'.
'OpenAI's 'ChatGPT' <https://chat.openai.com/> coding assistant for 'RStudio'. A set 
  of functions and 'RStudio' addins that aim to help the R developer in tedious coding tasks.
Enables user interactivity with large-language models ('LLM') inside
    the 'RStudio' integrated development environment (IDE). The user can
    interact with the model using the 'shiny' app included in this package, or
    directly in the 'R' console. It comes with back-ends for 'OpenAI', 'GitHub'
    'Copilot', and 'LlamaGPT'.
Fast and memory-efficient (or 'cheap') tools to facilitate
    efficient programming, saving time and memory. It aims to provide
    'cheaper' alternatives to common base R functions, as well as some
    additional functions.
A simple package to grab cheat sheets and save them to your local computer.
Utility functions that allow checking the basic validity of a function argument or any other value, 
             including generating an error and assigning a default in a single line of code. The main purpose of
             the package is to provide simple and easily readable argument checking to improve code robustness.
Check digits are used like file hashes to verify that a
    number has been transcribed accurately. The functions provided by this
    package help to calculate and verify check digits according to various
    algorithms.
Creates auto-grading check-fields and check-boxes for 'rmarkdown' 
    or 'quarto' HTML. It can be used in class, when teacher share materials 
    and tasks, so students can solve some problems and check their work. In 
    contrast to the 'learnr' package, the 'checkdown' package works serverlessly
    without 'shiny'.
Checks that students have the correct version of 'R', 'R' packages,
    'RStudio' and other dependencies installed, and that the recommended 'RStudio' 
    configuration has been applied.
A minimal R-package to approximately detect global and imported functions or variables from R-source code or R-packages by static code analysis.
Deal with packages 'check' outputs and reduce the risk of
    rejection by 'CRAN' by following policies.
Confirms if the number is Luhn compliant.
    Can check if credit card, IMEI number or any other Luhn based number is correct. 
    For more info see: <https://en.wikipedia.org/wiki/Luhn_algorithm>.
Tests and assertions to perform frequent argument checks. A
    substantial part of the package was written in C to minimize any worries
    about execution time overhead.
The goal of checkpoint is to solve the problem of package
    reproducibility in R. Specifically, checkpoint allows you to install packages
    as they existed on CRAN on a specific snapshot date as if you had a CRAN time
    machine. To achieve reproducibility, the checkpoint() function installs the
    packages required or called by your project and scripts to a local library
    exactly as they existed at the specified point in time. Only those packages
    are available to your project, thereby avoiding any package updates that came
    later and may have altered your results. In this way, anyone using checkpoint's
    checkpoint() can ensure the reproducibility of your scripts or projects at any
    time. To create the snapshot archives, once a day (at midnight UTC) Microsoft
    refreshes the Austria CRAN mirror on the "Microsoft R Archived Network"
    server (<https://mran.microsoft.com/>). Immediately after completion
    of the rsync mirror process, the process takes a snapshot, thus creating the
    archive. Snapshot archives exist starting from 2014-09-17.
Provides a lightweight data validation and testing toolkit for R. 
    Its guiding philosophy is that adding code-based data checks to users' 
    existing workflow should be both quick and intuitive. The suite of 
    functions included therefore mirror the common data checks many users 
    already perform by hand or by eye. Additionally, the 'checkthat' package is 
    optimized to work within 'tidyverse' data manipulation pipelines.
Provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. Allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients.
Given a non-linear model, calculate the local explanation. 
    We purpose view the data space, explanation 
    space, and model residuals as ensemble graphic interactive on a shiny 
    application. After an observation of interest is identified, the normalized 
    variable importance of the local explanation is used as a 1D projection
    basis. The support of the local explanation is then explored by changing
    the basis with the use of the radial tour <doi:10.32614/RJ-2020-027>; 
    <doi:10.1080/10618600.1997.10474754>.
Contains tools for working with data during statistical analysis, promoting flexible, intuitive, and reproducible workflows. There are functions designated for specific statistical tasks such building a custom univariate descriptive table, computing pairwise association statistics, etc. These are built on a collection of data manipulation tools designed for general use that are motivated by the functional programming concept.
Contains the Multi-Species Acute Toxicity Database (CAS & SMILES
    columns only) [United States (US) Department of Health and Human Services
    (DHHS) National Institutes of Health (NIH) National Cancer Institute (NCI),
    "Multi-Species Acute Toxicity Database",
    <https://cactus.nci.nih.gov/download/acute-toxicity-db/>] combined with the
    Toxic Substances Control Act (TSCA) Inventory [United States Environmental
    Protection Agency (US EPA), "Toxic Substances Control Act (TSCA) Chemical
    Substance Inventory",
    <https://www.epa.gov/tsca-inventory/how-access-tsca-inventory} and
    <https://cdxapps.epa.gov/oms-substance-registry-services/substance-list-details/169>]
    and the Agency for Toxic Substances and Disease Registry (ATSDR) Database
    [United States (US) Department of Health and Human Services (DHHS) Centers
    for Disease Control and Prevention (CDC)/Agency for Toxic Substances and
    Disease Registry (ATSDR), "Agency for Toxic Substances and Disease Registry
    (ATSDR) Database",
    <https://cdxapps.epa.gov/oms-substance-registry-services/substance-list-details/105>]
    in 2 data sets. One data set has a focus on the latter 2 databases and one
    data set focuses on the former database. Also contains the collection of
    chemical data from Wikipedia compiled in the US EPA CompTox Chemicals
    Dashboard [United States Environmental Protection Agency (US EPA) /
    Wikimedia Foundation, Inc. "CompTox Chemicals Dashboard v2.2.1",
    <https://comptox.epa.gov/dashboard/chemical-lists/WIKIPEDIA>].
Combines taxonomic classifications of high-throughput 16S rRNA
  gene sequences with reference proteomes of archaeal and bacterial taxa to
  generate amino acid compositions of community reference proteomes. Calculates
  chemical metrics including carbon oxidation state ('Zc'), stoichiometric
  oxidation and hydration state ('nO2' and 'nH2O'), H/C, N/C, O/C, and S/C
  ratios, grand average of hydropathicity ('GRAVY'), isoelectric point ('pI'),
  protein length, and average molecular weight of amino acid residues. Uses
  precomputed reference proteomes for archaea and bacteria derived from the
  Genome Taxonomy Database ('GTDB'). Also includes reference proteomes derived
  from the NCBI Reference Sequence ('RefSeq') database and manual mapping from
  the 'RDP Classifier' training set to 'RefSeq' taxonomy as described by Dick and
  Tan (2023) <doi:10.1007/s00248-022-01988-9>. Processes taxonomic
  classifications in 'RDP Classifier' format or OTU tables in 'phyloseq-class'
  objects from the Bioconductor package 'phyloseq'.
Simple functions for plotting linear
	calibration functions and estimating standard errors for measurements
	according to the Handbook of Chemometrics and Qualimetrics: Part A
	by Massart et al. (1997) There are also functions estimating the limit
	of detection (LOD) and limit of quantification (LOQ).
	The functions work on model objects from - optionally weighted - linear
	regression (lm) or robust linear regression ('rlm' from the 'MASS' package).
A collection of functions that have been developed to assist
    experimenter in modeling chemical degradation kinetic data. The
    selection of the appropriate degradation model and parameter
    estimation is carried out automatically as far as possible and is
    driven by a rigorous statistical interpretation of the results.  The
    package integrates already available goodness-of-fit statistics for
    nonlinear models. In addition it allows data fitting with the
    nonlinear first-order multi-target (FOMT) model.
We aim to deal with the average treatment effect (ATE), where the data are
    subject to high-dimensionality and measurement error. This package primarily contains two 
    functions, which are used to generate artificial data and estimate ATE with high-dimensional 
    and error-prone data accommodated.
Contains a set of methods for fitting models and methods for
    validating the resulting models. The statistical methodologies comprise
    a comprehensive collection of approaches whose validity and utility have
    been accepted by experts in the Cheminformatics field. As promising new
    methodologies emerge from the statistical and data-mining communities, they
    will be incorporated into the laboratory. These methods are aimed at discovering
    quantitative structure-activity relationships (QSARs). However, the user can
    directly input their own choices of descriptors and responses, so the capability
    for comparing models is effectively unlimited.
Quantify and visualise various measures of chemical diversity and
    dissimilarity, for phytochemical compounds and other sets of chemical
    composition data. Importantly, these measures can incorporate biosynthetic
    and/or structural properties of the chemical compounds, resulting in a more
    comprehensive quantification of diversity and dissimilarity. For details,
    see Petrén, Köllner and Junker (2023) <doi:10.1111/nph.18685>.
R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).
A collection of functions for top-down exploratory data analysis
    of spectral data including nuclear magnetic resonance (NMR), infrared (IR),
    Raman, X-ray fluorescence (XRF) and other similar types of spectroscopy.
    Includes functions for plotting and inspecting spectra, peak alignment,
    hierarchical cluster analysis (HCA), principal components analysis (PCA) and
    model-based clustering. Robust methods appropriate for this type of
    high-dimensional data are available. ChemoSpec is designed for structured
    experiments, such as metabolomics investigations, where the samples fall into
    treatment and control groups. Graphical output is formatted consistently for
    publication quality plots. ChemoSpec is intended to be very user friendly and
    to help you get usable results quickly. A vignette covering typical operations
    is available.
A collection of functions for exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra. 'ChemoSpec2D' deploys methods aimed primarily at classification of samples and the identification of spectral features which are important in distinguishing samples from each other. Each 2D spectrum (a matrix) is treated as the unit of observation, and thus the physical sample in the spectrometer corresponds to the  sample from a statistical perspective.  In addition to chemometric tools, a few tools are provided for plotting 2D spectra, but these are not intended to replace the functionality typically available on the spectrometer. 'ChemoSpec2D' takes many of its cues from 'ChemoSpec' and tries to create consistent graphical output and to be very user friendly.
Functions supporting the common needs of packages 'ChemoSpec' and 'ChemoSpec2D'.
Computes Chernoff's distribution based on the method in Piet Groeneboom & Jon A Wellner (2001) Computing Chernoff's Distribution, Journal of Computational and Graphical Statistics, 10:2, 388-400, <doi:10.1198/10618600152627997>.    Chernoff's distribution is defined as the distribution of the maximizer of the two-sided Brownian motion minus quadratic drift. That is, Z = argmax (B(t)-t^2).
Provides an alternative approach to multiple testing
        by calculating a simultaneous upper confidence bounds for the
        number of true null hypotheses among any subset of the hypotheses of interest, 
        using the methods of Goeman and Solari (2011) <doi:10.1214/11-STS356>.
Race results of the Cherry Blossom Run, which is an annual road race that takes place in Washington, DC.
This is an opinionated wrapper around the
    python-chess package. It allows users to read and write PGN files as
    well as create and explore game trees such as the ones seen in chess
    books.
Provides functions to work with directed (asymmetric) and 
    undirected (symmetric) spatial networks. It makes the creation of 
    connectivity matrices easier, i.e. a binary matrix of dimension n x n, where 
    n is the number of nodes (sampling units) indicating the presence (1) or  
    the absence (0) of an edge (link) between pairs of nodes. Different network
    objects can be produced by 'chessboard': node list, neighbor list, edge 
    list, connectivity matrix. It can also produce objects that will be used 
    later in Moran's Eigenvector Maps (Dray et al. (2006) <doi:10.1016/j.ecolmodel.2006.02.015>)
    and Asymetric Eigenvector Maps (Blanchet et al. (2008) <doi:10.1016/j.ecolmodel.2008.04.001>), 
    methods available in the package 'adespatial' (Dray et al. (2023) 
    <https://CRAN.R-project.org/package=adespatial>). This work is part of the 
    FRB-CESAB working group Bridge 
    <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/bridge/>.
Applies the change-in-effect estimate method to assess confounding 
    effects in medical and epidemiological research (Greenland & Pearce (2016) 
    <doi:10.1146/annurev-publhealth-031914-122559> ). It starts with a crude model 
    including only the outcome and exposure variables. At each of the subsequent 
    steps, one variable which creates the largest change among the remaining variables 
    is selected. This process is repeated until all variables have been entered into 
    the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183–196). Currently, the 'chest' 
    package has functions for linear regression, logistic regression, negative 
    binomial regression, Cox proportional hazards model and conditional logistic 
    regression.
Provide standard tables, listings, and graphs (TLGs)
    libraries used in clinical trials. This package implements a structure
    to reformat the data with 'dunlin', create reporting tables using
    'rtables' and 'tern' with standardized input arguments to enable quick
    generation of standard outputs.  In addition, it also provides
    comprehensive data checks and script generation functionality.
The software matches the current history to the closest history in a time series to build a forecast.
Light weight implementation of the standard distribution 
  functions for the chi distribution, wrapping those for the chi-squared 
  distribution in the stats package.
Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model.
Measuring child development starts by collecting responses to 
    developmental milestones, such as "able to sit" or "says two words". 
    There are many ways to combine such responses into summaries. 
    The package bundles publicly available datasets with individual milestone 
    data for children aged 0-5 years, with the aim of supporting the construction, 
    evaluation, validation and interpretation of methodologies that aggregate 
    milestone data into informative measures of child development.
Tools for connecting to 'CHILDES', an open repository for
    transcripts of parent-child interaction. For more information on the 
    underlying data, see <https://langcog.github.io/childes-db-website/>.
Reads demographic data from a variety of public data sources, extracting and harmonizing variables useful for the study of childfree individuals. The identification of childfree individuals and those with other family statuses uses Neal & Neal's (2024) "A Framework for Studying Adults who Neither have Nor Want Children" <doi:10.1177/10664807231198869>; A pre-print is available at <doi:10.31234/osf.io/fa89m>.
Calculation of standard deviation scores and percentiles adduced from different
    standards (WHO, UK, Germany, Italy, China, etc). Also, references for laboratory values in
    children and adults are available, e.g., serum lipids, iron-related blood parameters, IGF, liver enzymes. See package documentation for full list.
Mapas terrestres con topologias simplificadas. Estos mapas no 
  tienen precision geodesica, por lo que aplica el DFL-83 de 1979 de la Republica
  de Chile y se consideran referenciales sin validez legal.
  No se incluyen los territorios antarticos y bajo ningun evento estos mapas
  significan que exista una cesion u ocupacion de territorios soberanos en
  contra del Derecho Internacional por parte de Chile. Esta paquete esta 
  documentado intencionalmente en castellano asciificado para que funcione sin 
  problema en diferentes plataformas.
  (Terrestrial maps with simplified toplogies. These maps lack geodesic
  precision, therefore DFL-83 1979 of the Republic of Chile applies and are
  considered to have no legal validity.
  Antartic territories are excluded and under no event these maps mean
  there is a cession or occupation of sovereign territories against International
  Laws from Chile. This package was intentionally documented in asciified
  spanish to make it work without problem on different platforms.)
Calculates the chilling and heat accumulation for studies of the temperate fruit trees. The models in this package are: Utah (Richardson et al., 1974, ISSN:0018-5345), Positive Chill Units - PCU (Linsley-Noaks et al., 1995, ISSN:1017-0316), GDH-A - Growing Degree Hours by Anderson et al.(1986, ISSN:0567-7572), GDH-R - Growing Degree Hours by Richardson et al.(1975, ISSN:0018-5345), North Carolina (Shaltout e Unrath, 1983, ISSN:0003-1062), Landsberg Model (Landsberg, 1974, ISSN:0305-7364), Q10 Model (Bidabe, 1967, ISSN:0031-9368), Jones Model (Jones et al., 2013 <DOI:10.1111/j.1438-8677.2012.00590.x>), Low-Chill Model (Gilreath and Buchanan, 1981, ISSN:0003-1062), Model for Cherry "Sweetheart" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Model for apple "Gala" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Taiwan Model (Lu et al., 2012 <DOI:10.17660/ActaHortic.2012.962.35>), Dynamic Model (Fishman et al., 1987, ISSN:0022-5193) adapted from the function Dynamic_Model() of the 'chillR' package (Luedeling, 2018), Unified Model (Chuine et al., 2016 <DOI:10.1111/gcb.13383>) and Heat Restriction model.
The phenology of plants (i.e. the timing of their annual life
    phases) depends on climatic cues. For temperate trees and many other plants,
    spring phases, such as leaf emergence and flowering, have been found to result
    from the effects of both cool (chilling) conditions and heat. Fruit tree
    scientists (pomologists) have developed some metrics to quantify chilling
    and heat (e.g. see Luedeling (2012) <doi:10.1016/j.scienta.2012.07.011>).
    'chillR' contains functions for processing temperature records into
    chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units
    (Growing Degree Hours). Regarding chilling metrics, Chill Portions are often
    considered the most promising, but they are difficult to calculate. This package
    makes it easy. 'chillR' also contains procedures for conducting a PLS analysis
    relating phenological dates (e.g. bloom dates) to either mean temperatures or
    mean chill and heat accumulation rates, based on long-term weather and phenology
    records (Luedeling and Gassner (2012) <doi:10.1016/j.agrformet.2011.10.020>).
    As of version 0.65, it also includes functions for generating weather
    scenarios with a weather generator, for conducting climate change analyses
    for temperature-based climatic metrics and for plotting results from such
    analyses. Since version 0.70, 'chillR' contains a function for interpolating
    hourly temperature records.
Efforts are made to make Chinese text mining easier, faster, and robust to errors. 
    Document term matrix can be generated by only one line of code; detecting encoding, 
    segmenting and removing stop words are done automatically. 
	Some convenient tools are also supplied.
A database of Chinese surnames and Chinese given names (1930-2008).
    This database contains nationwide frequency statistics of
    1,806 Chinese surnames and 2,614 Chinese characters used in given names,
    covering about 1.2 billion Han Chinese population
    (96.8% of the Han Chinese household-registered population
    born from 1930 to 2008 and still alive in 2008).
    This package also contains a function for computing multiple features of
    Chinese surnames and Chinese given names for scientific research (e.g.,
    name uniqueness, name gender, name valence, and name warmth/competence).
A collection of functions to pre-process amplification curve data from polymerase chain reaction (PCR) or isothermal amplification reactions. Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems ('VideoScan HCU', capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used. For more information see: Roediger et al. (2015) <doi:10.1093/bioinformatics/btv205>.
API Client for the Climate Hazards Center 'CHIRPS' and 'CHIRTS'. 
  The 'CHIRPS' data is a quasi-global (50°S – 50°N) high-resolution 
  (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery 
  and in-situ station data to create gridded rainfall time series for trend 
  analysis and seasonal drought monitoring. 'CHIRTS' is a quasi-global 
  (60°S – 70°N), high-resolution data set of daily maximum and minimum
  temperatures. For more details on 'CHIRPS' and 'CHIRTS' data please visit 
  its official home page <https://www.chc.ucsb.edu/data>.
Perform post hoc analysis based on residuals of Pearson's Chi-squared Test for Count Data based on T. Mark Beasley & Randall E. Schumacker (1995) <doi: 10.1080/00220973.1995.9943797>.
Provides the facility to perform the chi-square and G-square test of independence, calculates the power of the traditional chi-square test, compute permutation and Monte Carlo p-value, and provides measures of association such as Phi, odds ratio with 95 percent CI and p-value, adjusted contingency coefficient, Cramer's V and 95 percent CI, bias-corrected Cramer's V, W, Cohen's w, Goodman-Kruskal's lambda, gamma and its p-value, and tau, Cohen's k and its 95 percent CI. It also calculates standardized, moment-corrected standardized, and adjusted standardized residuals, and their significance. Different outputs are returned in nicely formatted tables.
For developers to check user-supplied function arguments.  It
    is designed to be simple, fast and customizable.  Error messages
    follow the tidyverse style guide.
Fit Bayesian models in Stan <doi: 10.18637/jss.v076.i01> 
  with checkpointing, that is, the ability to stop the MCMC sampler at 
  will, and then pick right back up where the MCMC sampler left off. 
  Custom 'Stan' models can be fitted, or the popular package 'brms' 
  <doi: 10.18637/jss.v080.i01> can be used to generate the 'Stan' code. This 
  package is fully compatible with the R packages 'brms', 'posterior', 'cmdstanr', 
  and 'bayesplot'.
As different antipsychotic medications have different potencies,
    the doses of different medications cannot be directly compared. Various
    strategies are used to convert doses into a common reference so that
    comparison is meaningful. Chlorpromazine (CPZ) has historically been used
    as a reference medication into which other antipsychotic doses can be
    converted, as "chlorpromazine-equivalent doses". Using conversion keys
    generated from widely-cited scientific papers, e.g. Gardner et. al 2010
    <doi:10.1176/appi.ajp.2009.09060802> and Leucht et al. 2016
    <doi:10.1093/schbul/sbv167>, antipsychotic doses are converted
    to CPZ (or any specified antipsychotic) equivalents. The use of the package
    is described in the included vignette. Not for clinical use.
An exact and a variational inference for
    coupled Hidden Markov Models applied to the joint detection of copy number variations.
Compute Chinese capital stocks in provinces level, based on Zhang (2008) <DOI:10.1080/14765280802028302>.
Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models, with and without interaction terms. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) <DOI:10.1186/s12859-017-1863-x> and the package vignette.
An integrated set of tools for thermodynamic calculations in
  aqueous geochemistry and geobiochemistry. Functions are provided for writing
  balanced reactions to form species from user-selected basis species and for
  calculating the standard molal properties of species and reactions, including
  the standard Gibbs energy and equilibrium constant. Calculations of the
  non-equilibrium chemical affinity and equilibrium chemical activity of species
  can be portrayed on diagrams as a function of temperature, pressure, or
  activity of basis species; in two dimensions, this gives a maximum affinity or
  predominance diagram. The diagrams have formatted chemical formulas and axis
  labels, and water stability limits can be added to Eh-pH, oxygen fugacity-
  temperature, and other diagrams with a redox variable. The package has been
  developed to handle common calculations in aqueous geochemistry, such as
  solubility due to complexation of metal ions, mineral buffers of redox or pH,
  and changing the basis species across a diagram ("mosaic diagrams"). CHNOSZ
  also implements a group additivity algorithm for the standard thermodynamic
  properties of proteins.
Design functions for DCMs and other types of choice studies (including MaxDiff and other tradeoffs).
Implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a normal heterogeneity distribution. The algorithm uses a hybrid Gibbs Sampler with a random walk metropolis step for the MNL coefficients for each unit. Dependent variable may be discrete or continuous. Independent variables may be discrete or continuous with optional order constraints. Means of the distribution of heterogeneity can optionally be modeled as a linear function of unit characteristics variables.
Collection of utility functions for visualizing
    body map data collected with the Collaborative Health Outcomes
    Information Registry.
Amends errors, augments data and aids analysis of John Snow's map
  of the 1854 London cholera outbreak.
Sampling from the Cholesky factorization of a Wishart random 
    variable, sampling from the inverse Wishart distribution, sampling from 
    the Cholesky factorization of an inverse Wishart random variable, sampling
    from the pseudo Wishart distribution, sampling from the generalized
    inverse Wishart distribution, computing densities for the Wishart 
    and inverse Wishart distributions, and computing the multivariate gamma 
    and digamma functions. Provides a header file so the C functions can be
    called directly from other programs.
One way to choose the number of principal components is via the reconstruction error. This package is designed mainly for this purpose. Graphical representation is also supported, plus some other principal component analysis related functions. References include: Jolliffe I.T. (2002). Principal Component Analysis. <doi:10.1007/b98835> and Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press.
Calculates permutation tests that can be powerful for comparing two groups with some positive but many zero responses (see Follmann, Fay, and Proschan <DOI:10.1111/j.1541-0420.2008.01131.x>).
Resampling is a standard step in particle filtering and in
    sequential Monte Carlo. This package implements the chopthin resampler, which
    keeps a bound on the ratio between the largest and the smallest weights after
    resampling.
Learning the structure of graphical models from datasets with thousands of variables.
    More information about the research papers detailing the theory behind Chordalysis is available at
    <http://www.francois-petitjean.com/Research> (KDD 2016, SDM 2015, ICDM 2014, ICDM 2013).
    The R package development site is <https://github.com/HerrmannM/Monash-ChoR>.
Maximum likelihood estimation in respondent driven samples.
Choropleths are thematic maps where geographic regions, such as
    states, are colored according to some metric, such as the number of people
    who live in that state. This package simplifies this process by 1.
    Providing ready-made functions for creating choropleths of common maps. 2.
    Providing data and API connections to interesting data sources for making
    choropleths. 3. Providing a framework for creating choropleths from
    arbitrary shapefiles. 4. Overlaying those maps over reference maps from
    Google Maps.
Contains an administrative-level-1 map of the world.
    Administrative-level-1 is the generic term for the largest sub-national
    subdivision of a country. This package was created for use with the
    choroplethr package.
Contains 3 maps. 1) US States 2) US Counties 3) Countries of the
    world.
An engine for stochastic cellular automata. It provides a high-level
  interface to declare a model, which can then be simulated by various backends
  (Genin et al. (2023) <doi:10.1101/2023.11.08.566206>).
Generation of different Christmas cards, most of them being
    animated. Most of the cards can be generated in three languages (English,
    Catalan and Spanish). The collection started in 2009.
Reads chromatograms from binary formats into R objects. Currently supports conversion of 'Agilent ChemStation', 'Agilent MassHunter', and 'ThermoRaw' files as well as various text-based formats. Utilizes file parsers from external libraries, such as 'Aston' <https://github.com/bovee/aston>, 'Entab' <https://github.com/bovee/entab>, and 'ThermoRawFileParser' <https://github.com/compomics/ThermoRawFileParser>.
A programmatic interface to the Chromosome Counts Database
    (<https://taux.evolseq.net/CCDB_web/>), Rice et al. (2014) <doi:10.1111/nph.13191>.
    This package is part of the 'ROpenSci' suite (<https://ropensci.org>).
Provides interactive, configurable and elegant graphics visualization of the chromosomes or chromosome regions
    of any living organism allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces
    a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data
    associated with chromosome elements (like gene expression) in the form of heat colors which can be highly
    advantageous in the scientific interpretations and research work. Because of the large size of the chromosomes,
    it is impractical to visualize each element on the same plot. However, the plot provides a magnified view for each
    of chromosome locus to render additional information and visualization specific for that location. You can map
    thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings
    (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the
    chromosome at a location showing each mapped element in sequential order. The package provide multiple features
    like visualizing multiple sets, chromosome heat-maps, group annotations, adding hyperlinks, and labelling.
    The plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R 'Shiny' applications.
An implementation of the 'Chrome DevTools Protocol', for
    controlling a headless Chrome web browser.
Chromosome files in the 'Fasta' format usually contain large sequences like human genome.
  Sometimes users have to split these chromosomes into different files according to their 
  chromosome number. The 'chromseq' can help to handle this. So the selected chromosome sequence can be
  used for downstream analysis like motif finding. Howard Y. Chang(2019)
  <doi:10.1038/s41587-019-0206-z>.
Provides chronological objects which can handle dates and times.
A system for creating R Markdown reports with a sequential syntax.
Decorate functions to make them return enhanced output. The enhanced output consists in an object of type
  'chronicle' containing the result of the function applied to its arguments, as well as a log detailing when the function
  was run, what were its inputs, what were the errors (if the function failed to run) and other useful information.
  Tools to handle decorated functions are included, such as a forward pipe operator that makes chaining decorated functions possible.
Easy way to draw chronological charts from tables, aiming to include an intuitive environment for anyone new to R. Includes 'ggplot2' geoms and theme for chronological charts.
The implemented functions allow the query, download, and import of remotely-stored and version-controlled data items. The inherent meta-database maps data files and import code to programming classes and allows access to these items via files deposited in public repositories. The purpose of the project is to increase reproducibility and establish version tracking of results from (paleo)environmental/ecological research.
Data stored in text file can be processed chunkwise using 'dplyr' commands. These
    are recorded and executed per data chunk, so large files can be processed with
    limited memory using the 'LaF' package.
Set chunk hooks for 'R Markdown' documents <https://rmarkdown.rstudio.com/>,
    and improve user experience.
    For example, change units of figure sizes, benchmark chunks, and number
    lines on code blocks.
Read tables chunk by chunk using a C++ backend and a simple R interface.
An implementation of the probability mass function, cumulative density function, quantile function, random number generator, maximum likelihood estimator, and p-value generator from a conditional hypergeometric distribution: the distribution of how many items are in the overlap of all samples when samples of arbitrary size are each taken without replacement from populations of arbitrary size.
Calculation of consensus values for atomic weights, isotope amount ratios, and isotopic abundances with the associated uncertainties using multivariate meta-regression approach for consensus building.
Identification of markers of rare cell types by looking at genes whose expression is confined in small regions of the expression space <https://github.com/ScialdoneLab>.
Clustering multi-subject resting state functional Magnetic Resonance Imaging data. This methods enables the clustering of subjects based on multi-subject resting state functional Magnetic Resonance Imaging data. Objects are clustered based on similarities and differences in cluster-specific estimated components obtained by Independent Component Analysis.
Provides functionality for computing support intervals for univariate parameters based on confidence intervals or parameter estimates with standard errors (Pawel et al., 2022) <doi:10.48550/arXiv.2206.12290>.
Estimation and inference methods for causal relative and attributable risk in case-control and case-population studies
  under the monotone treatment response and monotone treatment selection assumptions.
  For more details, see the paper by Jun and Lee (2023), 
  "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," <arXiv:2004.08318 [econ.EM]>,
  accepted for publication in Journal of Business & Economic Statistics.
Provide step by step guided tours of 'Shiny' applications.
Estimation of counterfactual outcomes for multiple values of continuous interventions at different time points, and plotting of causal dose-response curves. Details are given in Schomaker, McIlleron, Denti, Diaz (2023) <arXiv:2305.06645>.
In many studies across different disciplines, detailed measures of the variables of interest are available. If assumptions can be made regarding the direction of effects between the assessed variables, this has to be considered in the analysis. The functions in this package implement the novel approach CIEE (causal inference using estimating equations; Konigorski et al., 2018, <DOI:10.1002/gepi.22107>) for estimating and testing the direct effect of an exposure variable on a primary outcome, while adjusting for indirect effects of the exposure on the primary outcome through a secondary intermediate outcome and potential factors influencing the secondary outcome. The underlying directed acyclic graph (DAG) of this considered model is described in the vignette. CIEE can be applied to studies in many different fields, and it is implemented here for the analysis of a continuous primary outcome and a time-to-event primary outcome subject to censoring. CIEE uses estimating equations to obtain estimates of the direct effect and robust sandwich standard error estimates. Then, a large-sample Wald-type test statistic is computed for testing the absence of the direct effect. Additionally, standard multiple regression, regression of residuals, and the structural equation modeling approach are implemented for comparison.
Set of forecasting tools to predict ICU beds using a Vector Error Correction model with a single cointegrating vector. Method described in  Berta, P. Lovaglio, P.G. Paruolo, P. Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand" Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, Department of Economics, University of York, <https://www.york.ac.uk/media/economics/documents/hedg/workingpapers/2020/2016.pdf>.
Computes confidence intervals for the positive predictive value (PPV) and negative predictive value (NPV) based on varied scenarios. In situations where the proportion of diseased subjects does not correspond to the disease prevalence (e.g. case-control studies), this package provides two types of solutions: 1) five methods for estimating confidence intervals for PPV and NPV via ratio of two binomial proportions including Gart & Nam (1988), Walter (1975), MOVER-J (Laud, 2017), Fieller (1954), and Bootstrap (Efron, 1979); 2) three direct methods that compute the confidence intervals including Pepe (2003), Zhou (2007), and Delta. In prospective studies where the proportion of diseased subjects is an unbiased estimate of the disease prevalence, this package provides several methods for calculating the confidence intervals for PPV and NPV including Clopper-Pearson, Wald, Wilson, Agresti-Coull, and Beta. See the Details and References sections in the corresponding functions.
Functions for the input/output and visualization of
    medical imaging data in the form of 'CIFTI' files 
    <https://www.nitrc.org/projects/cifti/>.
CIFTI files contain brain imaging data in "grayordinates," which 
    represent the gray matter as cortical surface vertices (left and right) and
    subcortical voxels (cerebellum, basal ganglia, and other deep gray matter). 
    'ciftiTools' provides a unified environment for reading, writing, 
    visualizing and manipulating CIFTI-format data. It supports the "dscalar," 
    "dlabel," and "dtseries" intents. Grayordinate data is read in as a "xifti" 
    object, which is structured for convenient access to the data and metadata,
    and includes support for surface geometry files to enable
    spatially-dependent functionality such as static or interactive 
    visualizations and smoothing.
Produces statistical indicators of the impact of migration on the socio-demographic
	composition of an area. Three measures can be used: ratios, percentages and the Duncan index
	of dissimilarity. The input data files are assumed to be in an  origin-destination matrix format,
	with each cell representing a flow count between an origin and a destination area. Columns are expected
	to represent origins, and rows are expected to represent destinations. The first row and column are
	assumed to contain labels for each area. See Rodriguez-Vignoli and Rowe (2018)
	<doi:10.1080/00324728.2017.1416155> for technical details.
Connect to the California Irrigation Management 
    Information System (CIMIS) Web API. See the CIMIS main page 
    <https://cimis.water.ca.gov> and web API documentation
    <https://et.water.ca.gov> for more information.
Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Hu et al. <doi:10.1177/0962280220921909>.
Differential analyses and Enrichment pipeline for bulk 'ATAC-seq' data
  analyses. This package combines different packages to have an ultimate package
  for both data analyses and visualization of 'ATAC-seq' data. Methods are described in 
  'Karakaslar et al.' (2021) <doi:10.1101/2021.03.05.434143>.
Immune related gene sets provided along with the 'cinaR' package.
Function using lemmatization to classify educational programs according to the CINE(Classification International Normalized of Education) for Peru.
Method for identifying the instar of Curculionid larvae from the observed distribution of the headcapsule size of mature larvae.
Implement various chromosomal instability metrics. 'CINmetrics' (Chromosomal INstability metrics) provides functions to 
    calculate various chromosomal instability metrics on masked Copy Number Variation(CNV) data at individual sample level. The 
    chromosomal instability metrics have been implemented as described in the following studies: 
    Baumbusch LO et al. 2013 <doi:10.1371/journal.pone.0054356>, 
    Davidson JM et al. 2014 <doi:10.1371/journal.pone.0079079>, 
    Chin SF et al. 2007 <doi:10.1186/gb-2007-8-10-r215>.
Computing, comparing, and demonstrating top informative centrality measures within a network. "CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis" provides a comprehensive overview of the package functionality Ashtiani et al. (2018) <doi:10.1093/bioinformatics/bty819>.
Simple interpolation methods designed to be used from C
    code. Supports constant, linear and spline interpolation. An R
    wrapper is included but this package is primarily designed to be
    used from C code using 'LinkingTo'.  The spline calculations are
    classical cubic interpolation, e.g., Forsythe, Malcolm and Moler
    (1977) <ISBN: 9780131653320>.
Implements computationally-efficient construction of
    confidence intervals from permutation or randomization tests
    for simple differences in means,
    based on Nguyen (2009) <doi:10.15760/etd.7798>.
Encrypts and decrypts using basic ciphers. None of 
  these should be used in place of real encryption using state of the art tools. 
  The ciphers included use methods described in the ciphers's Wikipedia and 
  cryptography hobby websites.
Plot confidence interval from the objects of statistical tests such as
  t.test(), var.test(), cor.test(), prop.test() and fisher.test() ('htest' class),
  Tukey test [TukeyHSD()], Dunnett test [glht() in 'multcomp' package],
  logistic regression [glm()], and Tukey or Games-Howell test [posthocTGH() in
  'userfriendlyscience' package].
  Users are able to set the styles of lines and points.
  This package contains the function to calculate odds ratios and their confidence
  intervals from the result of logistic regression.
Isotonic regression (IR) and its improvement: centered isotonic regression (CIR). CIR is recommended in particular with small samples. Also, interval estimates for both, and additional utilities such as plotting dose-response data. For dev version and change history, see GitHub assaforon/cir.
Uses non-linear regression to statistically compare two circadian rhythms.
    Groups are only compared if both are rhythmic (amplitude is non-zero).
    Performs analyses regarding mesor, phase, and amplitude, reporting on estimates and statistical differences, for each, between groups.
    Details can be found in Parsons et al (2020) <doi:10.1093/bioinformatics/btz730>.
Wraps the 'CIRCE' (<https://github.com/ohdsi/circe-be>) 'Java'
  library allowing cohort definition expressions to be edited and converted to 
  'Markdown' or 'SQL'.
Tools for interacting with the 'Circle CI' API
    (<https://circleci.com/docs/api/v2/>). Besides executing common tasks
    such as querying build logs and restarting builds, this package also
    helps setting up permissions to deploy from builds.
Implementation of Librino, Levorato, and Zorzi (2014) <doi:10.1002/wcm.2305>
    algorithm for computation of the intersection areas of an arbitrary number of circles.
Method for visualizing proportions between objects of different sizes. 
    The proportions are drawn as circles with different diameters, which makes them ideal
    for visualizing proportions between planets.
Enables curving text elements in 'Shiny' apps.
Circular layout is an efficient way for the visualization of huge 
    amounts of information. Here this package provides an implementation 
    of circular layout generation in R as well as an enhancement of available 
    software. The flexibility of the package is based on the usage of low-level 
    graphics functions such that self-defined high-level graphics can be easily 
    implemented by users for specific purposes. Together with the seamless 
    connection between the powerful computational and visual environment in R, 
    it gives users more convenience and freedom to design figures for 
    better understanding complex patterns behind multiple dimensional data. 
    The package is described in Gu et al. 2014 <doi:10.1093/bioinformatics/btu393>.
A series of wrapper functions to
    implement the 10 maximum likelihood models of animal orientation
    described by Schnute and Groot (1992) <DOI:10.1016/S0003-3472(05)80068-5>. The
    functions also include the ability to use different optimizer
    methods and calculate various model selection metrics (i.e., AIC,
    AICc, BIC).  The ability to perform variants of the Hermans-Rasson test and
    Pycke test is also included as described in Landler et al. (2019) 
    <DOI:10.1186/s12898-019-0246-8>. The latest version also includes a new
    method to calculate circular-circular and circular-linear distance correlations.
Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more.
A collection of utilities for the statistical analysis of multivariate circular data using distributions based on Multivariate Nonnegative Trigonometric Sums (MNNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, and more.
Detection of outliers in circular-circular regression models, modifying its and estimating of models parameters.
Implementation of Bayesian models for spatial and spatio-temporal
             interpolation of circular data using Gaussian Wrapped and Gaussian Projected distributions.
             We developed the methods described in Jona Lasinio G. et al. (2012) <doi: 10.1214/12-aoas576>, 
             Wang F. et al. (2014) <doi: 10.1080/01621459.2014.934454> and 
             Mastrantonio G. et al. (2016) <doi: 10.1007/s11749-015-0458-y>.
Circular Statistics, from "Topics in Circular Statistics"
        (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Circular drift-diffusion model for continuous reports.
General functions for performing extreme value analysis on a circular 
    domain as part of the statistical methodology in the paper by Konzen, E., 
    Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667.
Calculating silhouette information for clusters on
 circular or linear data using fast algorithms. These algorithms run in
 linear time on sorted data, in contrast to quadratic time by the
 definition of silhouette. When used together with the fast and optimal
 circular clustering method FOCC (Debnath & Song 2021)
 <doi:10.1109/TCBB.2021.3077573> implemented in R package 'OptCirClust',
 circular silhouette can be maximized to find the optimal number of
 circular clusters; it can also be used to estimate the period of noisy
 periodical data.
Circumplex models, which organize constructs in a circle around two 
    underlying dimensions, are popular for studying interpersonal functioning, 
    mood/affect, and vocational preferences/environments. This package provides 
    tools for analyzing and visualizing circular data, including scoring 
    functions for relevant instruments and a generalization of the bootstrapped 
    structural summary method from Zimmermann & Wright (2017) 
    <doi:10.1177/1073191115621795> and functions for creating publication-ready 
    tables and figures from the results.
Jointly model the accuracy of cognitive responses and item choices
    within a Bayesian hierarchical framework as described by Culpepper and
    Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package
    contains the datasets used within the analysis of the paper.
An implementation of double generalized linear model (DGLM) building with variable selection procedures and handling of interaction terms and other complex situations. We also provide a method of handling convergence issues within the dglm() function. The package offers a simulation function for generating simulated data for testing purposes and utilizes the forward stepwise variable selection procedure in model-building. It also provides a new custom bootstrap function for mean and standard deviation estimation and functions for building crossplots and squareplots from a data set.
A likelihood-based hypothesis testing approach is implemented for
  assessing causal mediation. Described in Millstein, Chen, and Breton (2016),
  <DOI:10.1093/bioinformatics/btw135>, it could be used to test for mediation
  of a known causal association between a DNA variant, the 'instrumental variable',
  and a clinical outcome or phenotype by gene expression or DNA methylation, the
  potential mediator. Another example would be testing mediation of the effect
  of a drug on a clinical outcome by the molecular target. The hypothesis test
  generates a p-value or permutation-based FDR value with confidence intervals
  to quantify uncertainty in the causal inference. The outcome can be represented
  by either a continuous or binary variable, the potential mediator is continuous,
  and the instrumental variable can be continuous or binary and is not limited to
  a single variable but may be a design matrix representing multiple variables.
Supports quantitative
    research in scientometrics and bibliometrics. Provides
    various tools for preprocessing bibliographic
    data retrieved, e.g., from Elsevier's SciVerse Scopus,
    computing bibliometric impact of individuals,
    or modelling phenomena encountered in the social sciences.
    This package is deprecated, see 'agop' instead.
A collection of functions to extract citation information from 'R' packages and to deal with files in 'citation file format' (<https://citation-file-format.github.io/>), extending the functionality already provided by the citation() function in the 'utils' package.
In searching for research articles, we often want to 
  obtain lists of references from across studies, and also obtain lists 
  of articles that cite a particular study. In systematic reviews, this 
  supplementary search technique is known as 'citation chasing': forward 
  citation chasing looks for all records citing one or more articles of 
  known relevance; backward citation chasing looks for all records 
  referenced in one or more articles. Traditionally, this process would 
  be done manually, and the resulting records would need to be checked 
  one-by-one against included studies in a review to identify potentially 
  relevant records that should be included in a review. This package 
  contains functions to automate this process by making use of the 
  Lens.org API. An input article list can be used to return a list of 
  all referenced records, and/or all citing records in the Lens.org 
  database (consisting of PubMed, PubMed Central, CrossRef, Microsoft 
  Academic Graph and CORE; <https://www.lens.org>).
Client for the Open Citations Corpus (<http://opencitations.net/>).
    Includes a set of functions for getting one identifier type from another,
    as well as getting references and citations for a given identifier.
Simulates clinical trials and summarizes causal effects and treatment policy estimands in the presence of intercurrent events in a transparent and intuitive manner.
Downloads the Representative Market Rate Exchange (RMRE) from the <www.datos.gov.co> source. Allows setting the data series in time frequencies, splitting the time series through start and end functions, transforming the data set in log returns or levels, and making a Dynamic graph.
The 'cito' package provides a user-friendly interface for training and interpreting deep neural networks (DNN). 'cito' simplifies the fitting of DNNs by supporting the familiar formula syntax, hyperparameter tuning under cross-validation, and helps to detect and handle convergence problems.  DNNs can be trained on CPU, GPU and MacOS GPUs. In addition, 'cito' has many downstream functionalities such as various explainable AI (xAI) metrics (e.g. variable importance, partial dependence plots, accumulated local effect plots, and effect estimates) to interpret trained DNNs. 'cito' optionally provides confidence intervals (and p-values) for all xAI metrics and predictions. At the same time, 'cito' is computationally efficient because it is based on the deep learning framework 'torch'. The 'torch' package is native to R, so no Python installation or other API is required for this package.
Functions to append confidence intervals, prediction intervals,
    and other quantities of interest to data frames. All appended quantities
    are for the response variable, after conditioning on the model and covariates.
    This package has a data frame first syntax that allows for easy piping.
    Currently supported models include (log-) linear, (log-) linear mixed,
    generalized linear models, generalized linear mixed models, and
    accelerated failure time models.
A tool to easily run and visualise supervised and unsupervised state of the art customer segmentation. 
    It is built like a pipeline covering the 3 main steps in a segmentation project: pre-processing, modelling, and plotting.
    Users can either run the pipeline as a whole, or choose to run any one of the three individual steps.
    It is equipped with a supervised option (tree optimisation) and an unsupervised option (k-clustering) as default models.
Implementation of the Contextual Importance and Utility 
    (CIU) concepts for Explainable AI (XAI). A recent description of CIU
    can be found in e.g. Främling (2020) <arXiv:2009.13996>.
Computes a confidence interval for a specified linear combination of 
    the regression parameters in a linear regression model with iid normal 
    errors with unknown variance when there is uncertain prior information 
    that a distinct specified linear combination of the regression 
    parameters takes a specified number. This confidence interval, found by 
    numerical nonlinear constrained optimization, has the required minimum coverage 
    and utilizes this uncertain prior information through desirable 
    expected length properties. This confidence interval is proposed by 
    Kabaila, P. and Giri, K. (2009) <doi:10.1016/j.jspi.2009.03.018>.
Implementation of the categorical instrumental variable (CIV) 
    estimator proposed by Wiemann (2023) <arXiv:2311.17021>. CIV allows for optimal instrumental 
    variable estimation in settings with relatively few observations per 
    category. To obtain valid inference in these challenging settings, CIV
    leverages a regularization assumption that implies existence of a latent 
    categorical variable with fixed finite support achieving the same first 
    stage fit as the observed instrument.
A convenient interface for making
  requests directly to the 'Civis Platform API' <https://www.civisanalytics.com/platform/>.
  Full documentation available 'here' <https://civisanalytics.github.io/civis-r/>.
Directory reads and summaries are provided for one or more of the subdirectories
 of the <https://cran.r-project.org/incoming/> directory, and a compact summary object is returned.
 The package name is a contraption of 'CRAN Incoming Watcher'.
We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2019, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, <DOI:10.1186/s12919-016-0045-6>; Konigorski, Yilmaz, Bull, 2014, <DOI:10.1186/1753-6561-8-S1-S72>). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers.
Connect to the 'CJA' API, which powers 'CJA Workspace' <https://github.com/AdobeDocs/cja-apis>. The package 
             was developed with the analyst in mind and will continue to be 
             developed with the guiding principles of iterative, repeatable, 
             timely analysis. New features are actively being developed and we 
             value your feedback and contribution to the process.
A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.
Joint and Individual Variation Explained (JIVE) is a method for decomposing multiple datasets obtained on the same subjects into
		shared structure, structure unique to each dataset, and noise. The two most common implementations are R.JIVE, an iterative
		approach, and AJIVE, which uses principal angle analysis. JIVE estimates subspaces but interpreting these subspaces can be
		challenging with AJIVE or R.JIVE. We expand upon insights into AJIVE as a canonical correlation analysis (CCA) of principal component
		scores. This reformulation, which we call CJIVE, 1) provides an ordering of joint components by the degree of correlation between
		corresponding canonical variables; 2) uses a computationally efficient permutation test for the number of joint components, which
		provides a p-value for each component; and 3) can be used to predict subject scores for out-of-sample observations.
		Please cite the following article when utilizing this package: 
		Murden, R., Zhang, Z., Guo, Y., & Risk, B. (2022) <doi:10.3389/fnins.2022.969510>.
An R implementation of the Average Marginal Component-specific
    Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto
    T. (2014) <DOI:10.1093/pan/mpt024> Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional
    Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.
Client for 'CKAN' API (<https://ckan.org/>). Includes interface
    to 'CKAN' 'APIs' for search, list, show for packages, organizations, and
    resources. In addition, provides an interface to the 'datastore' API.
Composite Kernel Association Test (CKAT) is a flexible and robust kernel machine based approach to jointly test the genetic main effect and gene-treatment interaction effect for a set of single-nucleotide polymorphisms (SNPs) in pharmacogenetics (PGx) assessments embedded within randomized clinical trials.
Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).)
Fast, optimal, and reproducible weighted univariate
 clustering by dynamic programming. Four problems are solved, including
 univariate k-means (Wang & Song 2011) <doi:10.32614/RJ-2011-015>
 (Song & Zhong 2020) <doi:10.1093/bioinformatics/btaa613>, k-median,
 k-segments, and multi-channel weighted k-means. Dynamic programming
 is used to minimize the sum of (weighted) within-cluster distances
 using respective metrics. Its advantage over heuristic clustering in
 efficiency and accuracy is pronounced when there are many clusters.
 Multi-channel weighted k-means groups multiple univariate
 signals into k clusters. An auxiliary function generates histograms
 adaptive to patterns in data. This package provides a powerful set
 of tools for univariate data analysis with guaranteed optimality,
 efficiency, and reproducibility, useful for peak calling on temporal,
 spatial, and spectral data.
Provides an R wrapper around the program 'spip'
    (<https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1471-8286.2005.00884.x>), a C
    program for the simulation of pedigrees within age-structured populations
    with user-specified life histories.  Also includes a variety
    of functions to parse 'spip' output to compile information about
    related pairs amongst simulated, sampled individuals, to assess
    the feasibility and potential accuracy of close-kin mark-recapture (CKMR).
    Full documentation
    and vignettes are mirrored at <https://eriqande.github.io/CKMRpop/index.html>
    and can be read online there.
Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical
  mean-variance portfolio optimization, see Markowitz (1952) <doi:10.2307/2975974>.
  Care has been taken for correctness in light of previous buggy implementations.
Measures morphological diversity from discrete character data and
    estimates evolutionary tempo on phylogenetic trees. Imports morphological
    data from #NEXUS (Maddison et al. (1997) <doi:10.1093/sysbio/46.4.590>)
    format with read_nexus_matrix(), and writes to both #NEXUS and TNT format 
    (Goloboff et al. (2008) <doi:10.1111/j.1096-0031.2008.00217.x>). Main
    functions are test_rates(), which implements AIC and likelihood
    ratio tests for discrete character rates introduced across Lloyd et al.
    (2012) <doi:10.1111/j.1558-5646.2011.01460.x>, Brusatte et al. (2014)
    <doi:10.1016/j.cub.2014.08.034>, Close et al. (2015)
    <doi:10.1016/j.cub.2015.06.047>, and Lloyd (2016) <doi:10.1111/bij.12746>,
    and MatrixDistances(), which implements multiple discrete character
    distance metrics from Gower (1971) <doi:10.2307/2528823>, Wills (1998)
    <doi:10.1006/bijl.1998.0255>, Lloyd (2016) <doi:10.1111/bij.12746>, and
    Hopkins and St John (2018) <doi:10.1098/rspb.2018.1784>. This also includes
    the GED correction from Lehmann et al. (2019) <doi:10.1111/pala.12430>.
    Multiple functions implement morphospace plots:
    plot_chronophylomorphospace() implements Sakamoto and Ruta (2012)
    <doi:10.1371/journal.pone.0039752>, plot_morphospace() implements Wills et
    al. (1994) <doi:10.1017/S009483730001263X>, plot_changes_on_tree()
    implements Wang and Lloyd (2016) <doi:10.1098/rspb.2016.0214>, and
    plot_morphospace_stack() implements Foote (1993)
    <doi:10.1017/S0094837300015864>. Other functions include
    safe_taxonomic_reduction(), which implements Wilkinson (1995)
    <doi:10.1093/sysbio/44.4.501>, map_dollo_changes() implements
    the Dollo stochastic character mapping of Tarver et al. (2018)
    <doi:10.1093/gbe/evy096>, and estimate_ancestral_states() implements
    the ancestral state options of Lloyd (2018) <doi:10.1111/pala.12380>.
Various cladogenesis-related calculations that are slow in pure R are implemented in C++ with Rcpp. These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events.  Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state).  This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue.  C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations.  CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it!  Type: citation(package="cladoRcpp") to get the citation information.
The analysis of conflicting claims arises when an amount has to be divided among a set of agents with claims that exceed what is available. A rule is a way of selecting a  division among the claimants. This package computes the main rules introduced in the literature from the old times until nowadays. The inventory of rules covers the proportional and the adjusted proportional rules, the constrained equal awards and the constrained equal losses rules, the  constrained egalitarian, the Piniles’ and the minimal overlap rules, the random arrival and the Talmud rules. Besides, the Dominguez and Thomson and the average of awards rules are also included. All of them can be found in the book of W. Thomson (2019), 'How to divide when there isn't enough. From Aristotle, the Talmud, and Maimonides to the axiomatics of resource allocation', with the exception of the average of awards rule (Mirás Calvo et al. (2022), <doi:10.1007/s00355-022-01414-6>). In addition,  graphical diagrams allow the user to represent, among others, the set of awards, the paths of awards, and the schedules of awards of a rule, and some indexes. A good understanding of the similarities and the differences of the rules is useful for a better decision making. Therefore this package could be helpful to students, researchers and managers alike.
Performs 'classical' age-depth modelling of dated sediment deposits - prior to applying more sophisticated techniques such as Bayesian age-depth modelling. Any radiocarbon dated depths are calibrated. Age-depth models are constructed by sampling repeatedly from the dated levels, each time drawing age-depth curves. Model types include linear interpolation, linear or polynomial regression, and a range of splines. See Blaauw (2010). <doi:10.1016/j.quageo.2010.01.002>.
Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams.
A versatile interior point solver that solves linear programs (LPs), quadratic programs (QPs), second-order cone programs (SOCPs), semidefinite programs (SDPs), and problems with exponential and power cone constraints (<https://oxfordcontrol.github.io/ClarabelDocs/stable/>). For quadratic objectives, unlike interior point solvers based on the standard homogeneous self-dual embedding (HSDE) model, 'Clarabel' handles quadratic objective without requiring any epigraphical reformulation of its objective function. It can therefore be significantly faster than other HSDE-based solvers for problems with quadratic objective functions. Infeasible problems are detected using using a homogeneous embedding technique.
Get description of images from Clarifai API. For more information,
    see <http://clarifai.com>. Clarifai uses a large deep learning cloud to come
    up with descriptive labels of the things in an image. It also provides how
    confident it is about each of the labels.
Performs simulation-based inference as an alternative to the delta method for obtaining valid confidence intervals and p-values for regression post-estimation quantities, such as average marginal effects and predictions at representative values. This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in King, Tomz, and Wittenberg (2000) <doi:10.2307/2669316>. 'clarify' is meant to replace some of the functionality of the archived package 'Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality.
Various functions for classification, including k-nearest
  neighbour, Learning Vector Quantization and Self-Organizing Maps.
Defines the classes used for "class comparison" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  comparison includes tests for differential expression; see Simon's
  book for details on typical problem types.
Defines the classes used for "class discovery" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  discovery primarily consists of unsupervised clustering methods with
  attempts to assess their statistical significance.
Construct directed graphs of S4 class hierarchies and
  visualize them.  In general, these graphs typically are DAGs (directed
  acyclic graphs), often simple trees in practice.
Generates a visualization of binary classifier performance as a grid of
  diagnostic plots with just one function call. Includes ROC curves,
  prediction density, accuracy, precision, recall and calibration plots, all using
  ggplot2 for easy modification.
  Debug your binary classifiers faster and easier!
Given $p$-dimensional training data containing $d$ groups
    (the design space), a classification algorithm (classifier) predicts
    which group new data belongs to.  Generally the input to these
    algorithms is high dimensional, and the boundaries between groups will
    be high dimensional and perhaps curvilinear or multi-faceted. This
    package implements methods for understanding the division of space
    between the groups.
Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.
Tools to visualize the results of a classification of cases.
    The graphical displays include stacked plots, silhouette plots, quasi residual plots, and class maps.
    Implements the techniques described and illustrated in Raymaekers, Rousseeuw and Hubert (2021), Class maps for visualizing classification results, Technometrics, appeared online.
    <doi:10.1080/00401706.2021.1927849> (open access) and Raymaekers and Rousseeuw (2021),
    Silhouettes and quasi residual plots for neural nets and tree-based classifiers,
    <arXiv:2106.08814>. Examples can be found in the vignettes:
    "Discriminant_analysis_examples","K_nearest_neighbors_examples",
    "Support_vector_machine_examples", "Rpart_examples", "Random_forest_examples",
    and "Neural_net_examples".
The user first provides design vectors n, a and b as well as null (p0) and alternative (p1) benchmark values for the probability of success. The key function "mv.plots.SM()" calculates mean values of exact upper and lower limits based on four different rank ordering methods. These plots form the basis of selecting a rank ordering. The function "inference()" calculates exact limits from a provided realisation and ordering choice. For more information, see "Exact confidence limits after a group sequential single arm binary trial" by Lloyd, C.J. (2020), Statistics in Medicine, Volume 38, 2389-2399, <doi:10.1002/sim.8909>.
Bindings to Google's C++ library Compact Language Detector 2
    (see <https://github.com/cld2owners/cld2#readme> for more information). Probabilistically
    detects over 80 languages in plain text or HTML. For mixed-language input it returns the
    top three detected languages and their approximate proportion of the total classified 
    text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a 'cld3'
    package on CRAN which uses a neural network model instead.
Google's Compact Language Detector 3 is a neural network model for language 
    identification and the successor of 'cld2' (available from CRAN). The algorithm is still
    experimental and takes a novel approach to language detection with different properties
    and outcomes. It can be useful to combine this with the Bayesian classifier results 
    from 'cld2'. See <https://github.com/google/cld3#readme> for more information.
Contains a time series classification method that obtains a set of filters that maximize the between-class and minimize the within-class distances.
A wrapper around the new 'cleaner' package, that allows
  data cleaning functions for classes 'logical', 'factor', 'numeric', 
  'character', 'currency' and 'Date' to make data cleaning fast and
  easy. Relying on very few dependencies, it provides smart guessing,
  but with user options to override anything if needed.
Curates biological sequences massively, quickly, without errors and 
 without internet connection.  Biological sequences curing is performed by 
 aligning the forward and / or revers primers or ends of cloning vectors with the sequences 
 to be cleaned. After the alignment, new subsequences are generated without biological fragment 
 not desired by the user.
 Pozzi et al (2020) <doi:10.1007/s00438-020-01671-z>.
Wrapper of .Call() that runs exit handlers to clean up C
    resources. Helps managing C (non-R) resources while using the R API.
Functions to work with data frames to prepare data for further analysis.
    The functions for imputation, encoding, partitioning, and other manipulation can produce log files to keep track of process.
Data cleaning functions for classes logical,
  factor, numeric, character, currency and Date to make
  data cleaning fast and easy. Relying on very few dependencies, it 
  provides smart guessing, but with user options to override 
  anything if needed.
Provides a set of utility tools to inspect spatial objects, facilitate
  handling and reporting of topology errors and geometry validity issue with sp objects.
  Finally, it provides a geometry cleaner that will fix all geometry problems,
  and eliminate (at least reduce) the likelihood of having issues when doing
  spatial data processing.
Provides a set of fast tools for converting a textual corpus into
  a set of normalized tables. Users may make use of the 'udpipe' back end with
  no external dependencies, or two Python back ends with 'spaCy'
  <https://spacy.io> or 'CoreNLP' <https://stanfordnlp.github.io/CoreNLP/>.
  Exposed annotation tasks include tokenization, part of speech tagging, named
  entity recognition, and dependency parsing.
Check your R code for some of the most common
    layout flaws.  Many tried to teach us how to write code less dreadful,
    be it implicitly as B. W. Kernighan and D. M. Ritchie (1988)
    <ISBN:0-13-110362-8> in 'The C Programming Language' did, be it
    explicitly as R.C. Martin (2008) <ISBN:0-13-235088-2> in 'Clean Code:
    A Handbook of Agile Software Craftsmanship' did.  So we should check
    our code for files too long or wide, functions with too many lines,
    too wide lines, too many arguments or too many levels of nesting.
    Note: This is not a static code analyzer like pylint or the like.
    Checkout <https://cran.r-project.org/package=lintr> instead.
A collection of clean 'R Markdown' HTML document templates
    using classy-looking classless CSS styles. These documents use a
    minimal set of dependencies but still look great, making them suitable
    for use a package vignettes or for sharing results via email.
A reliable and efficient tool for cleaning univariate time 
    series data. It implements reliable and efficient procedures for 
    automating the process of cleaning univariate time series data. 
    The package provides integration with already developed and deployed 
    tools for missing value imputation and outlier detection. It also 
    provides a way of visualizing large time-series data in different 
    resolutions.
Small package to clean the R console and the R environment
    with the call of just one function.
Composite likelihood approach is implemented to estimating statistical models for spatial ordinal and proportional data based on Feng et al. (2014) <doi:10.1002/env.2306>. Parameter estimates are identified by maximizing composite log-likelihood functions using the limited memory BFGS optimization algorithm with bounding constraints, while standard errors are obtained by estimating the Godambe information matrix.
Tools for evaluating link prediction and clustering algorithms 
    with respect to ground truth. Includes efficient implementations of 
    common performance measures such as pairwise precision/recall, 
    cluster homogeneity/completeness, variation of information, 
    Rand index etc.
Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) <DOI:10.1016/j.cageo.2005.12.009>. This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, <DOI:10.1201/b12728>).
A suite of tools to build attractive command line interfaces
    ('CLIs'), from semantic elements: headings, lists, alerts, paragraphs,
    etc. Supports custom themes via a 'CSS'-like language. It also
    contains a number of lower level 'CLI' elements: rules, boxes, trees,
    and 'Unicode' symbols with 'ASCII' alternatives. It support ANSI
    colors and text styles as well.
Create rich command line applications, with colors, headings,
    lists, alerts, progress bars, etc. It uses CSS for custom themes.
    This package is now superseded by the 'cli' package. Please use 'cli'
    instead in new projects.
Designed for web usage data analysis, it implements tools to process web sequences and identify web browsing profiles through sequential classification. Sequences' clusters are identified by using a model-based approach, specifically mixture of discrete time first-order Markov models for categorical web sequences. A Bayesian approach is used to estimate model parameters and identify sequences classification as proposed by Fruehwirth-Schnatter and Pamminger (2010) <doi:10.1214/10-BA606>.
Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures.
Provides an expectation maximization (EM) algorithm to fit a mixture of continuous time Markov models for use with clickstream or other sequence type data. Gallaugher, M.P.B and McNicholas, P.D. (2018) <arXiv:1802.04849>.
'ClickHouse' (<https://clickhouse.com/>)
   is an open-source, high performance columnar
   OLAP (online analytical processing of queries) database management system
   for real-time analytics using SQL. This 'DBI' backend
   relies on the 'ClickHouse' HTTP interface and support HTTPS protocol.
Tools for assessing data quality, performing exploratory analysis, and 
    semi-automatic preprocessing of messy data with change tracking for integral dataset cleaning.
A set of tools to read, analyze and write lists of click sequences
    on websites (i.e., clickstream). A click can be represented by a number,
    character or string. Clickstreams can be modeled as zero- (only computes
    occurrence probabilities), first- or higher-order Markov chains.
This data package contains monthly climate data in Germany, it can be used for heating and 
    cooling calculations (external temperature, heating / cooling days, solar radiation).
Includes climate data from Japan Meteorological Agency ('JMA') <https://www.jma.go.jp/jma/indexe.html>.
    Can download climate data from 'JMA'.
Execute command line programs and format results for interactive use. It is based on the package 'processx' so it does not use shell to start up the process like system() and system2(). It also provides a simpler and cleaner interface than processx::run().
A suite of routines for Clifford algebras, using the
   'Map' class of the Standard Template Library.  Canonical
   reference: Hestenes (1987, ISBN 90-277-1673-0, "Clifford algebra
   to geometric calculus").  Special cases including Lorentz transforms,
   quaternion multiplication, and Grassman algebra, are discussed.
   Conformal geometric algebra theory is implemented.  Uses 'disordR'
   discipline.
CliFlo is a web portal to the New Zealand National Climate
    Database and provides public access (via subscription) to around 6,500
    various climate stations (see <https://cliflo.niwa.co.nz/> for more
    information). Collating and manipulating data from CliFlo
    (hence clifro) and importing into R for further analysis, exploration and
    visualisation is now straightforward and coherent. The user is required to
    have an internet connection, and a current CliFlo subscription (free) if
    data from stations, other than the public Reefton electronic weather
    station, is sought.
Implementation of Hurst exponent estimators based on complex-valued lifting wavelet energy from Knight, M. I and Nunes, M. A. (2018) <doi:10.1007/s11222-018-9820-8>.
A profile likelihood based method of estimation and inference on the correlation coefficient of bivariate data with different types of censoring and missingness.
Tools to download the climatic data of the Spanish
    Meteorological Agency (AEMET) directly from R using their API and
    create scientific graphs (climate charts, trend analysis of climate
    time series, temperature and precipitation anomalies maps, warming
    stripes graphics, climatograms, etc.).
Climate stability measures are not formalized in the literature and
  tools for generating stability metrics from existing data are nascent.
  This package provides tools for calculating climate stability from raster data
  encapsulating climate change as a series of time slices. The methods follow
  Owens and Guralnick <doi:10.17161/bi.v14i0.9786> Biodiversity Informatics.
Functions for the quality control, homogenization and missing data filling of climatological series and to obtain climatological summaries and grids from the results. Also functions to display wind-roses, meteograms, Walter&Lieth diagrams, and more.
Supports analysis of trends in climate change,
        ecological and crop modelling.
Classification of climate according to Koeppen - Geiger, of aridity
    indices, of continentality indices, of water balance after Thornthwaite, of
    viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy,
    Bagnouls-Gaussen.
The developed function is a comprehensive tool for the analysis of India Meteorological Department (IMD) NetCDF rainfall data. Specifically designed to process high-resolution daily
             gridded rainfall datasets. It provides four key functions to process IMD NetCDF rainfall data and create rasters for various temporal scales, including annual, seasonal, monthly, and weekly
             rainfall. For method details see, Malik, A. (2019).<DOI:10.1007/s12517-019-4454-5>. It supports different aggregation methods, such as sum, min, max, mean, and standard deviation. These functions
             are designed for spatio-temporal analysis of rainfall patterns, trend analysis,geostatistical modeling of rainfall variability, identifying rainfall anomalies and extreme events and can be an input
             for hydrological and agricultural models.
A robust constrained L1 minimization method for estimating a large sparse inverse covariance matrix (aka precision matrix), and recovering its support for building graphical models.  The computation uses linear programming.  The method was published in TT Cai, W Liu, X Luo (2011) <doi:10.1198/jasa.2011.tm10155>.
Grants access to three widely recognised modelled data sets, namely
    Global Climate Data (WorldClim 2), Climatologies at high resolution for the
    earth's land surface areas (CHELSA), and National Aeronautics and Space
    Administration's (NASA) Shuttle Radar Topography Mission (SRTM). It handles
    both multi and single geospatial polygon and point data, extracts outputs
    that can serve as covariates in various ecological studies. Provides two
    common graphic options – the Walter-Lieth (1960)
    <https://donum.uliege.be/bitstream/2268.1/7079/1/Walter-Lieth_Klimadiagramm-Weltatlas.pdf> 
    climate diagram and the Holdridge (1967)
    <https://reddcr.go.cr/sites/default/files/centro-de-documentacion/holdridge_1966_-_life_zone_ecology.pdf>
    life zone classification scheme. Provides one new graphic scheme of our own
    design which incorporates aspects of both Walter-Leigh and Holdridge.
    Provides user-friendly access and extraction of globally recognisable data
    sets to enhance their usability across a broad spectrum of applications.
A framework that facilitates spatio-temporal analysis of climate dynamics through exploring and measuring different dimensions of climate change in space and time.
Functions for fitting GEV and POT (via point process fitting)
    models for extremes in climate data, providing return values, return
    probabilities, and return periods for stationary and nonstationary models.
    Also provides differences in return values and differences in log return
    probabilities for contrasts of covariate values. Functions for estimating risk
    ratios for event attribution analyses, including uncertainty. Under the hood,
    many of the functions use functions from 'extRemes', including for fitting the
    statistical models. Details are given in Paciorek, Stone, and Wehner (2018)
    <doi:10.1016/j.wace.2018.01.002>.
Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5).
API client for 'ClimMob', an open source software for decentralized 
    large-N trials with the 'tricot' approach <https://climmob.net/>. 
    Developed by van Etten et al. (2019) <doi:10.1017/S0014479716000739>, it turns the 
    research paradigm on its head; instead of a few researchers designing complicated 
    trials to compare several technologies in search of the best solutions for the 
    target environment, it enables many participants to carry out reasonably simple 
    experiments that taken together can offer even more information. 'ClimMobTools' 
    enables project managers to deep explore and analyse their 'ClimMob' data in R.
Set of tools to compute metrics and indices for climate analysis.
    The package provides functions to compute extreme indices, evaluate the
    agreement between models and combine theses models into an ensemble. Multi-model
    time series of climate indices can be computed either after averaging the 2-D
    fields from different models provided they share a common grid or by combining
    time series computed on the model native grid. Indices can be assigned weights
    and/or combined to construct new indices.
Contains functions to detect and visualise periods of climate
    sensitivity (climate windows) for a given biological response. 
    Please see van de Pol et al. (2016) <doi:10.1111/2041-210X.12590> 
    and Bailey and van de Pol (2016) <doi:10.1371/journal.pone.0167980> for details.
Creation of interactive tables, listings and figures ('TLFs') 
  and associated report for exploratory analysis of data in a clinical trial, 
  e.g. for clinical oversight activities.
  Interactive figures include sunburst, treemap, scatterplot, line plot and
  barplot of counts data. 
  Interactive tables include table of summary statistics
  (as counts of adverse events, enrollment table) and listings.
  Possibility to compare data (summary table or listing) across two data batches/sets.
  A clinical data review report is created via study-specific configuration 
  files and template 'R Markdown' reports contained in the package.
Bayesian and ML Emax model fitting, graphics and simulation for clinical dose
    response.  The summary data from the dose response meta-analyses in  
    Thomas, Sweeney, and Somayaji (2014) <doi:10.1080/19466315.2014.924876> and
    Thomas and Roy (2016) <doi:10.1080/19466315.2016.1256229> 
    Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) <doi:10.1177/0962280216684528> 
    are included 
    in the package.  The prior distributions for the Bayesian analyses default to
    the posterior predictive distributions derived from these references.
Utilities to make your clinical collaborations easier if not
          fun. It contains functions for designing studies such as Simon
          2-stage and group sequential designs and for data analysis such
          as Jonckheere-Terpstra test and estimating survival quantiles.
A clinical significance analysis can be used to determine if an 
    intervention has a meaningful or practical effect for patients. You provide 
    a tidy data set plus a few more metrics and this package will take care of 
    it to make your results publication ready.
Provides estimates of several summary measures for clinical trials including
    the average hazard ratio, the weighted average hazard ratio, the restricted 
    superiority probability ratio, the restricted mean survival difference and 
    the ratio of restricted mean times lost, based on the short-term and 
    long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) 
    which accommodates various non-proportional hazards scenarios. The inference
    procedures and the asymptotic results for the summary measures are discussed 
    in Yang (2018, <doi:10.1002/sim.7676>).
Recalibrate risk scores (predicting binary outcomes) to improve clinical utility of risk score using weighted logistic or constrained logistic recalibration methods. Additionally, produces plots to assess the potential for recalibration to improve the clinical utility of a risk model. Methods are described in detail in Mishra, A. (2019) "Methods for Risk Markers that Incorporate Clinical Utility" <http://hdl.handle.net/1773/44068>.
Every research team have their own script for calculation of hemodynamic indexes. 
   This package makes it possible  to insert a long-format dataframe, and add both 
   periods of interest (trigger-periods), and delete artifacts with deleter-files.
Provides equations commonly used in clinical
        pharmacokinetics and clinical pharmacology, such as equations
        for dose individualization, compartmental pharmacokinetics,
        drug exposure, anthropomorphic calculations, clinical
        chemistry, and conversion of common clinical parameters. Where
        possible and relevant, it provides multiple published and
        peer-reviewed equations within the respective R function.
Functions for calculating clinical significance.
Provides measures of effect sizes from summarized continuous variables as well as diagnostic accuracy statistics for 2x2 table data. Includes functions for Cohen's d, Cohen's q, partial eta-squared, coefficient of variation, odds ratio, likelihood ratios, sensitivity, specificity, positive and negative predictive values, and Youden index.
Performs biomedical named entity recognition,
    Unified Medical Language System (UMLS) concept mapping, and negation
    detection using the Python 'spaCy', 'scispaCy', and 'medspaCy' packages, and 
    transforms extracted data into a wide format for inclusion in machine
    learning models. The development of the 'scispaCy' package is described by
    Neumann (2019) <doi:10.18653/v1/W19-5034>. The 'medspacy' package uses
    'ConText', an algorithm for determining the context of clinical statements
    described by Harkema (2009) <doi:10.1016/j.jbi.2009.05.002>. Clinspacy
    also supports entity embeddings from 'scispaCy' and UMLS 'cui2vec' concept
    embeddings developed by Beam (2018) <arXiv:1804.01486>.
Every research team have their own script for data management, statistics and 
   most importantly hemodynamic indices. The purpose is to standardize scripts 
   utilized in clinical research. The hemodynamic indices can be used in a long-format dataframe, 
   and add both periods of interest (trigger-periods), and delete artifacts with deleter-files. 
   Transfer function analysis (Claassen et al. (2016) <doi:10.1177/0271678X15626425>) and
   Mx (Czosnyka et al. (1996) <doi:10.1161/01.str.27.10.1829>) can be calculated using this package.
Utility functions to facilitate the import, 
 the reporting and analysis of clinical data.
 Example datasets in 'SDTM' and 'ADaM' format, containing a subset of patients/domains
 from the 'CDISC Pilot 01 study' are also available as R datasets to demonstrate
 the package functionalities.
Collection of indices and tools relating to cardiovascular, nephrology, and hepatic research that aid epidemiological chort or retrospective chart review with big data. All indices and tools take commonly used lab values and patient demographics and measurements to compute various risk and predictive values for survival. References to original literature and validation contained in each function documentation.
A fast and general implementation of the Elston-Stewart algorithm
 that can calculate the likelihoods of large and complex pedigrees. 
 References for the Elston-Stewart algorithm are
 Elston & Stewart (1971) <doi:10.1159/000152448>,
 Lange & Elston (1975) <doi:10.1159/000152714> and
 Cannings et al. (1978) <doi:10.2307/1426718>.
Simple utility functions to read from and write to
    the Windows, OS X, and X11 clipboards.
Clique percolation community detection for weighted and
    unweighted networks as well as threshold and plotting functions.
    For more information see Farkas et al. (2007) <doi:10.1088/1367-2630/9/6/180>
    and Palla et al. (2005) <doi:10.1038/nature03607>.
A small subset of Unicode symbols, that are useful
    when building command line applications. They fall back to
    alternatives on terminals that do not support Unicode.
    Many symbols were taken from the 'figures' 'npm' package
    (see <https://github.com/sindresorhus/figures>).
Estimation and inference for linear models where some or all of the
    fixed-effects coefficients are subject to order restrictions. This package uses
    the robust residual bootstrap methodology for inference, and can handle some
    structure in the residual variance matrix.
Implementation of the chain ladder model under the reverse time framework
    introduced in Hiabu (2017) <doi:10.1080/03461238.2016.1240709>.
    It also implements extensions that add flexibility to the individual development factors modeling
    by allowing practitioners to set their own hazard rate model.
Provides a comprehensive library for date-time manipulations
    using a new family of orthogonal date-time classes (durations, time
    points, zoned-times, and calendars) that partition responsibilities so
    that the complexities of time zones are only considered when they are
    really needed. Capabilities include: date-time parsing, formatting,
    arithmetic, extraction and updating of components, and rounding.
A wrapper for the Clockify API <https://docs.clockify.me/>, making it possible to query, insert and update time keeping data.
A set of functions to fit a boosting conditional logit model.
Tools for the fitting and cross validation of exact conditional logistic regression models with lasso and elastic net penalties. Uses cyclic coordinate descent and warm starts to compute the entire path efficiently.
Fit a sequence of conditional logistic regression models with lasso, for small to large sized samples. Avalos, M., Pouyes, H., Grandvalet, Y., Orriols, L., & Lagarde, E. (2015) <doi:10.1186/1471-2105-16-S6-S1>.
Quickly estimate the net growth rate of a
    population or clone whose growth can be approximated by a birth-death
    branching process.  Input should be phylogenetic tree(s) of clone(s) with
    edge lengths corresponding to either time or mutations. Based on
    coalescent results in Johnson et al. (2023) <doi:10.1093/bioinformatics/btad561>. Simulation
    techniques as well as growth rate methods build on prior work from
    Lambert A. (2018) <doi:10.1016/j.tpb.2018.04.005> and Stadler T. (2009)
    <doi:10.1016/j.jtbi.2009.07.018>.
Defines the classes and functions used to simulate and
  to analyze data sets describing copy number variants and,
  optionally, sequencing mutations in order to detect clonal subsets.
  See Zucker et al. (2019) <doi:10.1093/bioinformatics/btz057>.
Analyze data from next-generation sequencing experiments on genomic samples. 'CLONETv2' offers a set of functions to compute allele specific copy number and clonality from segmented data and SNPs position pileup. The package has also calculated the clonality of single nucleotide variants given read counts at mutated positions. The package has been developed at the laboratory of Computational and Functional Oncology, Department of CIBIO, University of Trento (Italy), under the supervision of prof Francesca Demichelis. References: Prandi et al. (2014) <doi:10.1186/s13059-014-0439-6>; Carreira et al. (2014) <doi:10.1126/scitranslmed.3009448>; Romanel et al. (2015) <doi:10.1126/scitranslmed.aac9511>.
Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error.  Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) <doi: 10.1080/01621459.2017.1292915>, can act as a useful tool for model diagnostics.
A unified interface for simplifying cloud storage interactions, 
    including uploading, downloading, reading, and writing files, with functions
    for both 'Google Drive' (<https://www.google.com/drive/>) and 'Amazon S3' 
    (<https://aws.amazon.com/s3/>).
Interface to the Google Cloud Machine Learning Platform
  <https://cloud.google.com/ml-engine>, which provides cloud tools for training machine
  learning models.
The 'CloudOS' client library for R makes it easy to interact with 
    CloudOS in the R environment for analysis.
Access Cloudstor via their WebDAV API. This package can read, write, and navigate Cloudstor from R.
Provides means of plots for comparing utilization data of compute systems.
Computes the uniform rate of profit, the vector of price of 
    production and the vector of labor values; and also compute measures of deviation 
    between relative prices of production and relative values.  
    <https://scholarworks.umass.edu/econ_workingpaper/347/>. You provide the  
    input-output data and 'clptheory' does the calculations for you.
A new methodology for linear regression with both curve response 
    and curve regressors, which is described in Cho, Goude, Brossat and Yao 
    (2013) <doi:10.1080/01621459.2012.722900> and (2015) 
    <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is 
    dimension reduction based on a singular value decomposition in a Hilbert 
    space, which reduces the curve regression problem to several scalar linear 
    regression problems.
Calculations of "EP15-A3 document. A manual for user verification of precision and estimation of bias" CLSI (2014, ISBN:1-56238-966-1).
Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.
Implements a classification method described by Grice (2011, ISBN:978-0-12-385194-9) using
    binary procrustes rotation; a simplified version of procrustes rotation.
Provides several cluster-robust variance estimators (i.e.,
    sandwich estimators) for ordinary and weighted least squares linear regression
    models, including the bias-reduced linearization estimator introduced by Bell
    and McCaffrey (2002) 
    <https://www150.statcan.gc.ca/n1/pub/12-001-x/2002002/article/9058-eng.pdf> and 
    developed further by Pustejovsky and Tipton (2017) 
    <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating
    the variance- covariance matrix and for testing single- and multiple-
    contrast hypotheses based on Wald test statistics. Tests of single regression
    coefficients use Satterthwaite or saddle-point corrections. Tests of multiple-
    contrast hypotheses use an approximation to Hotelling's T-squared distribution.
    Methods are provided for a variety of fitted models, including lm() and mlm
    objects, glm(), geeglm() (from package 'geepack'), ivreg() (from package 'AER'), ivreg() (from package 'ivreg' when 
    estimated by ordinary least squares), plm() (from package 'plm'), gls() and 
    lme() (from 'nlme'), lmer() (from `lme4`), robu() (from 'robumeta'), and rma.uni() 
    and rma.mv() (from 'metafor').
CLUster Evaluation (CLUE) is a computational method for identifying 
              optimal number of clusters in a given time-course dataset clustered by 
              cmeans or kmeans algorithms and subsequently identify key kinases or 
              pathways from each cluster. Its implementation in R is called ClueR. 
              See README on <https://github.com/PYangLab/ClueR> for more details.
              P Yang et al. (2015) <doi:10.1371/journal.pcbi.1004403>.
An implementation of the clugen algorithm for generating
    multidimensional clusters with arbitrary distributions. Each cluster is
    supported by a line segment, the position, orientation and length of which
    guide where the respective points are placed. This package is described in
    Fachada & de Andrade (2023) <doi:10.1016/j.knosys.2023.110836>.
Two-step feature-based clustering method designed for micro panel (longitudinal) data with the artificial panel data generator. See Sobisek, Stachova, Fojtik (2018) <arXiv:1807.05926>.
Providing a cluster allocation for n samples, either with an $n \times p$ data matrix or an $n \times n$ distance
                matrix, a bootstrap procedure is performed. The proportion of bootstrap replicates where a pair of samples
                cluster in the same cluster indicates who tightly the samples in a particular cluster clusters together.
Clustered covariate regression enables estimation and inference in 
 both linear and non-linear models with linear predictor functions even when the
 design matrix is column rank deficient. Routines in this package implement algorithms
 in Soale and Tsyawo (2019) <doi:10.13140/RG.2.2.32355.81441>.
Cluster Evolution Analytics allows us to use exploratory what if questions in the sense that the present information of an object is plugged-in a dataset in a previous time frame so that we can explore its evolution (and of its neighbors) to the present. See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2024) <https://mpra.ub.uni-muenchen.de/120220>.
Parameter estimation of regression models with fixed group effects, when the group variable is missing while group-related variables are available. Parametric and semi-parametric approaches described in Marbac et al. (2020) <arXiv:2012.14159> are implemented.
Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test)
       for clustered data documented in
       Jiang et. al (2020) <doi:10.18637/jss.v096.i06>.
Statistical methods for ROC surface analysis in three-class classification problems for clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for:
  (i) true class fractions (TCFs) at fixed pairs of thresholds;
  (ii) the ROC surface;
  (iii) the volume under ROC surface (VUS);
  (iv) the optimal pairs of thresholds.
  Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022) <doi:10.1177/09622802221089029>. Referring to point (iv), three  different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018) <doi:10.1177/0962280217742539>. Visualization tools are also provided. We refer readers to the articles cited above for all details.
Implements a new method 'ClussCluster' descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished).
	Simultaneously perform clustering analysis and signature 	gene selection on high-dimensional transcriptome data sets. 	To do so, 'ClussCluster' incorporates a Lasso-type 	regularization penalty term to the objective function of K-	means so that cell-type-specific signature genes can be 	identified while clustering the cells.
Tests, utilities, and case studies for analyzing significance in
  clustered binary matched-pair data. The central function clust.bin.pair uses
  one of several tests to calculate a Chi-square statistic. Implemented are the
  tests Eliasziw (1991) <doi:10.1002/sim.4780101211>, Obuchowski (1998)
  <doi:10.1002/(SICI)1097-0258(19980715)17:13%3C1495::AID-SIM863%3E3.0.CO;2-I>,
  Durkalski (2003) <doi:10.1002/sim.1438>, and Yang (2010)
  <doi:10.1002/bimj.201000035> with McNemar (1947) <doi:10.1007/BF02295996>
  included for comparison. The utility functions nested.to.contingency and
  paired.to.contingency convert data between various useful formats. Thyroids
  and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989)
  <doi:10.1016/0165-1781(89)90196-0> respectively.
Evaluates the stability and significance of clusters on 'igraph' graphs.
    Supports weighted and unweighted graphs. Implements the cluster evaluation methods
    defined by Arratia A, Renedo M (2021) <doi:10.7717/peerj-cs.600>. Also includes an
    implementation of the Reduced Mutual Information introduced by Newman et al. (2020) 
    <doi:10.1103/PhysRevE.101.042304>.
A set of tools for evaluating clustering robustness using 
    proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) 
    <doi:10.1038/srep06207>), as well as similarity across methods 
    and method stability using element-centric clustering comparison (Gates et 
    al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package 
    enables stability-based parameter assessment for graph-based clustering 
    pipelines typical in single-cell data analysis.
Hierarchical and partitioning algorithms of blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>)  and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data. Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available.
Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2019) <doi:10.1007/s00180-018-0817-8>.
Methods for Cluster analysis.  Much extended the original from
	Peter Rousseeuw, Anja Struyf and Mia Hubert,
	based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
A collection of data sets for teaching cluster analysis.
Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' <http://openbudgets.eu/> data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface.
Test for cluster tendency (clusterability) of a data set.
    The methods implemented - reducing the data set to a single dimension using principal component analysis or computing
    pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - 
    are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026>. Such methods can inform whether clustering algorithms
    are appropriate for a data set.
Provides functionality for the analysis of clustered data using the cluster bootstrap.
Functions for calculation of robustness measures for clusters and cluster membership based on generating consensus matrices from bootstrapped clustering experiments in which a random proportion of rows of the data set are used in each individual clustering. This allows the user to prioritise clusters and the members of clusters based on their consistency in this regime. The functions allow the user to select several algorithms to use in the re-sampling scheme and with any of the parameters that the algorithm would normally take. See Simpson, T. I., Armstrong, J. D. & Jarman, A. P. (2010) <doi:10.1186/1471-2105-11-590> and Monti, S., Tamayo, P., Mesirov, J. & Golub, T. (2003) <doi:10.1023/a:1023949509487>.
Package providing functions for computing a collection of clustering validation or quality criteria and partition comparison indices.
Estimating causal effects from observational studies assuming 
    clustered (or partial) interference. These inverse probability-weighted
    estimators target new estimands arising from population-level treatment 
    policies. The estimands and estimators are introduced in Barkley et al. 
    (2017) <arXiv:1711.04834>.
Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information.
We developed the clusterGeneration package to provide functions 
        for generating random clusters, generating random 
        covariance/correlation matrices,
        calculating a separation index (data and population version)
        for pairs of clusters or cluster distributions, and 1-D and 2-D
        projection plots to visualize clusters.  The package also
        contains a function to generate random clusters based on
        factorial designs with factors such as degree of separation,
        number of clusters, number of variables, number of noisy
        variables.
One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci).
  clusterhap groups together all individuals of a population with the same haplotype.
  Each group contains individual with the same allele in each SNP,
  whether or not missing data. Thus, clusterhap groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, clusterhap calculates such
  probability from relative frequencies.
Tools for clustering high-dimensional data.
  In particular, it contains the methods described in
   <doi:10.1093/bioinformatics/btaa243>,
   <arXiv:2010.00950>.
The design of this package allows us to run different clustering packages and compare the results between them, to determine which algorithm behaves best from the data provided. See Martos, L.A.P., García-Vico, Á.M., González, P. et al.(2023) <doi:10.1007/s13748-022-00294-2> "Clustering: an R library to facilitate the analysis and comparison of cluster algorithms.", Martos, L.A.P., García-Vico, Á.M., González, P. et al. "A Multiclustering Evolutionary Hyperrectangle-Based Algorithm" <doi:10.1007/s44196-023-00341-3> and L.A.P., García-Vico, Á.M., González, P. et al. "An Evolutionary Fuzzy System for Multiclustering in Data Streaming" <doi:10.1016/j.procs.2023.12.058>.
A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of 'Ckmeans.1d.dp' to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp() can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp() and backtracking.sc.dp() can be used. See Szkaliczki, T. (2016) "clustering.sc.dp: Optimal Clustering with Sequential Constraint by Using Dynamic Programming" <doi: 10.32614/RJ-2016-022> for more information.
Allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps. 
    I) Missing data imputation using dedicated models. Four multiple imputation methods are proposed, two are based on joint modelling and two are fully sequential methods.
    II) cluster analysis of imputed data sets. Six clustering methods are available (distances-based or model-based), but custom methods can also be easily used.
    III) Partition pooling, The set of partitions is aggregated using Non-negative Matrix Factorization based method. An associated instability measure is computed by bootstrap. Among applications, this instability measure can be used to choose a number of clusters with missing values.
    The package also proposes several diagnostic tools to tune the number of imputed data sets, to tune the number of iterations in fully sequential imputation, to check the fit of imputation models, etc.
Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.
Evaluate arbitrary function calls using workers on HPC schedulers
    in single line of code. All processing is done on the network without
    accessing the file system. Remote schedulers are supported via SSH.
Integrative context-dependent clustering for heterogeneous
    biomedical datasets. Identifies local clustering structures in related
    datasets, and a global clusters that exist across the datasets.
Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.
Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size.
This is a function for validating microarray clusters via reproducibility,
 based on the paper referenced below.
Calculate p-values and confidence intervals using cluster-adjusted
    t-statistics (based on Ibragimov and Muller (2010) <DOI:10.1198/jbes.2009.08046>, pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) <DOI:10.1162/rest.90.3.414>. Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models.
Distance measures (GDM1, GDM2,	Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap,	Davies-Bouldin),	data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method,	replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). 
 (MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>, 
 HUBERT, L., ARABIE, P. (1985) <doi:10.1007%2FBF01908075>, 
 RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, 
 JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>, 
 MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>, 
 JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>, 
 DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>, 
 CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>,
 HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>, 
 TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>, 
 BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, 
 WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).
Allows one to assess the stability of individual objects, clusters 
    and whole clustering solutions based on repeated runs of the K-means and K-medoids 
    partitioning algorithms.
Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins statistic is implemented. See Hopkins and Skellam (1954) <doi:10.1093/oxfordjournals.aob.a083391>.
Implements a Ward-like hierarchical clustering
    algorithm including soft spatial/geographical constraints.
This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details.
Clustering methods, which (if asked) can provide step-by-step
    explanations of the algorithms used, as described in Ezugwu et. al., (2022)
    <doi:10.1016/j.engappai.2022.104743>; and datasets to test them on, which
    highlight the strengths and weaknesses of each technique, as presented in
    the clustering section of 'scikit-learn' (Pedregosa et al., 2011)
    <https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html>.
Model-based clustering of mixed data (i.e. data which consist of
    continuous, binary, ordinal or nominal variables) using a parsimonious
    mixture of latent Gaussian variable models.
Functions to perform k-prototypes partitioning clustering for
    mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means
    Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining
    and Knowledge Discovery 2, 283-304.
Network-based clustering using a Bayesian network mixture model with optional covariate adjustment.
Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.
Provides various tools of for clustering multivariate angular 
  data on the torus. The package provides angular 
  adaptations of usual clustering methods such as the k-means 
  clustering, pairwise angular distances, which can be used as an 
  input for distance-based clustering algorithms, and implements
  clustering based on the conformal prediction framework. Options 
  for the conformal scores include scores based on a kernel density 
  estimate, multivariate von Mises mixtures, and naive k-means clusters. 
  Moreover, the package provides some basic data handling tools for 
  angular data.
Clusters longitudinal trajectories over time (can be unequally 
    spaced, unequal length time series and/or partially overlapping series) on
    a common time axis. Performs k-means clustering on a single continuous 
    variable measured over time, where each mean is defined by a thin plate 
    spline fit to all points in a cluster. Distance is MSE across trajectory 
    points to cluster spline. Provides graphs of derived cluster splines, 
    silhouette plots, and Adjusted Rand Index evaluations of the number
    of clusters. Scales well to large data with multicore parallelism available
    to speed computation.
Monitor and trace changes in clustering solutions of accumulating datasets 
    at successive time points. The clusters can adopt External and Internal transition at 
    succeeding time points. The External transitions comprise of Survived, Merged, Split, 
    Disappeared, and newly Emerged candidates. In contrast, Internal transition includes changes 
    in location and cohesion of the survived clusters. The package uses MONIC framework developed by 
    Spiliopoulou, Ntoutsi, Theodoridis, and Schult (2006)<doi:10.1145/1150402.1150491> .
A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; <DOI:10.18637/jss.v091.i10>). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <DOI:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <DOI:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <DOI:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <DOI:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; <DOI:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; <DOI:10.1002/wics.1456>), which combine PCA for mixed-type data with K-means.
Deciding what resolution to use can be a difficult question when
    approaching a clustering analysis. One way to approach this problem is to
    look at how samples move as the number of clusters increases. This package
    allows you to produce clustering trees, a visualisation for interrogating
    clusterings as resolution increases.
Returns an edit-distance based clusterization of an input vector of strings.
    Each cluster will contain a set of strings w/ small mutual edit-distance
    (e.g., Levenshtein, optimum-sequence-alignment, Damerau-Levenshtein), as computed by
    stringdist::stringdist(). The set of all mutual edit-distances is then used by
    graph algorithms (from package 'igraph') to single out subsets of high connectivity.
An interactive document on  the topic of cluster analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/ClusterAnalysis/>.
Functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also be defined.
Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available.
Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering done on any type of data. Visualization is based on the probabilities of classification.