It provides ensemble capabilities to supervised and unsupervised learning models predictions without using training labels. It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions.
Non linear dot plots are diagrams that allow dots of varying size to be constructed, so that columns with a large number of samples are reduced in height. Implementation of algorithm described in: Nils Rodrigues and Daniel Weiskopf, "Nonlinear Dot Plots", IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 616-625, 2018. <doi:10.1109/TVCG.2017.2744018>.
Performs 'nonlinear Invariant Causal Prediction' to estimate the 
    causal parents of a given target variable from data collected in
    different experimental or environmental conditions, extending
    'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016), 
    <arXiv:1501.01332>, to nonlinear settings. For more details, see C. Heinze-Deml, 
    J. Peters and N. Meinshausen: 'Invariant Causal Prediction for Nonlinear Models', 
    <arXiv:1706.08576>.
Function and data sets in the book entitled "Nonlinear Time Series Analysis with R Applications" B.Guris (2020). The book will be published in Turkish and the original name of this book will be "R Uygulamali Dogrusal Olmayan Zaman Serileri Analizi". It is possible to perform nonlinearity tests, nonlinear unit root tests, nonlinear cointegration tests and estimate nonlinear error correction models by using the functions written in this package. The Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) type unit root tests can be performed using the functions written. In addition, cointegration tests using the Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) models can be applied. It is possible to estimate nonlinear error correction models. The Granger causality test performed using nonlinear models can also be applied.
Functions for nonlinear time series analysis. This package permits
    the computation of the  most-used nonlinear statistics/algorithms
    including generalized correlation dimension, information dimension,
    largest Lyapunov exponent, sample entropy and Recurrence
    Quantification Analysis (RQA), among others. Basic routines
    for surrogate data testing are also included. Part of this work
    was based on the  book "Nonlinear time series analysis" by
    Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
Loading NONMEM (NONlinear Mixed-Effect Modeling, <https://www.iconplc.com/solutions/technologies/nonmem/>) and PSN (Perl-speaks-NONMEM, <https://uupharmacometrics.github.io/PsN/>) output files to extract parameter estimates, provide visual predictive check (VPC) and goodness of fit (GOF) plots, and simulate with parameter uncertainty.
'NONMEM' has been a tool for running nonlinear mixed
    effects models since the 80s and is still used today (Bauer 2019
    <doi:10.1002/psp4.12404>). This tool allows you to convert
    'NONMEM' models to 'rxode2' (Wang, Hallow and James (2016)
    <doi:10.1002/psp4.12052>) and with simple models 'nlmixr2' syntax
    (Fidler et al (2019) <doi:10.1002/psp4.12445>). The 'nlmixr2'
    syntax requires the residual specification to be included and it
    is not always translated. If available, the 'rxode2' model will
    read in the 'NONMEM' data and compare the simulation for the
    population model ('PRED') individual model ('IPRED') and residual
    model ('IWRES') to immediately show how well the translation is
    performing. This saves the model development time for people who are
    creating an 'rxode2' model manually.  Additionally, this package reads
    in all the information to allow simulation with uncertainty (that is the
    number of observations, the number of subjects, and the covariance
    matrix) with a 'rxode2' model.  This is complementary to the
    'babelmixr2' package that translates 'nlmixr2' models to 'NONMEM' and can
    convert the objects converted from 'nonmem2rx' to a full 'nlmixr2' fit.
Systematically creates and modifies NONMEM(R) control streams. Harvests
 NONMEM output, builds run logs, creates derivative data, generates diagnostics.
 NONMEM (ICON Development Solutions <https://www.iconplc.com/>) is software for 
 nonlinear mixed effects modeling. See 'package?nonmemica'.
Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0),
  using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in
  (Li, Can, 2013, <https://www.hindawi.com/journals/jam/2013/986317/abs/>).
Testing non-nested models via theory supplied by Vuong (1989) <DOI:10.2307/1912557>.
    Includes tests of model distinguishability and of model fit that can be applied
    to both nested and non-nested models. Also includes functionality to obtain
    confidence intervals associated with AIC and BIC. This material is partially based on
    work supported by the National Science Foundation under Grant Number SES-1061334.
Implements calculation of probability density function, cumulative distribution function, equicoordinate quantile function and survival function, and random numbers generation for the following multivariate distributions: Lomax (Pareto Type II), generalized Lomax, Mardia’s Pareto of Type I, Logistic, Burr, Cook-Johnson’s uniform, F and Inverted Beta. See Tapan Nayak (1987) <doi:10.2307/3214068>.
Contains the following 5 nonparametric hypothesis tests:
    The Sign Test,
    The 2 Sample Median Test,
    Miller's Jackknife Procedure,
    Cochran's Q Test, &
    The Stuart-Maxwell Test.
Efficiency and productivity indices are measured using this package. This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations.
Basic implementation of a Gibbs sampler for a Chinese Restaurant Process along with some visual aids to help understand how the sampling works. This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute.
Plot, process, and analyze NPO files produced by
    'Nonpareil' <http://enve-omics.ce.gatech.edu/nonpareil/>.
Estimates and plots (as a single plot and as a heat map) the rolling window correlation coefficients between two time series and computes their statistical significance, which is carried out through a non-parametric computing-intensive method. This method addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. The method is based on Monte Carlo simulations by permuting one of the variables (e.g., the dependent) under analysis and keeping fixed the other variable (e.g., the independent). We improve the computational efficiency of this method to reduce the computation time through parallel computing. The 'NonParRolCor' package also provides examples with synthetic and real-life environmental time series to exemplify its use. Methods derived from R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/> and J.M. Polanco-Martinez and J.L. Lopez-Martinez (2021) <doi:10.1016/j.ecoinf.2021.101379>.
Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA).
    The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz–Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables.
    Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities.
    The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample.
Statistical inference with non-probability samples when auxiliary information from external sources such as probability samples or population totals or means is available. Details can be found in: Wu et al. (2020) <doi:10.1080/01621459.2019.1677241>, Kim et al. (2021) <doi:10.1111/rssa.12696>, Wu et al. (2023) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2022002/article/00002-eng.htm>, Kim et al. (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021001/article/00004-eng.htm>, Kim et al. (2020) <doi:10.1111/rssb.12354>.
Nonparametric methods for smoothing regression function data with change-points, utilizing range kernels for iterative and anisotropic smoothing methods. For further details, see the paper by John R.J. Thompson (2024) <doi:10.1080/02664763.2024.2352759>.
A non-parametric test for multi-observer concordance and
    differences between concordances in (un)balanced data.
Estimation of party/candidate ideological positions
             that correspond to a Nash equilibrium along a 
             one-dimensional space.
Onedimensional Normal (i.e. Gaussian) Mixture Models (S3) Classes,
   for, e.g., density estimation or clustering algorithms research and teaching;
   providing the widely used Marron-Wand densities.  Efficient random
   number generation and graphics. Fitting to data by efficient ML (Maximum
   Likelihood) or traditional EM estimation.
Provides the Arctic Ice Studio's Nord and Group of Seven inspired colour palettes for use with 'ggplot2' via custom functions.
The Nordklim dataset 1.0 is a unique and useful achievement for climate 
    analysis. It includes observations of twelve different climate elements from 
    more than 100 stations in the Nordic region, in time span over 100 years.
    The project contractors were NORDKLIM/NORDMET on behalf of the National 
    meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), 
    Norway (DNMI) and Sweden (SMHI).
An integrated set of functions for the analysis of 
  multivariate normal datasets with missing values, including implementation of
  the EM algorithm, data augmentation, and multiple imputation.
Builds general noise SVR models using Naive Online R Minimization Algorithm, NORMA, an optimization method based on classical stochastic gradient descent suitable for computing SVR models in an online setting.
Provides standardized access to a range of re-scaling methods for numerical vectors
    and time-series features calculated within the 'theft' ecosystem.
Package including an interactive Shiny application for
    testing normality visually.
Provides a simple method for centering and scaling of numeric data. 
    Certain columns or rows can be ignored when normalizing or be normalized jointly.
Normalize a given Hadamard matrix. A Hadamard matrix is said to be normalized when its first row and first column entries are all 1, see Hedayat, A. and Wallis, W. D. (1978) "Hadamard matrices and their applications. The Annals of Statistics, 1184-1238." <doi:10.1214/aos/1176344370>.
Functions for the normal Laplace distribution.  Currently, it
 provides limited functionality.  Density, distribution and quantile
 functions, random number generation, and moments are provided.
A collection of utilities referred to Exponential Power distribution, also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4).
The robustness of many of the statistical techniques, such as factor analysis, applied in 
          the social sciences rests upon the assumption of item-level normality. However, when dealing 
          with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964)
          <http://www.jstor.org/stable/2984418> provides an optimal transformation for non-normal variables. Yet, for 
          large datasets of continuous variables, its application in current software programs is cumbersome
          with analysts having to take several steps to normalise each variable. We present an R package 
          'normalr' that enables researchers to make convenient optimal transformations of multiple variables
          in datasets. This R package enables users to quickly and accurately: (1) anchor all of their 
          variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, 
          (3) apply each unique exponent to its variable, (4) rescale resultant values to within their 
          original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, 
          kurtosis, and other inferential assessments of normality.
Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature.
It provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis, based on the consistency of metrics and the consistency of datasets.
             Zhenfeng Wu, Weixiang Liu, Xiufeng Jin, Deshui Yu, Hua Wang, Gustavo Glusman, Max Robinson, Lin Liu, Jishou Ruan and Shan Gao (2018) <doi:10.1101/251140>.
Cleans and Normalizes FLUOstar DBF and DAT Files obtained from liposome flux assays. Users should verify extended usage of the package on files from other assay types.
Multivariate Normal (i.e. Gaussian) Mixture Models (S3) Classes.
    Fitting models to data using 'MLE' (maximum likelihood estimation) for
    multivariate normal mixtures via smart parametrization using the 'LDL'
    (Cholesky) decomposition, see McLachlan and Peel (2000, ISBN:9780471006268),
    Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>.
Functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided. See Philipps et al (2014) <doi:10.1159/000365637> for details.
Five omnibus tests for testing the composite hypothesis of
        normality.
Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model.
Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Seven normality test are implemented. The asymptotic Lobato & Velasco's, asymptotic Epps, Psaradakis and  Vávra, Lobato & Velasco's and Epps sieve bootstrap approximations, El bouch et al., and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for  stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages.
Calculate NOS (node overlap and segregation) and
    the associated metrics described in Strona and Veech (2015)
    <doi:10.1111/2041-210X.12395> and Strona et al. (2018) 
    <doi:10.1111/ecog.03447>. The functions provided in the 
    package enable assessment of structural patterns ranging from
    complete node segregation to perfect nestedness in a variety 
    of network types. In addition, they provide a measure of 
    network modularity.
The aim of 'nosoi' (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. Methods in Ecology and Evolution 11:1002-1007). It is named after the daimones of plague, sickness and disease that escaped Pandora's jar in the Greek mythology. 'nosoi' is able to take into account the influence of multiple variable on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.
Provides functions to produce advanced ascii graphics, directly to the terminal window. This package utilizes the txtplot() function from the 'txtplot' package, to produce text-based histograms, empirical cumulative distribution function plots, scatterplots with fitted and regression lines, quantile plots, density plots, image plots, and contour plots.
Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following 'deterministic signal + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise. For details, see Baranowski, Chen and Fryzlewicz (2019) <doi:10.1111/rssb.12322>.
This is a pure dummy interfaces package which mirrors 'MsSparkUtils' APIs <https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/microsoft-spark-utilities?pivots=programming-language-r> of 'Azure Synapse Analytics' <https://learn.microsoft.com/en-us/azure/synapse-analytics/> for R users,
    customer of Azure Synapse can download this package from CRAN for local development.
Functions to flash your hue lights, or text yourself, from R. Designed to be used with long running scripts.
Computes the probability density function, the cumulative density function, quantile function, random numbers and measures of inference for the following families exponentiated generalized gull alpha power family, exponentiated gull alpha powerfamily, gull alpha power family.
The raw dataset and model used in Lai et al. (2021) 
    Decoupled responses of native and exotic tree diversities to 
    distance from old-growth forest and soil phosphorous in 
    novel secondary forests. Applied Vegetation Science, 24, e12548.
In semi-structured interviews that use the 'framework' method, it
    is not always clear how refinements to interview questions affect the
    decision of when to stop interviews. The trend of 'novel' and 'duplicate'
    interview codes (novel codes are information that other interviewees have
    not previously mentioned) provides insight into the richness of qualitative
    information. This package provides tools to visualise when refinements occur
    and how that affects the trends of novel and duplicate codes. These
    visualisations, when used progressively as new interviews are finished, can
    help the researcher to decide on a stopping point for their interviews.
Assist novice developers when preparing a single package or a set of integrated packages to submit to CRAN. Automate the following individual or batch processing: check local source packages;  build local .tar.gz source files; install packages from local .tar.gz files; detect conflicts between function names in the environment.
Assist novice developers when preparing a single package or a set of integrated packages to submit to CRAN. Provide additional resources to facilitate the automation of the following individual or batch processing: check local source packages;  build local .tar.gz source files; install packages from local .tar.gz files; detect conflicts between function names in the environment. The additional resources include determining the identity and ordering of the packages to process when updating an imported package.
The noweb system for source code, implemented in R.
The Nozzle package provides an API to generate HTML
        reports with dynamic user interface elements based on
        JavaScript and CSS (Cascading Style Sheets). Nozzle was
        designed to facilitate summarization and rapid browsing of
        complex results in data analysis pipelines where multiple
        analyses are performed frequently on big data sets. The package
        can be applied to any project where user-friendly reports need
        to be created.
Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.
Computes interdaily stability (IS), intradaily variability (IV) & the relative amplitude (RA) from actigraphy data as described in Blume et al. (2016) <doi: 10.1016/j.mex.2016.05.006> and van Someren et al. (1999) <doi: 10.3109/07420529908998724>. Additionally, it also computes L5 (i.e. the 5 hours with lowest average actigraphy amplitude) and M10 (the 10 hours with highest average amplitude) as well as the respective start times. The flex versions will also compute the L-value for a user-defined number of minutes. IS describes the strength of coupling of a rhythm to supposedly stable zeitgebers. It varies between 0 (Gaussian Noise) and 1 for perfect IS. IV describes the fragmentation of a rhythm, i.e. the frequency and extent of transitions between rest and activity. It is near 0 for a perfect sine wave, about 2 for Gaussian noise and may be even higher when a definite ultradian period of about 2 hrs is present. RA is the relative amplitude of a rhythm. Note that to obtain reliable results, actigraphy data should cover a reasonable number of days.
With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers. See Konietschke et al. (2015) <doi:10.18637/jss.v064.i09> for details.
Performs nonparametric
        analysis of longitudinal data in factorial experiments.
        Longitudinal data are those which are collected from the same
        subjects over time, and they frequently arise in biological
        sciences. Nonparametric methods do not require distributional
        assumptions, and are applicable to a variety of data types
        (continuous, discrete, purely ordinal, and dichotomous).  Such
        methods are also robust with respect to outliers and for small
        sample sizes.
Analysis of multivariate data with two-way completely randomized
    factorial design. The analysis is based on fully nonparametric, rank-based
    methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda,
    Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate
    response is allowed to be ordinal, quantitative, binary or a mixture of the
    different variable types. The package offers two functions performing the
    analysis, one for small and the other for large sample sizes. The underlying methodology
    is largely described in Bathke and Harrar (2016) <doi:10.1007/978-3-319-39065-9_7> and in 
    Munzel and Brunner (2000) <doi:10.1016/S0378-3758(99)00212-8>.
Nonparametric Tests for Main Effects, Simple Effects and Interaction Effect with Censored Data and Two Factorial Influencing Variables.
These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) <doi:10.1080/10618600.2013.844700>.
Nested Partially Balanced Bipartite Block (NPBBB) designs involve two levels of blocking: (i) The block design (ignoring sub-block classification) serves as a partially balanced bipartite block (PBBB) design, and (ii) The sub-block design (ignoring block classification) also serves as a PBBB design. More details on constructions of the PBBB designs and their characterization properties are available in Vinayaka et al.(2023) <doi:10.1080/03610926.2023.2251623>. This package calculates A-efficiency values for both block and sub-block structures, along with all parameters of a given NPBBB design.
Addressing crucial research questions often necessitates a small
    sample size due to factors such as distinctive target populations, rarity
    of the event under study, time and cost constraints, ethical concerns, or
    group-level unit of analysis. Many readily available analytic methods,
    however, do not accommodate small sample sizes, and the choice of the best
    method can be unclear. The 'npboottprm' package enables the execution of
    nonparametric bootstrap tests with pooled resampling to help fill this gap.
    Grounded in the statistical methods for small sample size studies detailed
    in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, the
    package facilitates a range of statistical tests, encompassing independent
    t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests.
    The nonparboot() function undertakes essential computations, yielding
    detailed outputs which include test statistics, effect sizes, confidence
    intervals, and bootstrap distributions. Further, 'npboottprm' incorporates
    an interactive 'shiny' web application, nonparboot_app(), offering intuitive,
    user-friendly data exploration.
Sample sizes are often small due to hard to reach target populations,
    rare target events, time constraints, limited budgets, or ethical considerations.
    Two statistical methods with promising performance in small samples are the
    nonparametric bootstrap test with pooled resampling method, which is the
    focus of Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>,
    and informative hypothesis testing, which is implemented in the 'restriktor'
    package. The 'npboottprmFBar' package uses the nonparametric bootstrap test
    with pooled resampling method to implement informative hypothesis testing.
    The bootFbar() function can be used to analyze data with this method and the
    persimon() function can be used to conduct performance simulations on type-one
    error and statistical power.
A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers  as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods.  Its use is illustrated via a number of empirical applications and simulated examples.
An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.
Statistical tools for analyzing cognitive diagnosis (CD) data collected from small settings using the nonparametric classification (NPCD) framework. The core methods of the NPCD framework includes the nonparametric classification (NPC) method developed by Chiu and Douglas (2013) <DOI:10.1007/s00357-013-9132-9> and the general NPC (GNPC) method developed by Chiu, Sun, and Bian (2018) <DOI:10.1007/s11336-017-9595-4> and Chiu and Köhn (2019) <DOI:10.1007/s11336-019-09660-x>. An extension of the NPCD framework included in the package is the nonparametric method for multiple-choice items (MC-NPC) developed by Wang, Chiu, and Koehn (2023) <DOI:10.3102/10769986221133088>.  Functions associated with various extensions concerning the evaluation, validation, and feasibility of the CD analysis are also provided. These topics include the completeness of Q-matrix, Q-matrix refinement method, as well as Q-matrix estimation.
Nonparametric smoothing methods for density and regression estimation involving circular data, 
				including the estimation of the mean regression function and other conditional characteristics.
Nonparametric tests for clustered data in pre-post intervention design documented in Cui and Harrar (2021) <doi:10.1002/bimj.201900310> and Harrar and Cui (2022) <doi:10.1016/j.jspi.2022.05.009>. Other than the main test results mentioned in the reference paper, this package also provides a function to calculate the sample size allocations for the input long format data set, and also a function for adjusted/unadjusted confidence intervals calculations. There are also functions to visualize the distribution of data across different intervention groups over time, and also the adjusted/unadjusted confidence intervals.
An estimation procedure for the analysis of nonparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z)), providing estimation of b(t) and its pointwise standard errors, and semiparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z1 + c*Z2)), providing estimation of b(t), c and their standard errors. More details can be found in Lu Tian et al. (2005) <doi:10.1198/016214504000000845>.
Provides nonparametric CUSUM tests for detecting changes
  in possibly serially dependent univariate or low-dimensional
  multivariate observations. Retrospective tests sensitive to changes
  in the expectation, the variance, the covariance, the
  autocovariance, the distribution function, Spearman's rho, Kendall's
  tau, Gini's mean difference, and the copula are provided, as well as
  a test for detecting changes in the distribution of independent
  block maxima (with environmental studies in mind). The package also
  contains a test sensitive to changes in the autocopula and a
  combined test of stationarity sensitive to changes in the
  distribution function and the autocopula. The latest additions are
  an open-end sequential test based on the retrospective CUSUM
  statistic that can be used for monitoring changes in the mean of
  possibly serially dependent univariate observations, as well as
  closed-end and open-end sequential tests based on empirical
  distribution functions that can be used for monitoring changes in
  the contemporary distribution of possibly serially dependent
  univariate or low-dimensional multivariate observations.
We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021).
Performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.
Performs nonparametric estimation in mixture cure models when the cure status is partially known. For details, see Safari et al (2021) <doi:10.1002/bimj.202100156>, Safari et al (2022) <doi:10.1177/09622802221115880> and Safari et al (2023) <doi:10.1007/s10985-023-09591-x>.
Provides routines to compute normalised prediction distribution errors, a metric designed to evaluate non-linear mixed effect models such as those used in pharmacokinetics and pharmacodynamics.
A novel integral estimator for estimating the causal effects with continuous treatments (or dose-response curves) and a localized derivative estimator for estimating the derivative effects. The inference on the dose-response curve and its derivative is conducted via nonparametric bootstrap.
    The reference paper is Zhang, Chen, and Giessing (2024) <doi:10.48550/arXiv.2405.09003>.
Provides several novel exact hypothesis tests with minimal assumptions on the errors. The tests are exact, meaning that their p-values are correct for the given sample sizes (the p-values are not derived from asymptotic analysis). The test for stochastic inequality is for ordinal comparisons based on two independent samples and requires no assumptions on the errors. The other tests include tests for the mean and variance of a single sample and comparing means in independent samples. All these tests only require that the data has known bounds (such as percentages that lie in [0,100]. These bounds are part of the input.
Provides tools for non-parametric Fourier deconvolution using the N-Power Fourier Deconvolution (NPFD) method. This package includes methods for density estimation (densprf()) and sample generation (createSample()), enabling users to perform statistical analyses on mixed or replicated data sets.
Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions
             modeling oriented towards flow-cytometry data preprocessing applications. Method is 
             detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.
Piecewise constant hazard functions are used to flexibly model survival distributions with non-proportional hazards and 
	to simulate data from the specified distributions. A function to calculate weighted log-rank tests for the comparison of two
	hazard functions is included. Also, a function to calculate a test using the maximum of a set of test statistics from weighted
	log-rank tests (MaxCombo test) is provided. This test utilizes the asymptotic multivariate normal joint distribution of the
	separate test statistics. The correlation is estimated from the data. These methods are described in Ristl et al. (2021) 
	<doi:10.1002/pst.2062>.
	Finally, a function is provided for the estimation and inferential statistics of various parameters that quantify the difference
	between two survival curves. Eligible parameters are differences in survival probabilities, log survival probabilities,
	complementary log log (cloglog) transformed survival probabilities, quantiles of the survival functions, log transformed quantiles,
	restricted mean survival times, as well as an average hazard ratio, the Cox model score statistic (logrank statistic), and the
	Cox-model hazard ratio. Adjustments for multiple testing and simultaneous confidence intervals are calculated using a multivariate
	normal approximation to the set of selected parameters.
Provides functions and examples for histogram, kernel (classical, variable bandwidth and transformations based), discrete and semiparametric hazard rate estimators.
An R-package for calculating sample size of a survival trial with or without cure fractions.
Performs combination tests and sample size calculation for 
   fixed design with survival endpoints using combination tests under either
   proportional hazards or non-proportional hazards. The combination tests 
   include maximum weighted log-rank test and projection test. The sample 
   size calculation procedure is very flexible, allowing for user-defined
   hazard ratio function and considering various trial conditions like 
   staggered entry, drop-out etc. The sample size calculation also applies to 
   various cure models such as proportional hazards cure model, cure model with
   (random) delayed treatments effects. Trial simulation function is also provided 
   to facilitate the empirical power calculation. The references for 
   projection test and maximum weighted logrank test include Brendel et al. (2014)
   <doi:10.1111/sjos.12059> and Cheng and He (2021) <arXiv:2110.03833>. The 
   references for sample size calculation under proportional hazard include
   Schoenfeld (1981) <doi:10.1093/biomet/68.1.316> and Freedman (1982) <doi:10.1002/sim.4780010204>.
   The references for calculation under non-proportional hazards include 
   Lakatos (1988) <doi:10.2307/2531910> and Cheng and He (2023) <doi:10.1002/bimj.202100403>.
Perform a stratified weighted log-rank test in a randomized controlled trial. Tests can be visualized as a difference in average score on the two treatment arms. These methods are described in Magirr and Burman (2018) <doi:10.48550/arXiv.1807.11097>, Magirr (2020) <doi:10.48550/arXiv.2007.04767>, and Magirr and Jimenez (2022) <doi:10.48550/arXiv.2201.10445>.
Access the United States National Provider Identifier
    Registry API <https://npiregistry.cms.hhs.gov/api/>. Obtain and transform
    administrative data linked to a specific individual or organizational
    healthcare provider, or perform advanced searches based on provider name,
    location, type of service, credentials, and other attributes exposed by
    the API.
Returns nonparametric aligned rank tests for the interaction in two-way factorial designs, on R data sets with repeated measures in 'wide' format. Five ANOVAs tables are reported. A PARAMETRIC one on the original data, one for a CHECK upon the interaction alignments, and three aligned rank tests: one on the aligned REGULAR, one on the FRIEDMAN, and one on the KOCH ranks. In these rank tests, only the resulting values for the interaction are relevant.
An implementation of the Nonparametric Predictive Inference approach in R. It provides tools for quantifying uncertainty via lower and upper probabilities. It includes useful functions for pairwise and multiple comparisons: comparing two groups with and without terminated tails, selecting the best group, selecting the subset of best groups, selecting the subset including the best group.
Implements methods introduced in Chen, Christensen, and Kankanala (2024) <doi:10.1093/restud/rdae025> for estimating and constructing uniform confidence bands for nonparametric structural functions using instrumental variables, including data-driven choice of tuning parameters. All methods in this package apply to nonparametric regression as a special case.
Provides routines for plotting
      linkage and association results along a chromosome,
      with marker names displayed along the top border.
      There are also routines for generating BED and BedGraph
      custom tracks for viewing in the UCSC genome browser.
      The data reformatting program Mega2 uses this
      package to plot output from a variety of
      programs.
Performing drug response analyses and IC50 estimations using n-Parameter logistic regression. Can also be applied to proliferation analyses.
Provides functions for manipulating nested data frames in a 
    list-column using 'dplyr' <https://dplyr.tidyverse.org/> syntax. Rather than 
    unnesting, then manipulating a data frame, 'nplyr' allows users to 
    manipulate each nested data frame directly. 'nplyr' is a wrapper for 'dplyr'
    functions that provide tools for common data manipulation steps: filtering 
    rows, selecting columns, summarising grouped data, among others.
Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data. 
             Chapman & Hall/CRC (to appear); and provide fit for using global and local smoothing methods
             for the conditional-mean and conditional-distribution based models with longitudinal Data.
Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).
Nonparametric maximum likelihood estimation or Gaussian
        quadrature for overdispersed generalized linear models and
        variance component models.
Fit multinomial logistic regression with a penalty on the nuclear
    norm of the estimated regression coefficient matrix, using proximal
    gradient descent.
Performs analysis of one-way multivariate data, for small samples using Nonparametric techniques. Using approximations for ANOVA Type, Wilks' Lambda, Lawley Hotelling, and Bartlett Nanda Pillai Test statics, the package compares the multivariate distributions for a single explanatory variable.  The comparison is also performed using a permutation test for each of the four test statistics. The package also performs an all-subsets algorithm regarding variables and regarding factor levels.
Performs nonparametric tests for equality of location against ordered alternatives.
Posterior sampling in several commonly used distributions using
    normalized power prior as described in 
    Duan, Ye and Smith (2006) <doi:10.1002/env.752> and 
    Ibrahim et.al. (2015) <doi:10.1002/sim.6728>. 
    Sampling of the power parameter is achieved via 
    either independence Metropolis-Hastings or random walk Metropolis-Hastings 
    based on transformation.
Implements a nonparametric statistical test for rank or score data
 from partially-balanced incomplete block-design experiments.
Calculates phenological cycle and anomalies using a non-parametric
    approach applied to time series of vegetation indices derived from remote sensing data 
    or field measurements. The package implements basic and high-level functions for 
    manipulating vector data (numerical series) and raster data (satellite derived products).
    Processing of very large raster files is supported. For more information, please check 
    the following paper:
    Chávez et al. (2023) <doi:10.3390/rs15010073>.
Partial informational correlation (PIC) is used to identify the meaningful predictors to the response from a large set of potential predictors. Details of methodologies used in the package can be found in Sharma, A., Mehrotra, R. (2014). <doi:10.1002/2013WR013845>, Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). <doi:10.1016/j.envsoft.2016.05.021>, and Mehrotra, R., & Sharma, A. (2006). <doi:10.1016/j.advwatres.2005.08.007>.
Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.
Estimating the first and second derivatives of a regression function by the method of Wang and Lin (2015) <http://www.jmlr.org/papers/v16/wang15b.html>.
A method for obtaining nonparametric estimates of regression models
    with or without factor-by-curve interactions using local polynomial kernel
    smoothers or splines. Additionally, a parametric model (allometric model) can be
    estimated.
Tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2018, <doi:10.1080/01621459.2017.1285776>): lprobust() for local polynomial point estimation and robust bias-corrected inference, lpbwselect() for local polynomial bandwidth selection, kdrobust() for kernel density point estimation and robust bias-corrected inference, kdbwselect() for kernel density bandwidth selection, and nprobust.plot() for plotting results. The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019, <doi:10.18637/jss.v091.i08>).
In many binary classification applications, such as disease
    diagnosis and spam detection, practitioners commonly face the need to limit
    type I error (i.e., the conditional probability of misclassifying a class 0
    observation as class 1) so that it remains below a desired threshold. To address
    this need, the Neyman-Pearson (NP) classification paradigm is a natural choice;
    it minimizes type II error (i.e., the conditional probability of misclassifying
    a class 1 observation as class 0) while enforcing an upper bound, alpha, on the
    type I error. Although the NP paradigm has a century-long history in hypothesis
    testing, it has not been well recognized and implemented in classification
    schemes. Common practices that directly limit the empirical type I error to
    no more than alpha do not satisfy the type I error control objective because
    the resulting classifiers are still likely to have type I errors much larger
    than alpha. As a result, the NP paradigm has not been properly implemented
    for many classification scenarios in practice. In this work, we develop the
    first umbrella algorithm that implements the NP paradigm for all scoring-type
    classification methods, including popular methods such as logistic regression,
    support vector machines and random forests. Powered by this umbrella algorithm,
    we propose a novel graphical tool for NP classification methods: NP receiver
    operating characteristic (NP-ROC) bands, motivated by the popular receiver
    operating characteristic (ROC) curves. NP-ROC bands will help choose in a data
    adaptive way and compare different NP classifiers.
Implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework.
Fits sphere-sphere regression models by estimating locally weighted
    rotations. Simulation of sphere-sphere data according to non-rigid rotation
    models. Provides methods for bias reduction applying iterative procedures
    within a Newton-Raphson learning scheme. Cross-validation is exploited to select
    smoothing parameters. See Marco Di Marzio, Agnese Panzera & Charles C. Taylor
    (2018) <doi:10.1080/01621459.2017.1421542>.
Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) <doi:10.1017/CBO9780511551710>, Kneip, Simar, and Wilson (2008) <doi:10.1017/S0266466608080651> and Badunenko and Mozharovskyi (2020) <doi:10.1080/01605682.2019.1599778>) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) <doi:10.1017/CBO9781139174411>, Badunenko and Kumbhakar (2016) <doi:10.1016/j.ejor.2016.04.049>). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained.
Accompanies the book "Nonparametric Statistical Methods Using R, 2nd Edition" by Kloke and McKean (2024, ISBN:9780367651350).  Includes methods, datasets, and random number generation useful for the study of robust and/or nonparametric statistics.  Emphasizes classical nonparametric methods for a variety of designs --- especially one-sample and two-sample problems.  Includes methods for general scores, including estimation and testing for the two-sample location problem as well as Hogg's adaptive method.
Multidimensional nonparametric spatial (spatio-temporal) geostatistics.
    S3 classes and methods for multidimensional: linear binning,
    local polynomial kernel regression (spatial trend estimation), density and variogram estimation.
    Nonparametric methods for simultaneous inference on both spatial trend
    and variogram functions (for spatial processes).
    Nonparametric residual kriging (spatial prediction).
    For details on these methods see, for example, Fernandez-Casal and Francisco-Fernandez (2014) 
    <doi:10.1007/s00477-013-0817-8> or Castillo-Paez et al. (2019) <doi:10.1016/j.csda.2019.01.017>.
Non-parametric survival analysis of exact and
	     interval-censored observations. The methods implemented
	     are developed by Wang (2007)
	     <doi:10.1111/j.1467-9868.2007.00583.x>, Wang (2008)
	     <doi:10.1016/j.csda.2007.10.018>, Wang and Taylor (2013)
	     <doi:10.1007/s11222-012-9341-9> and Wang and Fani (2018)
	     <doi:10.1007/s11222-017-9724-z>.
A number of statistical tests have been proposed to compare two 
  survival curves, including the difference in (or ratio of) t-year 
  survival, difference in (or ratio of) p-th percentile survival, difference in
  (or ratio of) restricted mean survival time, and the weighted log-rank test. 
  Despite the multitude of options, the convention in survival studies is to assume
  proportional hazards and to use the unweighted log-rank test for design and 
  analysis. This package provides sample size and power 
  calculation for all of the above statistical tests with allowance for 
  flexible accrual, censoring, and survival (eg. Weibull, piecewise-exponential, 
  mixture cure). It is the companion R package to the paper by Yung and Liu (2020)
  <doi:10.1111/biom.13196>. Specific to the weighted log-rank test, users may 
  specify which approximations they wish to use to estimate the large-sample mean 
  and variance. The default option has been shown to provide substantial
  improvement over the conventional sample size and power equations based on Schoenfeld 
  (1981) <doi:10.1093/biomet/68.1.316>.
Robust nonparametric bootstrap and permutation tests for location, correlation, and regression problems, as described in Helwig (2019a) <doi:10.1002/wics.1457> and Helwig (2019b) <doi:10.1016/j.neuroimage.2019.116030>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the 'parallel' package.
Implements the procedure from G. J. Ross (2021) - "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" <arxiv:2107.01742>. This uses a version of Wild Binary Segmentation to detect multiple location-scale (i.e. mean and/or variance) change points in a sequence of univariate observations, with a strict control on the probability of incorrectly detecting a change point in a sequence which does not contain any.
Facilitates nonresponse bias analysis (NRBA) 
    for survey data.  Such data may arise from a complex
    sampling design with features such as stratification, clustering, or
    unequal probabilities of selection. Multiple types of analyses may be
    conducted: comparisons of response rates across subgroups; comparisons
    of estimates before and after weighting adjustments; comparisons of
    sample-based estimates to external population totals; tests of
    systematic differences in covariate means between respondents
    and full samples; tests of independence between response status
    and covariates; and modeling of outcomes and response status as a 
    function of covariates. Extensive documentation and references are
    provided for each type of analysis. Krenzke, Van de Kerckhove,
    and Mohadjer (2005) <http://www.asasrms.org/Proceedings/y2005/files/JSM2005-000572.pdf>
    and Lohr and Riddles (2016) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2016002/article/14677-eng.pdf?st=q7PyNsGR>
    provide an overview of the methods implemented in this package.
Provides a function designed to estimate the minimal sample size required to attain a specific statistical power in the context of linear regression and logistic regression models through simulations.
Implements methods in Mathur and VanderWeele (in preparation) to characterize global evidence strength across W correlated ordinary least squares (OLS) hypothesis tests. Specifically, uses resampling to estimate a null interval for the total number of rejections in, for example, 95% of samples generated with no associations (the global null), the excess hits (the difference between the observed number of rejections and the upper limit of the null interval), and a test of the global null based on the number of rejections.
Calculating the net reclassification improvement (NRI) for risk prediction models with time to event and binary data.
Executes nonstationary Fay-Herriot model and nonstationary generalized linear mixed model for small area estimation.The empirical best linear unbiased predictor (EBLUP) under stationary and nonstationary Fay-Herriot models and empirical best predictor (EBP) under nonstationary generalized linear mixed model along with the mean squared error estimation are included. EBLUP for prediction of non-sample area is also included under both stationary and nonstationary Fay-Herriot models. This extension to the Fay-Herriot model that accounts for the presence  of spatial nonstationarity was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2015) <doi:10.1093/jssam/smu026> and nonstationary generalized linear mixed model was developed by Hukum  Chandra, Nicola Salvati and Ray Chambers (2017) <doi:10.1016/j.spasta.2017.01.004>. This package is dedicated to the memory of Dr. Hukum Chandra who passed away while the package creation was in progress.
Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of covariance stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) <doi:10.1111/j.1368-423X.2007.00202.x>. Beran, J. (1995) <doi:10.1111/j.2517-6161.1995.tb02054.x>.
Two implementations of canonical correlation analysis
        (CCA) that are based on iterated regression. By choosing the
        appropriate regression algorithm for each data domain, it is
        possible to enforce sparsity, non-negativity or other kinds of
        constraints on the projection vectors. Multiple canonical
        variables are computed sequentially using a generalized
        deflation scheme, where the additional correlation not
        explained by previous variables is maximized. nscancor() is
        used to analyze paired data from two domains, and has the same
        interface as cancor() from the 'stats' package (plus some extra
        parameters). mcancor() is appropriate for analyzing data from
        three or more domains. See
        <https://sigg-iten.ch/learningbits/2014/01/20/canonical-correlation-analysis-under-constraints/>
        and Sigg et al. (2007) <doi:10.1109/MLSP.2007.4414315> for more
        details.
Simulation and estimation for Neyman-Scott spatial cluster point
 process models and their extensions, based on the methodology in Tanaka, Ogata,
 and Stoyan (2008) <doi:10.1002/bimj.200610339>. To estimate parameters by the
 simplex method, parallel computation using 'OpenMP' application programming
 interface is available. For more details see Tanaka, Saga and Nakano
 <doi:10.18637/jss.v098.i06>.
Collection of functions designed to calculate numerical standard error (NSE) of univariate time series as described in Ardia et al. (2018) <doi:10.1515/jtse-2017-0011> and Ardia and Bluteau (2017) <doi:10.21105/joss.00172>.
Count the occurrence of sequences of values in a vector that meets certain conditions of length and magnitude. The method is based on the Run Length Encoding algorithm, available with base R, inspired by A. H. Robinson and C. Cherry (1967) <doi:10.1109/PROC.1967.5493>.
Download Current & Historical Bhavcopy. Get Live Market data from NSE India of Equities and Derivatives (F&O) segment. Data source  <https://www.nseindia.com/>.
Box-constrained multiobjective optimization using the 
        elitist non-dominated sorting genetic algorithm - NSGA-II.
        Fast non-dominated sorting, crowding distance, tournament 
        selection, simulated binary crossover, and polynomial 
        mutation are called in the main program. The methods are 
        described in Deb et al. (2002) <doi:10.1109/4235.996017>.
An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective 
    feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm
    that solves multiple optimization problems simultaneously by applying a non-dominated sorting 
    technique. It uses a reference points based selection operator to explore 
    solution space and preserve diversity. See the original paper by K. Deb and 
    H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.
Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods.  Exact procedures are performed when computationally possible.  Monte Carlo and Asymptotic procedures are performed otherwise.  For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.
Designed to add datasets which are used in the Nonparametric Statistical Methods textbook, 3rd edition.
National Statistical Office of Mongolia (NSO) is the national statistical service and an organization of Mongolian government. NSO provides open access to official data via its API <http://opendata.1212.mn/en/doc>. The package NSO1212 has functions for accessing the API service. The functions are compatible with the API v2.0 and get data sets and its detailed informations from the API.
Implementation of Narrowest Significance Pursuit, a general and
    flexible methodology for automatically detecting localised regions in data sequences
    which each must contain a change-point (understood as an abrupt change in the
    parameters of an underlying linear model), at a prescribed global significance level.
    Narrowest Significance Pursuit works with a wide range of distributional assumptions
    on the errors, and yields exact desired finite-sample coverage probabilities,
    regardless of the form or number of the covariates. For details, see P. Fryzlewicz
    (2021) <https://stats.lse.ac.uk/fryzlewicz/nsp/nsp.pdf>.
Mainly for maximum likelihood estimation of nonparametric
             and semiparametric mixture models, but can also be used
             for fitting finite mixtures. The algorithms are
             developed in Wang (2007)
             <doi:10.1111/j.1467-9868.2007.00583.x> and Wang (2010)
             <doi:10.1007/s11222-009-9117-z>.
Two methods for performing a constrained principal
        component analysis (PCA), where non-negativity and/or sparsity
        constraints are enforced on the principal axes (PAs). The
        function 'nsprcomp' computes one principal component (PC) after
        the other. Each PA is optimized such that the corresponding PC
        has maximum additional variance not explained by the previous
        components. In contrast, the function 'nscumcomp' jointly
        computes all PCs such that the cumulative variance is maximal.
        Both functions have the same interface as the 'prcomp' function
        from the 'stats' package (plus some extra parameters), and both
        return the result of the analysis as an object of class
        'nsprcomp', which inherits from 'prcomp'. See
        <https://sigg-iten.ch/learningbits/2013/05/27/nsprcomp-is-on-cran/>
        and Sigg et al. (2008) <doi:10.1145/1390156.1390277> for more
        details.
Provides access to the 'Native Status Resolver' (NSR) <https://github.com/ojalaquellueva/nsr> API through R. The user supplies plant taxonomic names and political divisions and the package returns information about their likely native status (e.g., native, non-native,endemic), along with information on how those decisions were made.
A collection of statistical tools for objective (non-supervised) applications 
             of the Regional Frequency Analysis methods in hydrology. 
             The package refers to the index-value method and, more precisely, helps the
             hydrologist to: (1) regionalize the index-value; (2) form homogeneous regions 
             with similar growth curves; (3) fit distribution functions to the 
             empirical regional growth curves.
             Most of the methods are those described in the Flood Estimation Handbook 
            (Centre for Ecology & Hydrology, 1999, ISBN:9781906698003).
             Homogeneity tests from Hosking and Wallis (1993) <doi:10.1029/92WR01980> 
             and Viglione et al. (2007) <doi:10.1029/2006WR005095> are available.
Tools for estimating Receiver Operating Characteristic (ROC) curves,
        building confidence bands, comparing several curves both for dependent and 
        independent data, estimating the cumulative-dynamic ROC curve in presence of
        censored data, and performing meta-analysis studies, among others.
To estimate ecological stochasticity in community assembly. Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. The new index, normalized stochasticity ratio (NST), is to estimate ecological stochasticity, i.e. relative importance of stochastic processes, in community assembly. With functions in this package, NST can be calculated based on different similarity metrics and/or different null model algorithms, as well as some previous indexes, e.g. previous Stochasticity Ratio (ST), Standard Effect Size (SES), modified Raup-Crick metrics (RC). Functions for permutational test and bootstrapping analysis are also included. Previous ST is published by Zhou et al (2014) <doi:10.1073/pnas.1324044111>. NST is modified from ST by considering two alternative situations and normalizing the index to range from 0 to 1 (Ning et al 2019) <doi:10.1073/pnas.1904623116>. A modified version, MST, is a special case of NST, used in some recent or upcoming publications, e.g. Liang et al (2020) <doi:10.1016/j.soilbio.2020.108023>. SES is calculated as described in Kraft et al (2011) <doi:10.1126/science.1208584>. RC is calculated as reported by Chase et al (2011) <doi:10.1890/ES10-00117.1> and Stegen et al (2013) <doi:10.1038/ismej.2013.93>. Version 3 added NST based on phylogenetic beta diversity, used by Ning et al (2020) <doi:10.1038/s41467-020-18560-z>.
Geospatial data for creating maps of New South Wales (NSW),
  Australia, and some helpers to work with common problems like normalising
  postcodes. Registers its data with 'cartographer'.
Counts syllables in character vectors for English words.  Imputes syllables as the number of vowel sequences for words not found.
Downloads the latest 'National Transit Database' data,
    processes it, and returns in a tidy data format.
The 'ntfy' (pronounce: notify) service is a simple HTTP-based pub-sub
    notification service. It allows you to send notifications to your phone or 
    desktop via scripts from any computer, entirely without signup, cost or 
    setup. It's also open source if you want to run your own. 
    Visit <https://ntfy.sh> for more details.
Implements statistical tools for analyzing, simulating, and computing properties of the New Topp-Leone Kumaraswamy Inverse Exponential (NTLKwIEx) distribution. See Atchadé M, Otodji T, and Djibril A (2024) <doi:10.1063/5.0179458> and Atchadé M, Otodji T, Djibril A, and N'bouké M (2023) <doi:10.1515/phys-2023-0151> for details.
Dealing with neutrosophic data of the form N=D+I(where N is a Neutrosophic number
   ,D is the determinant part of the number and I is  the indeterminacy part)
   using the neutrosophic two way anova test keeps the type I error low.
   This algorithm calculates the fisher statistics when we have a neutrosophic
   data, also tests two hypothesizes, first is to test differences between treatments,
   and second is to test differences between sectors. For more information see
    Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022)
    <https://www.americaspg.com/articleinfo/21/show/1058>.
Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).
Provides a collection of datasets related to neutrosophic sets for statistical modeling and analysis.
Computes the pdf, cdf, quantile function and generating random numbers for neutrosophic distributions. This family have been developed by different authors in the recent years. See Patro and Smarandache (2016) <doi:10.5281/zenodo.571153> and Rao et al (2023) <doi:10.5281/zenodo.7832786>.
Nonparametric test of independence between a pair of spatial objects
    (random fields, point processes) based on random shifts with torus or variance correction. See
    Mrkvička et al. (2021) <doi:10.1016/j.spasta.2020.100430>,
    Dvořák et al. (2022) <doi:10.1111/insr.12503>,
    Dvořák and Mrkvička (2024) <doi:10.1080/10618600.2024.2357626>.
Especially when cross-sectional data are observational, effects of treatment selection
  bias and confounding are best revealed by using Nonparametric and Unsupervised methods to
  "Design" the analysis of the given data ...rather than the collection of "designed data".
  Specifically, the "effect-size distribution" that best quantifies a potentially causal
  relationship between a numeric y-Outcome variable and either a binary t-Treatment or
  continuous e-Exposure variable needs to consist of BLOCKS of relatively well-matched
  experimental units (e.g. patients) that have the most similar X-confounder characteristics.
  Since our NU Learning approach will form BLOCKS by "clustering" experimental units in
  confounder X-space, the implicit statistical model for learning is One-Way ANOVA. Within
  Block measures of effect-size are then either [a] LOCAL Treatment Differences (LTDs) between
  Within-Cluster y-Outcome Means ("new" minus "control") when treatment choice is
  Binary or else [b] LOCAL Rank Correlations (LRCs) when the e-Exposure variable is numeric
  with (hopefully many) more than two levels. An Instrumental Variable (IV) method is also
  provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute
  information for effect-size inferences when X-Covariates are assumed to influence Treatment
  choice or Exposure level but otherwise have no direct effects on y-Outcomes. Finally, a
  "Most-Like-Me" function provides histograms of effect-size distributions to aid
  Doctor-Patient (or Researcher-Society) communications about Heterogeneous Outcomes.
  Obenchain and Young (2013) <doi:10.1080/15598608.2013.772821>; Obenchain, Young and Krstic
  (2019) <doi:10.1016/j.yrtph.2019.104418>.
Tools for 4D nucleome imaging. 
    Quantitative analysis of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy. 
    See Volker J. Schmid, Marion Cremer, Thomas Cremer (2017) <doi:10.1016/j.ymeth.2017.03.013>.
Modelling the vegetation, carbon, nitrogen and water dynamics of undisturbed open bog ecosystems in a temperate to sub-boreal climate. The executable of the model can downloaded from <https://github.com/jeroenpullens/NUCOMBog>.
Comprehensive R package designed to facilitate the calculation of Nitrogen Use Efficiency (NUE) indicators using experimentally derived data. The package incorporates 23 parameters categorized into six fertilizer-based, four plant-based, three soil-based, three isotope-based, two ecology-based, and four system-based indicators, providing a versatile platform for NUE assessment. As of the current version, 'NUETON' serves as a starting point for users to compute NUE indicators from their experimental data. Future updates are planned to enhance the package's capabilities, including robust data visualization tools and error margin consideration in calculations. Additionally, statistical methods will be integrated to ensure the accuracy and reliability of the calculated indicators. All formulae used in 'NUETON' are thoroughly referenced within the source code, and the package is released as open source software. Users are encouraged to provide feedback and contribute to the improvement of this package. It is important to note that the current version of 'NUETON' is not intended for rigorous research purposes, and users are responsible for validating their results. The package developers do not assume liability for any inaccuracies in calculations. This package includes content from Congreves KA, Otchere O, Ferland D, Farzadfar S, Williams S and Arcand MM (2021) 'Nitrogen Use Efficiency Definitions of Today and Tomorrow.' Front. Plant Sci. 12:637108. <doi:10.3389/fpls.2021.637108>. The article is available under the Creative Commons Attribution License (CC BY) C. 2021 Congreves, Otchere, Ferland, Farzadfar, Williams and Arcand.
Extensible framework for
    subgroup discovery (Atzmueller (2015) <doi:10.1002/widm.1144>),
    contrast patterns (Chen (2022) <doi:10.48550/arXiv.2209.13556>),
    emerging patterns (Dong (1999) <doi:10.1145/312129.312191>),
    association rules (Agrawal (1994) <https://www.vldb.org/conf/1994/P487.PDF>) and
    conditional correlations (Hájek (1978) <doi:10.1007/978-3-642-66943-9>).
    Both crisp (Boolean, binary) and fuzzy data are supported.
    It generates conditions in the form of elementary conjunctions, evaluates
    them on a dataset and checks the induced sub-data for interesting statistical
    properties. A user-defined function may be defined to evaluate on each generated
    condition to search for custom patterns.
Tools for visual inference. Generate null data sets
    and null plots using permutation and simulation. Calculate distance metrics
    for a lineup, and examine the distributions of metrics.
A computational method that infers copy number variations (CNVs) in cancer scRNA-seq data and reconstructs the tumor phylogeny. 'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at <https://kharchenkolab.github.io/numbat/>. For details on the method please see Gao et al. Nature Biotechnology (2022) <doi:10.1038/s41587-022-01468-y>.
Converts written out numbers into their equivalent numbers.
    Supports numbers written out in English, French, or Spanish.
The number of distinct alleles observed in a DNA mixture is informative of the number of contributors to the mixture. The package provides methods for computing the probability distribution of the number of distinct alleles in a mixture for a given set of allele frequencies. The mixture contributors may be related according to a provided pedigree.
Provides number-theoretic functions for factorization, prime 
    numbers, twin primes, primitive roots, modular logarithm and
    inverses, extended GCD, Farey series and continued fractions.
    Includes Legendre and Jacobi symbols, some divisor functions,
    Euler's Phi function, etc.
Validate, format and compare identification numbers used in Brazil.
  These numbers are used to identify individuals (CPF), vehicles (RENAVAN),
  companies (CNPJ) and etc.
  Functions to format, validate and compare these numbers have been 
  implemented in a vectorized way in order to speed up
  validations and comparisons in big datasets.
Methods for calculating (usually) accurate
	numerical first and second order derivatives. Accurate calculations 
	are done using 'Richardson''s' extrapolation or, when applicable, a
	complex step derivative is available. A simple difference 
	method is also provided. Simple difference is (usually) less accurate
	but is much quicker than 'Richardson''s' extrapolation and provides a 
	useful cross-check. 
	Methods are provided for real scalar and vector valued functions.
High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
Format numbers and plots for publication; includes the removal of leading zeros,
           standardization of number of digits, addition of affixes, and a p-value formatter. These
           tools combine the functionality of several 'base' functions such as 'paste()',
           'format()', and 'sprintf()' into specific use case functions that are named in a way
           that is consistent with usage, making their names easy to remember and easy to deploy.
A number series generator that creates number series items based on cognitive models.
To add the table of numbers at risk below the Kaplan-Meier plot.
Optimizing regular numeric problems in optically stimulated luminescence dating, 
        such as: equivalent dose calculation, dose rate determination, growth curve fitting, decay 
        curve decomposition, statistical age model optimization, and statistical plot visualization.
Segmentation of short text sequences - like hashtags - into the
    separated words sequence, done with the use of dictionary, which may be
    built on custom corpus of texts. Unigram dictionary is used to find most
    probable sequence, and n-grams approach is used to determine possible
    segmentation given the text corpus.
Provides a tool set for food information and dietary assessment. It 
    uses food composition data from several reference databases, including: 'USDA' (United States), 
    'CIQUAL' (France), 'BEDCA' (Spain), 'CNF' (Canada) and 'STFCJ' (Japan). 'NutrienTrackeR' calculates 
    the intake levels for both macronutrient and micronutrients, and compares them with the recommended 
    dietary allowances (RDA). It includes a number of visualization tools, such as time series 
    plots of nutrient intake, and pie-charts showing the main foods contributing to the intake 
    level of a given nutrient. A shiny app exposing the main functionalities of the package is also 
    provided.
Statistical tool for learning the structure of direct associations among variables for 
			 continuous data, discrete data and mixed discrete-continuous data. The package is based
			 on the copula graphical model in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287>.
Contains a collection of functions for performing different kinds
    of calculation that are of interest to someone following a diet plan.
    Calculators for the Basal Metabolic Rate are based on Mifflin et al. (1990)
    <doi:10.1093/ajcn/51.2.241> and  McArdle, W. D., Katch, F. I., &
    Katch, V. L. (2010, ISBN:9780812109917).
Motivated by changing administrative boundaries over time,
    the 'nuts' package can convert European regional data with NUTS codes
    between versions (2006, 2010, 2013, 2016 and 2021) and levels (NUTS 1,
    NUTS 2 and NUTS 3). The package uses spatial interpolation as in Lam
    (1983) <doi:10.1559/152304083783914958> based on granular (100m x
    100m) area, population and land use data provided by the European
    Commission's Joint Research Center.
Estimate nonlinear vector autoregression models (also known as the 
    next generation reservoir computing) for nonlinear dynamic systems. The 
    algorithm was described by Gauthier et al. (2021) <doi:10.1038/s41467-021-25801-2>.
Fits Bayesian regularized varying coefficient models with the Nonparametric Varying Coefficient Spike-and-Slab Lasso (NVC-SSL) introduced by Bai et al. (2023) <arXiv:1907.06477>. Functions to fit frequentist penalized varying coefficients are also provided, with the option of employing the group lasso penalty of Yuan and Lin (2006) <doi:10.1111/j.1467-9868.2005.00532.x>, the group minimax concave penalty (MCP) of Breheny and Huang <doi:10.1007/s11222-013-9424-2>, or the group smoothly clipped absolute deviation (SCAD) penalty of Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>.
The n-vector framework uses the normal vector to
    the Earth ellipsoid (called n-vector) as a non-singular position
    representation that turns out to be very convenient for practical
    position calculations.  The n-vector is simple to use and gives exact
    answers for all global positions, and all distances, for both
    ellipsoidal and spherical Earth models.  This package is a translation
    of the 'Matlab' library from FFI, the Norwegian Defence Research
    Establishment, as described in Gade (2010)
    <doi:10.1017/S0373463309990415>.
Functions for working with (grouped) multivariate normal variance mixture
  distributions (evaluation of distribution functions and densities,
  random number generation and parameter estimation), including
  Student's t distribution for non-integer degrees-of-freedom as well as the grouped t
  distribution and copula with multiple degrees-of-freedom parameters.
  See <doi:10.18637/jss.v102.i02> for a high-level description of select functionality.
Airline on-time data for all flights departing NYC in 2013.
    Also includes useful 'metadata' on airlines, airports, weather, and
    planes.
Updating the now 10-year-old 'nycflights13' data package. It contains information about all flights that departed from the three main New York City airports in 2023 and metadata on airlines, airports, weather, and planes.
Convenient access to New Zealand election
    results by voting place.  Voting places have been matched to Regional Council,
    Territorial Authority, and Area Unit, to facilitate matching with additional data.
    Opinion polls since 2002 and some convenience analytical function are also supplied.
Access the New Zealand Freshwater Fish Database from R and a few functions to clean the data once in R.
'LaBB-CAT' is a web-based language corpus management
 system developed by the New Zealand Institute of Language, Brain
 and Behaviour (NZILBB) - see <https://labbcat.canterbury.ac.nz>.
 This package defines functions for accessing corpus data in a 'LaBB-CAT'
 instance. You must have at least version 20230224.1731 of 'LaBB-CAT'
 to use this package.
 For more information about 'LaBB-CAT', see
 Robert Fromont and Jennifer Hay (2008) <doi:10.3366/E1749503208000142>
 or 
 Robert Fromont (2017) <doi:10.1016/j.csl.2017.01.004>.
Tools to support research on vowel covariation. Methods are provided to
    support Principal Component Analysis workflows (as in Brand et al. (2021) 
    <doi:10.1016/j.wocn.2021.101096> and Wilson Black et al. (2023) 
    <doi:10.1515/lingvan-2022-0086>).
Datasets of driving offences and fines in New Zealand between 2009 and 2017.
    Originally published by the New Zealand Police at
    <http://www.police.govt.nz/about-us/publication/road-policing-driver-offence-data-january-2009-december-2017>.
Bayesian reconstruction of who infected whom during past outbreaks using routinely-collected surveillance data. Inference of transmission trees using genotype, age specific social contacts, distance between cases and onset dates of the reported cases. (Robert A, Kucharski AJ, Gastanaduy PA, Paul P, Funk S. (2020) <doi:10.1098/rsif.2020.0084>).
Provides functions to do 'O2PLS-DA' analysis for multiple omics data integration.
            The algorithm came from "O2-PLS, a two-block (X±Y) latent variable regression (LVR) method with an integral OSC filter" 
            which published by Johan Trygg and Svante Wold at 2003 <doi:10.1002/cem.775>. 
            'O2PLS' is a bidirectional multivariate regression method that aims to separate the covariance between
            two data sets (it was recently extended to multiple data sets) (Löfstedt and Trygg, 2011 <doi:10.1002/cem.1388>; Löfstedt et al., 2012 <doi:10.1016/j.aca.2013.06.026>) 
            from the systematic sources of variance being specific for each data set separately.
Provides carefully chosen color palettes as used a.o. at OpenAnalytics <http://www.openanalytics.eu>.
A general purpose client to work with any 'OAI-PMH'
    (Open Archives Initiative Protocol for 'Metadata' Harvesting) service.
    The 'OAI-PMH' protocol is described at
    <http://www.openarchives.org/OAI/openarchivesprotocol.html>.
    Functions are provided to work with the 'OAI-PMH' verbs: 'GetRecord',
    'Identify', 'ListIdentifiers', 'ListMetadataFormats', 'ListRecords', and
    'ListSets'.
Harvest metadata using the Open Archives Initiative Protocol for Metadata
  Harvesting (OAI-PMH) version 2.0 (for more information, see
  <https://www.openarchives.org/OAI/openarchivesprotocol.html>).
A comprehensive set of helpers that streamline data transmission
  and processing, making it effortless to interact with the 'OpenAI' API.
Offers a suite of functions for enhancing R plots.
Implements the efficient algorithm by Ortmann and Brandes (2017)
  <doi:10.1007/s41109-017-0027-2> to compute the orbit-aware frequency distribution of induced and non-induced quads, i.e. subgraphs of size four. Given an edge matrix, data frame, or a graph object (e.g., 'igraph'), the orbit-aware counts are computed respective each of the edges and nodes.
Generalise the starting point of the array index.
A comprehensive set of indexes and tests for social segregation analysis,
              as described in Tivadar (2019) - 'OasisR': An R Package to Bring Some Order
              to the World of Segregation Measurement <doi:10.18637/jss.v089.i07>.
              The package  is the most complete existing tool and it clarifies
              many ambiguities and errors regarding the definition of segregation
              indices. Additionally, 'OasisR' introduces several resampling methods
              that enable testing their statistical significance
              (randomization tests, bootstrapping, and jackknife methods).
An implementation of the Blinder-Oaxaca decomposition for linear regression models.
Data processing, visualisation and analysis of Limit Order Book
    event data.
It makes an objective Bayesian analysis of the spatial regression model using both the normal (NSR) and student-T (TSR) distributions. The functions provided give prior and posterior objective densities and allow default Bayesian estimation of the model regression parameters. Details can be found in Ordonez et al. (2020) <arXiv:2004.04341>.
This database contains necessary data relevant to medical costs on obesity throughout the United States. This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses. This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis". Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic (2022) "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" <doi:10.3390/risks10100197>. The database is composed of the following main tables: 1. Relative_Risks: (constant) Relative risks for a given disease group with a risk factor of obesity; 2. Disease_Cost: (obesity_cost_disease) Supplementary output with all variables related to individual disease groups in a given state and year; 3. Full_Cost: (obesity_cost_full) Complete output with all variables used to make cost calculations, as well as cost calculations in a given state and year; 4. National_Summary: (obesity_cost_national_summary) National summary cost calculations in a given year. Three functions are included to assist users in calling and adjusting the mentioned tables and they are data_load(), data_produce(), and rel_risk_fun().
When people make decisions, they may do so using a wide variety of decision rules. The package allows users to easily create obfuscation games to test the obfuscation hypothesis. It provides an easy to use interface and multiple options designed to vary the difficulty of the game and tailor it to the user's needs. For more detail: Chorus et al., 2021, Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence, Mathematical Social Sciences, 109, 28-44, <doi:10.1016/j.mathsocsci.2020.10.002>.
The Open Bodem Index (OBI) is a method to evaluate the quality of soils of agricultural fields in The Netherlands and the sustainability of the current agricultural practices.
    The OBI score is based on four main criteria: chemical, physical, biological and management, which consist of more than 21 indicators. 
    By providing results of a soil analysis and management info the 'OBIC' package can be use to calculate he scores, indicators and derivatives that are used by the OBI.
    More information about the Open Bodem Index can be found at <https://openbodemindex.nl/>.
Supports the definition of sets of properties on objects. Observers can listen to changes on individual properties or the set as a whole. The properties are meant to be fully self-describing. In support of this, there is a framework for defining enumerated types, as well as other bounded types, as S4 classes.
An 'RStudio' addin to assist with removing objects from the global environment. Features include removing objects according to name patterns and object type. During the course of an analysis, temporary objects are often created and this tool assists with removing them quickly. This can be useful when memory management within 'R' is important.
A mutable Signal object can report changes to its state,
    clients could register functions so that they are called whenever
    the signal is emitted. The signal could be emitted, disconnected,
    blocked, unblocked, and buffered.
Obtain optimum block from Non-overlapping Block Bootstrap method.
Three-dimensional rendering for 'grid' and 'ggplot2' graphics using cubes and cuboids drawn with an oblique projection.  As a special case also supports primary view orthographic projections.  Can be viewed as an extension to the 'isocubes' package <https://github.com/coolbutuseless/isocubes>.
Oblique random survival forests incorporate linear combinations of input variables into random survival forests (Ishwaran, 2008 <DOI:10.1214/08-AOAS169>). Regularized Cox proportional hazard models (Simon, 2016 <DOI:10.18637/jss.v039.i05>) are used to identify optimal linear combinations of input variables.
Applies an objective Bayesian method to the Mb capture-recapture model to estimate the population size N.  The Mb model is a class of capture-recapture methods used to account for variations in capture probability due to animal behavior.  Under the Mb formulation,  the initial capture of an animal may effect the probability of subsequent captures due to their becoming "trap happy" or "trap shy."
An implementation for computing Optimal B-Robust Estimators of two-parameter 
    distribution. The procedure is composed of some equations
    that are evaluated alternatively until the solution is reached. Some tools
    for analyzing the estimates are included. The most relevant is
    covariance matrix computation using a closed formula.
Two-part system for first collecting then managing direct
    observation data, as described by Hibbing PR, Ellingson LD,
    Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.
Implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models, see Deldossi and Nai Ruscone (2020) <doi:10.18637/jss.v094.i02>.
Observational studies are limited in that there could be an unmeasured variable related to both the response variable and the primary predictor.  If this unmeasured variable were included in the analysis it would change the relationship (possibly changing the conclusions).  Sensitivity analysis is a way to see how much of a relationship needs to exist with the unmeasured variable before the conclusions change.  This package provides tools for doing a sensitivity analysis for regression (linear, logistic, and cox) style models.
Estimates optimal classification (Poole 2000) <doi:10.1093/oxfordjournals.pan.a029814>
		scores from roll call votes supplied though a 'rollcall'
		object from package 'pscl'.
Computes optimal capital allocations based on some standard principles such as Haircut, Overbeck type II and the Covariance Allocation Principle. It also provides some shortcuts for obtaining the Value at Risk and the Expectation Shortfall, using both the normal and the t-student distribution, see Urbina and Guillén (2014)<doi:10.1016/j.eswa.2014.05.017> and Urbina (2013)<http://hdl.handle.net/2099.1/19443>.
Estimate the positron emission tomography (PET) neuroreceptor occupancies from the total volumes of distribution of a set of regions of interest. Fitting methods include the simple 'reference region', 'ordinary least squares' (sometimes known as occupancy plot), and 'restricted maximum likelihood estimation'.
Facilitates the gathering of biodiversity occurrence data 
  from disparate sources. Metadata is managed throughout the process to facilitate 
  reporting and enhanced ability to repeat analyses.
Fits multispecies site occupancy models to environmental DNA
    metabarcoding data collected using spatially-replicated survey design.
    Model fitting results can be used to evaluate and compare the effectiveness
    of species detection to find an efficient survey design.
    Reference: Fukaya et al. (2022) <doi:10.1111/2041-210X.13732>.
The classical and extended occupancy distributions occur in cases where balls are randomly allocated 
    to bins. The PDF, CDF, quantile functions, generation of random variates, and calculating 
    the first four central moments of the distributions are implemented as described in 
    O’Neill (2019) <doi:10.1080/00031305.2019.1699445>.
Perform interactive occupation coding during interviews as
    described in Peycheva, D., Sakshaug, J., Calderwood, L. (2021) <doi:10.2478/jos-2021-0042>
    and Schierholz, M., Gensicke, M., Tschersich, N., Kreuter, F. (2018) <doi:10.1111/rssa.12297>.
    Generate suggestions for occupational categories based on free text
    input, with pre-trained machine learning models in German and a ready-to-use
    shiny application provided for quick and easy data collection.
Implements the algorithm in Chen, Wang and Samworth (2020) <arxiv:2003.03668> for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) <doi:10.1093/biomet/asq010>, Xie and Siegmund (2013) <doi:10.1214/13-AOS1094> and Chan (2017) <doi:10.1214/17-AOS1546>.
Supports the analysis of Oceanographic data, including 'ADCP'
    measurements, measurements made with 'argo' floats, 'CTD' measurements,
    sectional data, sea-level time series, coastline and topographic data, etc.
    Provides specialized functions for calculating seawater properties such as
    potential temperature in either the 'UNESCO' or 'TEOS-10' equation of state.
    Produces graphical displays that conform to the conventions of the
    Oceanographic literature. This package is discussed extensively by
    Kelley (2018) "Oceanographic Analysis with R" <doi:10.1007/978-1-4939-8844-0>.
Provides tools for easy exploration of the world ocean atlas of 
  the US agency National Oceanic and Atmospheric Administration (NOAA). It 
  includes functions to extract NetCDF data from the repository and code to 
  visualize several physical and chemical parameters of the ocean. A Shiny app 
  further allows interactive exploration of the data. The methods for data 
  collecting and quality checks are described in several papers, which can be
  found here: <https://www.ncei.noaa.gov/products/world-ocean-atlas>.
Determine the sea area where the fishing boat operates. 
             The latitude and longitude of geographic coordinates are used to match oceanic areas and economic sea areas. 
	    	     You can plot the distribution map with dotplot() function.
		         Please refer to Flanders Marine Institute (2020) <doi:10.14284/403>.
Creating maps for statistical analysis such as proportional circles, choropleth, typology and flows. Some functions use 'shiny' or 'leaflet' technologies for dynamism and interactivity.
	The great features are :
	- Create maps in a web environment where the parameters are modifiable on the fly ('shiny' and 'leaflet' technologies).
	- Create interactive maps through zoom and pop-up ('leaflet' technology).
	- Create frozen maps with the possibility to add labels.
Plotting toolbox for 2D oceanographic data (satellite data, sea surface temperature, chlorophyll, ocean fronts & bathymetry). Recognized classes and formats include netcdf, Raster, '.nc' and '.gz' files.
Functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output.
Calculate ocean wave height summary statistics and process data 
    from bottom-mounted pressure sensor data loggers. Derived primarily from 
    MATLAB functions provided by U. Neumeier at 
    <http://neumeier.perso.ch/matlab/waves.html>. Wave number 
    calculation based on the algorithm in Hunt, J. N. (1979, ISSN:0148-9895) 
    "Direct Solution of Wave Dispersion Equation", American Society of Civil 
    Engineers Journal of the Waterway, Port, Coastal, and Ocean Division, 
    Vol 105, pp 457-459.
Estimates win ratio or Mann-Whitney parameter for two group
 comparisons using ordered composite endpoints with right censoring
 as described in Follmann, Fay, Hamasaki, and Evans (2020)<doi:10.1002/sim.7890>.
Several Oceanographic data sets are provided for use
    by the 'oce' package and for other purposes.
Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. 'ocf' provides forest-based estimation of the 
    conditional choice probabilities and the covariates’ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal 
    and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025)
    <doi:10.1080/07474938.2024.2429596>.
Extends 'flexclust' with an R implementation of order constrained
  solutions in k-means clustering (Steinley and Hubert, 2008, <doi:10.1007/s11336-008-9058-z>).
Provides a function to detect and trim outliers in Gaussian mixture model-based clustering using methods described in Clark and McNicholas (2022) <arXiv:1907.01136>.
Generate and analyze Optimal Channel Networks (OCNs): 
	oriented spanning trees reproducing all scaling features characteristic 
	of real, natural river networks. As such, they can be used in a variety 
	of numerical experiments in the fields of hydrology, ecology and 
	epidemiology. See Carraro et al. (2020) <doi:10.1002/ece3.6479> 
	for a presentation of the package; Rinaldo et al. (2014) 
	<doi:10.1073/pnas.1322700111> for a theoretical overview on the OCN 
	concept; Furrer and Sain (2010) <doi:10.18637/jss.v036.i10> for the 
	construct used.
Implements the Bayesian online changepoint
     detection method by Adams and MacKay (2007) 
     <arXiv:0710.3742> for univariate or multivariate data.
     Gaussian and Poisson probability models are implemented.
      Provides post-processing functions with 
      alternative ways to extract changepoints.
Provides an Interface to Open Collaboration Services 'OCS' (<https://www.open-collaboration-services.org/>) REST API.
Provides functions to access and download data from the 'Open Case Studies' <https://www.opencasestudies.org/> 
    repositories on 'GitHub' <https://github.com/opencasestudies>. Different functions enable 
    users to grab the data they need at different sections in the case study, as well as 
    download the whole case study repository. All the user needs to do is input the name of 
    the case study being worked on. The package relies on the httr::GET() function to access
    files through the 'GitHub' API. The functions usethis::use_zip() and usethis::create_from_github() 
    are used to clone and/or download the case study repositories. To cite an individual case study,
    please see the respective 'README' file at <https://github.com/opencasestudies/>.
    <https://github.com/opencasestudies/ocs-bp-rural-and-urban-obesity> 
    <https://github.com/opencasestudies/ocs-bp-air-pollution>
    <https://github.com/opencasestudies/ocs-bp-vaping-case-study>
    <https://github.com/opencasestudies/ocs-bp-opioid-rural-urban>
    <https://github.com/opencasestudies/ocs-bp-RTC-wrangling>
    <https://github.com/opencasestudies/ocs-bp-RTC-analysis>
    <https://github.com/opencasestudies/ocs-bp-youth-disconnection>
    <https://github.com/opencasestudies/ocs-bp-youth-mental-health>
    <https://github.com/opencasestudies/ocs-bp-school-shootings-dashboard>
    <https://github.com/opencasestudies/ocs-bp-co2-emissions>
    <https://github.com/opencasestudies/ocs-bp-diet>.
Generates n hierarchical clustering hypotheses on subsets of classifiers (usually species in community ecology studies). The n clustering hypotheses are combined to generate a generalized cluster, and computes three metrics of support. 1) The average proportion of elements conforming the group in each of the n clusters (integrity). And 2) the contamination, i.e., the average proportion of elements from other groups that enter a focal group. 3) The probability of existence of the group gives the integrity and contamination in a Bayesian approach.
A database management tool built as a 'shiny' application. Connect to various 
    databases to send queries, upload files, preview tables, and more.
A simple wrapper for the 'Octopus Energy' API
    <https://developer.octopus.energy/docs/api/>. It handles
    authentication, by storing a provided API key and meter details.
    Implemented endpoints include 'products' for viewing tariff details
    and 'consumption' for viewing meter consumption data.
The aim of 'od' is to provide tools and example datasets for
    working with origin-destination ('OD') datasets of the type used to
    describe aggregate urban mobility patterns (Carey et al. 1981)
    <doi:10.1287/trsc.15.1.32>.  The package builds on functions for
    working with 'OD' data in the package 'stplanr', (Lovelace and Ellison
    2018) <doi:10.32614/RJ-2018-053> with a focus on computational
    efficiency and support for the 'sf' class system (Pebesma 2018)
    <doi:10.32614/RJ-2018-009>.  With few dependencies and a simple class
    system based on data frames, the package is intended to facilitate
    efficient analysis of 'OD' datasets and to provide a place for
    developing new functions.  The package enables the creation and
    analysis of geographic entities representing large scale mobility
    patterns, from daily travel between zones in cities to migration
    between countries.
Make querying on 'OData' easier. It exposes an 'ODataQuery' object
    that can be manipulated and provides features such as selection, filtering
    and ordering.
Functions to create, connect, update and query 'HSQL' databases embedded in Open Document Databases files, as 'OpenOffice' and 'LibreOffice' do.
A DBI-compatible interface to ODBC databases.
Download data from Brazil's Origin Destination Surveys. The package covers both data from household travel surveys, dictionaries of variables, and the spatial geometries of surveys conducted in different years and across various urban areas in Brazil. For some cities, the package will include enhanced versions of the data sets with variables "harmonized" across different years.
Anomaly detection in dynamic, temporal networks. The package 
    'oddnet' uses a feature-based method to identify anomalies. First, it computes 
    many features for each network. Then it models the features using time series 
    methods. Using time series residuals it detects anomalies. This way, the 
    temporal dependencies are accounted for when identifying anomalies 
    (Kandanaarachchi, Hyndman 2022) <arXiv:2210.07407>.
Conversion between the most common odds types for sports betting.
    Hong Kong odds, US odds, Decimal odds, Indonesian odds, Malaysian odds, and raw
    Probability are covered in this package.
Computes odds ratios and 95% confidence intervals from a generalized linear model object. It also computes model significance with the chi-squared statistic and p-value and it computes model fit using a contingency table to determine the percent of observations for which the model correctly predicts the value of the outcome. Calculates model sensitivity and specificity.
A utility to quickly obtain clean and tidy sports
    odds from The Odds API <https://the-odds-api.com>.
Uses the outputs of a logistic regression model, from caret <https://CRAN.R-project.org/package=caret>, to build an odds plot.
    This allows for the rapid visualisation of odds plot ratios and works best with the outputs of CARET's GLM model class, by returning the final trained model.
Simplified odds ratio calculation of GAM(M)s &
    GLM(M)s. Provides structured output (data frame) of all predictors and
    their corresponding odds ratios and confident intervals for further
    analyses.  It helps to avoid false references of predictors and
    increments by specifying these parameters in a list instead of using
    'exp(coef(model))' (standard approach of odds ratio calculation for
    GLMs) which just returns a plain numeric output.  For GAM(M)s, odds
    ratio calculation is highly simplified with this package since it
    takes care of the multiple 'predict()' calls of the chosen predictor
    while holding other predictors constant. Also, this package allows
    odds ratio calculation of percentage steps across the whole predictor
    distribution range for GAM(M)s.  In both cases, confident intervals
    are returned additionally. Calculated odds ratio of GAM(M)s can be
    inserted into the smooth function plot.
We proposes a framework that provides real time support for early detection of
    anomalous series within a large collection of streaming time series data. By definition, anomalies
    are rare in comparison to a system's typical behaviour. We define an anomaly as an observation that
    is very unlikely given the forecast distribution. The algorithm first forecasts a boundary for the
    system's typical behaviour using a representative sample of the typical behaviour of the system. An
    approach based on extreme value theory is used for this boundary prediction process. Then a sliding
    window is used to test for anomalous series within the newly arrived collection of series. Feature
    based representation of time series is used as the input to the model. To cope with concept drift,
    the forecast boundary for the system's typical behaviour is updated periodically.  More details
    regarding the algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al.
    (2019) <doi:10.1080/10618600.2019.1617160>.
Allows performing forwards prediction for the General Unified 
          Threshold model of Survival using compiled ode code. This package 
          was created to avoid dependency with the 'morse' package that requires 
          the installation of 'JAGS'. This package is based on functions from 
          the 'morse' package v3.3.1: Virgile Baudrot, Sandrine Charles, 
          Marie Laure Delignette-Muller, Wandrille Duchemin, Benoit Goussen, 
          Nils Kehrein, Guillaume Kon-Kam-King, Christelle Lopes, Philippe Ruiz, 
          Alexander Singer and Philippe Veber (2021) <https://CRAN.R-project.org/package=morse>.
Simulates a network of ordinary differential equations of order
    two. The package provides an easy interface to construct networks. In addition
    you are able to define different external triggers to manipulate the trajectory.
    The method is described by Surmann, Ligges, and Weihs (2014) <doi:10.1109/ENERGYCON.2014.6850482>.
Performs sensitivity analysis in ordinary differential equation (ode) models.
    The package utilize the ode interface from 'deSolve' and connects it with the 
    sensitivity analysis from 'sensitivity'. Additionally we add a method to
    run the sensitivity analysis on variables with class 'ODEnetwork'. A detailed
    plotting function provides outputs on the calculations.
    The method is described by Weber, Theers, Surmann, Ligges, and Weihs (2018) <doi:10.17877/DE290R-18874>.
An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 <ISBN 0-387-24435-2>). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants.
Generate systems of ordinary differential equations
    (ODE) and integrate them, using a domain specific language
    (DSL).  The DSL uses R's syntax, but compiles to C in order to
    efficiently solve the system.  A solver is not provided, but
    instead interfaces to the packages 'deSolve' and 'dde' are
    generated.  With these, while solving the differential equations,
    no allocations are done and the calculations remain entirely in
    compiled code.  Alternatively, a model can be transpiled to R for
    use in contexts where a C compiler is not present.  After
    compilation, models can be inspected to return information about
    parameters and outputs, or intermediate values after calculations.
    'odin' is not targeted at any particular domain and is suitable
    for any system that can be expressed primarily as mathematical
    expressions.  Additional support is provided for working with
    delays (delay differential equations, DDE), using interpolated
    functions during interpolation, and for integrating quantities
    that represent arrays.
After develop a 'ODK' <https://opendatakit.org/> frame, we can link the frame to 'Google Sheets' <https://www.google.com/sheets/about/> and collect data through 'Android' <https://www.android.com/>. This data uploaded to a 'Google sheets'. odk2spss() function help to convert the 'odk' frame into 'SPSS' <https://www.ibm.com/analytics/us/en/technology/spss/> frame. Also able to add downloaded 'Google sheets' data or read data from 'Google sheets' by using 'ODK' frame 'submission_url'.
OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. 
    In the first layer of the hierarchy, the clusters are separated automatically based on the variation 
    of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy 
    corresponds to the sub clustering process of small clusters based on the distance between the origin 
    and destination of each cluster.
Calculate the optimal sample size allocation that 
    produces the highest statistical power for experimental 
    studies under a budget constraint, and
    perform power analyses with and without accommodating 
    cost structures of sampling. The designs cover single-level 
    and multilevel experiments detecting main, mediation, 
    and moderation effects (and some combinations). 
    The references for the proposed methods include: 
    (1) Shen, Z., & Kelcey, B. (2020). 
    Optimal sample allocation under unequal costs in 
    cluster-randomized trials.
    Journal of Educational and Behavioral Statistics, 45(4): 446-474.
        <doi:10.3102/1076998620912418>.
    (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for
    three-level multisite cluster-randomized trials. 
    Journal of Research on Educational Effectiveness, 15 (1), 130-150.
    <doi:10.1080/19345747.2021.1953200>.
    (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in
    multisite randomized trials. The Journal of Experimental Education.
    <doi:10.1080/00220973.2020.1830361>.
    (4) Champely, S. (2020). pwr: Basic functions for power analysis 
    (Version 1.3-0) [Software]. Available from 
    <https://CRAN.R-project.org/package=pwr>.
The oblique decision tree (ODT) uses linear combinations of
    predictors as partitioning variables in a decision tree. Oblique
    Decision Random Forest (ODRF) is an ensemble of multiple ODTs
    generated by feature bagging. Both can be used for classification and
    regression as supplements to the classical CART of Breiman (1984)
    <DOI:10.1201/9781315139470> and Random Forest of Breiman (2001)
    <DOI:10.1023/A:1010933404324> respectively.
Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency. 
    In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome 
    variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event 
    outcome, and continuous outcome ODS design (Zhou et al. 2002) <doi: 10.1111/j.0006-341X.2002.00413.x>. 
    Because ODS data has biased sampling nature, standard statistical analysis such as linear regression 
    will lead to biases estimates of the population parameters. This package implements four statistical 
    methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous 
    outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial 
    linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011) 
    <doi: 10.1111/j.1541-0420.2010.01500.x>. (3) Analyze a secondary outcome in continuous ODS design 
    (Pan et al. 2018) <doi: 10.1002/sim.7672>. (4) An estimated likelihood method analyzing a secondary 
    outcome in case-cohort data (Pan et al. 2017) <doi: 10.1111/biom.12838>.
Implements a tree-based method specifically designed for personalized medicine applications. By using genomic and mutational data, 'ODT' efficiently identifies optimal drug recommendations tailored to individual patient profiles. The 'ODT' algorithm constructs decision trees that bifurcate at each node, selecting the most relevant markers (discrete or continuous) and corresponding treatments, thus ensuring that recommendations are both personalized and statistically robust. This iterative approach enhances therapeutic decision-making by refining treatment suggestions until a predefined group size is achieved. Moreover, the simplicity and interpretability of the resulting trees make the method accessible to healthcare professionals. Includes functions for training the decision tree, making predictions on new samples or patients, and visualizing the resulting tree. For detailed insights into the methodology, please refer to Gimeno et al. (2023) <doi:10.1093/bib/bbad200>.
Search and extract data from the Organization for Economic 
    Cooperation and Development (OECD).
Package for estimating the parameters of a nonlinear function using iterated linearization via Taylor series.  Method is based on  Kubáček (2000) ISBN: 80-244-0093-6. The algorithm is a generalization of the procedure given in Köning, R., Wimmer, G.
             and Witkovský, V. (2014) <doi:10.1088/0957-0233/25/11/115001>.
Some general helper functions that I (and maybe others) find useful 
    when developing data science software.
Solves penalized least squares problems for big tall data
    using the orthogonalizing EM algorithm of Xiong et al. (2016) 
    <doi:10.1080/00401706.2015.1054436>. The main fitting function is oem() and the
    functions cv.oem() and xval.oem() are for cross validation, the latter being an
    accelerated cross validation function for linear models. The big.oem() function
    allows for out of memory fitting. A description of the underlying methods and 
    code interface is described in Huling and Chien (2022) <doi:10.18637/jss.v104.i06>.
Tools to access data from the data web service of the Oesterreichische Nationalbank (OeNB), <https://www.oenb.at/en/Statistics/User-Defined-Tables/webservice.html>.
Developed to help researchers who need to model the kinetics of carbon dioxide (CO2) production in alcoholic fermentation of wines, beers and other fermented products. The following models are available for modeling the carbon dioxide production curve as a function of time: 5PL, Gompertz and 4PL. This package has different functions, which applied can: perform the modeling of the data obtained in the fermentation and return the coefficients, analyze the model fit and return different statistical metrics, and calculate the kinetic parameters: Maximum production of carbon dioxide; Maximum rate of production of carbon dioxide; Moment in which maximum fermentation rate occurs; Duration of the latency phase for carbon dioxide production; Carbon dioxide produced until maximum fermentation rate occurs. In addition, a function that generates graphs with the observed and predicted data from the models, isolated and combined, is available. Gava, A., Borsato, D., & Ficagna, E. (2020)."Effect of mixture of fining agents on the fermentation kinetics of base wine for sparkling wine production: Use of methodology for modeling". <doi:10.1016/j.lwt.2020.109660>.
Methods for statistical analysis and reporting preferred by the US Office of Evaluation Sciences (OES). This package prepares data from standard model output objects (such as from \code{lm()} and \code{estimatr::lm_robust()}) and creates visualizations of treatment effects from the prepared quantities, according to the standards of the US Office of Evaluation Sciences.
Contains data from the May 2020 Occupational Employment and 
    Wage Statistics data release from the U.S. Bureau of Labor Statistics. The 
    dataset covers employment and wages across occupations, industries, states,  
    and at the national level. Metropolitan data is not included.
Contains data from the May 2021 Occupational Employment and 
    Wage Statistics data release from the U.S. Bureau of Labor Statistics. The 
    dataset covers employment and wages across occupations, industries, states,  
    and at the national level. Metropolitan data is not included.
Allows production of 'Microsoft' corporate documents from 'R Markdown' by 
 reusing formatting defined in 'Microsoft Word' documents. You can reuse table styles, 
 list styles but also add column sections, landscape oriented pages. Table and image 
 captions as well as cross-references are transformed into 'Microsoft Word' fields, 
 allowing documents edition and merging without issue with references; the syntax 
 conforms to the 'bookdown' cross-reference definition. Objects generated by 
 the 'officer' package are also supported in the 'knitr' chunks. 
 'Microsoft PowerPoint' presentations also benefit from this as well as the 
 ability to produce editable vector graphics in 'PowerPoint' and also to 
 define placeholder where content is to be added.
Access and manipulate 'Microsoft Word', 'RTF' and 'Microsoft
    PowerPoint' documents from R.  The package focuses on tabular and
    graphical reporting from R; it also provides two functions that let
    users get document content into data objects. A set of functions lets
    add and remove images, tables and paragraphs of text in new or
    existing documents.  The package does not require any installation of
    Microsoft products to be able to write Microsoft files.
Detect the number and locations of change points. The locations can be either exact or in terms of ranges, 
            depending on the available computational resource. The method is based on Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh (2017) <doi:10.1109/TSP.2017.2711558>.
Extend the 'tidymodels' ecosystem <https://www.tidymodels.org/> to
    enable the creation of predictive models with offset terms. Models with 
    offsets are most useful when working with count data or when fitting an
    adjustment model on top of an existing model with a prior expectation. 
    The former situation is common in insurance where data is often weighted by
    exposures. The latter is common in life insurance where industry mortality 
    tables are often used as a starting point for setting assumptions.
Offers a gene-based meta-analysis test with filtering to detect gene-environment interactions (GxE) with association data, proposed by Wang et al. (2018) <doi:10.1002/gepi.22115>. It first conducts a meta-filtering test to filter out unpromising SNPs by combining all samples in the consortia data. It then runs a test of omnibus-filtering-based GxE meta-analysis (ofGEM) that combines the strengths of the fixed- and random-effects meta-analysis with meta-filtering. It can also analyze data from multiple ethnic groups.
A comprehensive system for designing and implementing on-farm precision field agronomic trials. You provide field data, tell 'ofpetrial' how to design a trial, and get readily-usable trial design files and a report checks the validity and reliability of the trial design.
Consider a data matrix of n individuals with p variates. The objective general index (OGI)
    is a general index that combines the p variates into a univariate index in order to rank the n
    individuals. The OGI is always positively correlated with each of the variates.
    More details can be found in Sei (2016) <doi:10.1016/j.jmva.2016.02.005>.
Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased (Weiss (1997)). This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects. Wrapper functions to estimate the standard limited dependent variable models are also included.
Multiple tools are now available for inferring the personalised 
    germ line set from an adaptive immune receptor repertoire. 
    Output from these tools is converted to 
    a single format and supplemented with rich data such as usage and 
    characterisation of 'novel' germ line alleles. This data can be 
    particularly useful when considering the validity of novel inferences. Use
    of the analysis provided is described in <doi:10.3389/fimmu.2019.00435>.
Prepares census and core housing needs data, specifically designed for use with Statistics Canada data and standardized input data. The package offers functions for tidying, organizing, and splitting complex data tables, making it easier for users to perform analyses on the data. 'OHCSpackage''' is particularly useful for those working with census data that has a consistent format, number of rows, and number of columns. With this package, users can save time and streamline their data preparation processes.
Users can build a single 'shiny' app for exploring population characterization, population-level causal effect estimation, and patient-level prediction results generated via the R analyses packages in 'HADES' (see <https://ohdsi.github.io/Hades/>). Learn more about 'OhdsiShinyAppBuilder' at <https://ohdsi.github.io/OhdsiShinyAppBuilder/>.
Supports the modeling of ordinal random variables, 
    like the outcomes of races, via Softmax regression,
    under the Harville <doi:10.1080/01621459.1973.10482425> and
    Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.
Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.
Inference using a class of Hidden Markov models
    (HMMs) called 'oHMMed'(ordered HMM with emission densities 
    <doi:10.1186/s12859-024-05751-4>): The 'oHMMed' algorithms identify 
    the number of comparably homogeneous regions within observed sequences
    with autocorrelation patterns. These are modelled as discrete hidden
    states; the observed data points are then realisations of continuous
    probability distributions with state-specific means that enable
    ordering of these distributions. The observed sequence is labelled
    according to the hidden states, permitting only neighbouring states
    that are also neighbours within the ordering of their associated
    distributions. The parameters that characterise these state-specific
    distributions are then inferred. Relevant for application to genomic
    sequences, time series, or any other sequence data with serial
    autocorrelation.
Perform a Bayesian estimation of the ordinal exploratory 
    Higher-order General Diagnostic Model (OHOEGDM) for Polytomous Data 
    described by Culpepper, S. A. and Balamuta, J. J. (In Press) <doi:10.1080/00273171.2021.1985949>.
Ordered homogeneity pursuit lasso (OHPL)
    algorithm for group variable selection proposed in Lin et al. (2017)
    <DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the
    homogeneity structure in high-dimensional data and enjoys the
    grouping effect to select groups of important variables
    automatically. This feature makes it particularly useful for
    high-dimensional datasets with strongly correlated variables,
    such as spectroscopic data.
A client that grants access to the power of the 'ohsome API'
    from R. It lets you analyze the rich data source of the 
    'OpenStreetMap (OSM)' history. You can retrieve the geometry of 'OSM' 
    data at specific points in time, and you can get aggregated statistics 
    on the evolution of 'OSM' elements and specify your own temporal, 
    spatial and/or thematic filters.
Calculate's Tomoka Ohta's partitioning of linkage disequilibrium,
 deemed D-statistics, for pairs of loci. Petrowski et al. (2019) <doi:10.5334/jors.250>.
Facilitates the automatic detection of acoustic signals, 
    providing functions to diagnose and optimize the performance of detection 
    routines. Detections from other software can also be explored and optimized. 
    This package has been peer-reviewed by rOpenSci.
    Araya-Salas et al. (2022) <doi:10.1101/2022.12.13.520253>.
Provides simple crosstab output with optional statistics (e.g., Goodman-Kruskal Gamma, Somers' d, and Kendall's tau-b) as well as two-way and one-way tables. The package is used within the statistics component of the Masters of Science (MSc) in Social Science of the Internet at the Oxford Internet Institute (OII), University of Oxford, but the functions should be useful for general data analysis and especially for analysis of categorical and ordinal data.
Crawler for 'OJS' pages and scraper for meta-data from articles. 
    You can crawl 'OJS' archives, issues, articles, galleys, and search results. 
    You can scrape articles metadata from their head tag in html, 
    or from Open Archives Initiative ('OAI') records.
    Most of these functions rely on 'OJS' routing conventions 
    (<https://docs.pkp.sfu.ca/dev/documentation/en/architecture-routes>).
Optimal k Nearest Neighbours Ensemble is an ensemble of base k nearest neighbour models each constructed on a bootstrap sample with a random subset of features. k closest observations are identified for a test point "x" (say), in each base k nearest neighbour model to fit a stepwise regression to predict the output value of "x". The final predicted value of "x" is the mean of estimates given by all the models. The implemented model takes training and test datasets and trains the model on training data to predict the test data. Ali, A., Hamraz, M., Kumam, P., Khan, D.M., Khalil, U., Sulaiman, M. and Khan, Z. (2020) <DOI:10.1109/ACCESS.2020.3010099>.
An unofficial wrapper for 'okx exchange v5' API <https://www.okx.com/docs-v5/en/>, including 'REST' API and 'WebSocket' API.
We provide two algorithms for monitoring change points with online matrix-valued time series, under the assumption of a two-way factor structure. The algorithms are based on different calculations of the second moment matrices. One is based on stacking the columns of matrix observations, while another is by a more delicate projected approach. A well-known fact is that, in the presence of a change point, a factor model can be rewritten as a model with a larger number of common factors. In turn, this entails that, in the presence of a change point, the number of spiked eigenvalues in the second moment matrix of the data increases. Based on this, we propose two families of procedures - one based on the fluctuations of partial sums, and one based on extreme value theory - to monitor whether the first non-spiked eigenvalue diverges after a point in time in the monitoring horizon, thereby indicating the presence of a change point. This package also provides some simple functions for detecting and removing outliers, imputing missing entries and testing moments. See more details in He et al. (2021)<doi:10.48550/arXiv.2112.13479>.
'Open Location Codes' <http://openlocationcode.com/>
    are a Google-created standard for identifying geographic locations. 'olctools' provides
    utilities for validating, encoding and decoding entries that follow this
    standard.
An implementation of the Rapid Assessment Method for Older People
    or RAM-OP <https://www.helpage.org/resource/rapid-assessment-method-for-older-people-ramop-manual/>.
    It provides various functions that allow the user to design and plan the
    assessment and analyse the collected data. RAM-OP provides accurate and
    reliable estimates of the needs of older people.
A collection of functions to facilitate analysis of proteomic
    data from Olink, primarily NPX data that has been exported from Olink
    Software. The functions also work on QUANT data from
    Olink by log- transforming the QUANT data. The functions are focused
    on reading data, facilitating data wrangling and quality control
    analysis, performing statistical analysis and generating figures to
    visualize the results of the statistical analysis. The goal of this
    package is to help users extract biological insights from proteomic
    data run on the Olink platform.
An interface to easily run local language models with 'Ollama' <https://ollama.com> server
    and API endpoints (see <https://github.com/ollama/ollama/blob/main/docs/api.md> for details). It lets 
    you run open-source large language models locally on your machine.
Computes the pdf, cdf, quantile function, hazard function and generating random numbers for Odd log-logistic family (OLL-G). This family have been developed by different authors in the recent years. See Alizadeh (2019) <doi:10.31801/cfsuasmas.542988> for example.
Density, distribution function, quantile function and random generation for the Odd Log-Logistic Generalized Gamma proposed in Prataviera, F. et al (2017) <doi:10.1080/00949655.2016.1238088>.
The optimal linear regression olr(), runs all the possible combinations of linear regression
  equations. The olr() returns the equation which has the greatest adjusted R-squared term or the greatest R-squared term based
  on the user's discretion. Essentially, the olr() returns the best fit equation out of all the possible equations. R-squared increases 
  with the addition of an explanatory variable whether it is 'significant' or not, thus this was developed to eliminate that conundrum. 
  Adjusted R-squared is preferred to overcome this phenomenon, but each combination will still produce different results and this will
  return the best one. Complimentary functions are included which list all of the equations, all of the equations in ascending order, 
  a function to give the user a specific model's summary, and the list of adjusted R-squared terms & R-squared terms.
  A 'Python' version is available at: <https://pypi.org/project/olr/>.
Tools designed to make it easier for users, particularly beginner/intermediate R users 
    to build ordinary least squares regression models. Includes comprehensive regression output, 
    heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, 
    model fit assessment and variable selection procedures.
The 'OLStrajr' package provides comprehensive functions for ordinary
    least squares (OLS) trajectory analysis and case-by-case OLS regression as
    outlined in Carrig, Wirth, and Curran (2004) <doi:10.1207/S15328007SEM1101_9>
    and Rogosa and Saner (1995) <doi:10.3102/10769986020002149>. It encompasses two
    primary functions, OLStraj() and cbc_lm(). The OLStraj() function simplifies
    the estimation of individual growth curves over time via OLS regression, with
    options for visualizing both group-level and individual-level growth trajectories
    and support for linear and quadratic models. The cbc_lm() function facilitates
    case-by-case OLS estimates and provides unbiased mean population intercept and
    slope estimators by averaging OLS intercepts and slopes across cases. It further
    offers standard error calculations across bootstrap replicates and computation
    of 95% confidence intervals based on empirical distributions from the resampling
    processes.
'Shiny' Application to visualize Olympic Data. From 1896 to
    2016. Even Winter Olympics events are included. Data is from Kaggle at
    <https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results>.
It is a computer tool to estimate the item-sum score's reliability (composite reliability, CR) in multidimensional scales with overlapping items. An item that measures more than one domain construct is called an overlapping item. 
    The estimation is based on factor models allowing unlimited cross-factor loadings such as exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM). The factor models include correlated-factor models and bi-factor models. Specifically for bi-factor models, a type of hierarchical factor model, the package estimates the CR hierarchical subscale/hierarchy and CR subscale/scale total. The CR estimator 'Omega-generic' was proposed by Mai, Srivastava, and Krull (2021) <https://whova.com/embedded/subsession/enars_202103/1450751/1452993/>. The current version can only handle continuous data. 
    Yujiao Mai contributes to the algorithms, R programming, and application example. Deo Kumar Srivastava contributes to the algorithms and the application example. Kevin R. Krull contributes to the application example. The package 'OmegaG' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of 'OmegaG' do not necessarily represent the policy of the ALSAC.
It provides functions to generate a correlation matrix
    from a genetic dataset and to use this matrix to predict the phenotype of an
    individual by using the phenotypes of the remaining individuals through
    kriging. Kriging is a geostatistical method for optimal prediction or best
    unbiased linear prediction. It consists of predicting the value of a
    variable at an unobserved location as a weighted sum of the variable at
    observed locations. Intuitively, it works as a reverse linear regression:
    instead of computing correlation (univariate regression coefficients are
    simply scaled correlation) between a dependent variable Y and independent
    variables X, it uses known correlation between X and Y to predict Y.
A tool for interactive exploration of the results from 'omics'
  experiments to facilitate novel discoveries from high-throughput biology. The
  software includes R functions for the 'bioinformatician' to deposit study
  metadata and the outputs from statistical analyses (e.g. differential
  expression, enrichment). These results are then exported to an interactive
  JavaScript dashboard that can be interrogated on the user's local machine or
  deployed online to be explored by collaborators. The dashboard includes
  'sortable' tables, interactive plots including network visualization, and
  fine-grained filtering based on statistical significance.
A collection of functions to analyse '--omics' datasets such as DNA
    methylation and gene expression profiles.
A method for the quantitative prediction using omics data. 
    This package provides functions to construct the quantitative prediction 
    model using omics data.
Performs the O2PLS data integration method for two datasets, yielding joint and data-specific parts for each dataset.
    The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data.
    It provides a rigorous and a faster alternative cross-validation method to select the number of components,
    as well as functions to report proportions of explained variation and to construct plots of the results. 
    See the software article by el Bouhaddani et al (2018) <doi:10.1186/s12859-018-2371-3>, 
    and Trygg and Wold (2003) <doi:10.1002/cem.775>.
    It also performs Sparse Group (Penalized) O2PLS, see Gu et al (2020) <doi:10.1186/s12859-021-03958-3> and cross-validation for the degree of sparsity.
A method that analyzes quality control metrics from multi-sample genomic sequencing studies and nominates poor quality samples for exclusion. Per sample quality control data are transformed into z-scores and aggregated. The distribution of aggregated z-scores are modelled using parametric distributions. The parameters of the optimal model, selected either by goodness-of-fit statistics or user-designation, are used for outlier nomination. Two implementations of the Cosine Similarity Outlier Detection algorithm are provided with flexible parameters for dataset customization.
Processing and analyzing omics data from genomics, transcriptomics, proteomics, and metabolomics platforms. It provides functions for preprocessing, normalization, visualization, and statistical analysis, as well as machine learning algorithms for predictive modeling. 'omicsTools' is an essential tool for researchers working with high-throughput omics data in fields such as biology, bioinformatics, and medicine.The QC-RLSC (quality control–based robust LOESS signal correction) algorithm is used for normalization. Dunn et al. (2011) <doi:10.1038/nprot.2011.335>.
In bulk epigenome/transcriptome experiments, molecular expression
    is measured in a tissue, which is a mixture of multiple types of cells.
    This package tests association of a disease/phenotype with a molecular marker
    for each cell type.
    The proportion of cell types in each sample needs to be given as input.
    The package is applicable to epigenome-wide association study (EWAS) and
    differential gene expression analysis.
    Takeuchi and Kato (submitted)
    "omicwas: cell-type-specific epigenome-wide and transcriptome association study".
Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) <doi:10.1037/1082-989X.12.4.414>.
An assortment of helper functions for managing data (e.g.,
	rotating values in matrices by a user-defined angle, switching from
	row- to column-indexing), dates (e.g., intuiting year from messy date
	strings), handling missing values (e.g., removing elements/rows across
	multiple vectors or matrices if any have an NA), text (e.g.,
	flushing reports to the console in real-time); and combining data frames
	with different schema (copying, filling, or concatenating columns or
	applying functions before combining).
The separate p-values of SNPs, RNA expressions and DNA
    methylations are calculated by KM regression. The correlation between
    different omics data are taken into account. This method can be applied to
    either samples with all three types of omics data or samples with two types.
Creates mock data for testing and package development for the
    Observational Medical Outcomes Partnership common data model. The
    package offers functions crafted with pipeline-friendly
    implementation, enabling users to effortlessly include only the
    necessary tables for their testing needs.
Provides definitions of core classes and methods used by analytic 
    pipelines that query the OMOP (Observational Medical Outcomes Partnership) 
    common data model.
Summarises key information in data mapped to the Observational 
    Medical Outcomes Partnership (OMOP) common data model. Assess suitability to 
    perform specific epidemiological studies and explore the different domains
    to obtain feasibility counts and trends.
Visualise results obtained from analysing data mapped to the 
    Observational Medical Outcomes Partnership (OMOP) common data model using
    'shiny' applications.
Model mixed integer linear programs in an algebraic way directly in R.
             The model is solver-independent and thus offers the possibility
             to solve a model with different solvers. It currently only supports
             linear constraints and objective functions. See the 'ompr'
             website <https://dirkschumacher.github.io/ompr/> for more information,
             documentation and examples.
A solver for 'ompr' based on the R Optimization Infrastructure ('ROI').
  The package makes all solvers in 'ROI' available to solve 'ompr' models. Please see the
  'ompr' website <https://dirkschumacher.github.io/ompr/> and package docs for more information
  and examples on how to use it.
Build 'SVG' components using element-based functions. With
    an 'svg' object, we can modify its graphical elements with a suite of
    transform functions.
Facilitates the creation of intuitive figures to describe metabolomics data by utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) hierarchy data, and gathers functional orthology and gene data from the KEGG-REST API.
Data sets for network analysis related to People Analytics.  
  Contains various data sets from the book 'Handbook of Graphs and Networks in People Analytics' 
  by Keith McNulty (2021).
Implementation of a likelihood ratio test of differential
    onset of senescence between two groups. Given two groups with
    measures of age and of an individual trait likely to be subjected to
    senescence (e.g. body mass), 'OnAge' provides an asymptotic p-value
    for the null hypothesis that senescence starts at the same age in
    both groups. The package implements the procedure used in 
    Douhard et al. (2017) <doi:10.1111/oik.04421>.
A database containing the names 
  of the babies born in Ontario between 1917 and 2018. 
  Counts of fewer than 5 names were suppressed for privacy.
Provides a wrapper for the Onboard Data building data API <https://api.onboarddata.io/swagger>. Along with streamlining access to the API, this package simplifies access to sensor time series data, metadata (sensors, equipment, and buildings), and details about the Onboard data model/ontology.
Automated reporting in Word and PowerPoint can require customization for each organizational template. This package works around this by adding standard reporting functions and an abstraction layer to facilitate automated reporting workflows that can be replicated across different organizational templates.
Allows users to discover and retrieve Ocean Networks Canada's oceanographic data in raw, text, image, audio, video or any other format available. Provides a class that wraps web service calls and business logic so that users can download data with a single line of code.
Allows you to easily execute expensive compute operations only once, and save the resulting object to disk.
Medication adherence, defined as medication-taking behavior that aligns with the agreed-upon 
    treatment protocol, is critical for realizing the benefits of prescription medications. 
    Medication adherence can be assessed using electronic adherence monitoring devices (EAMDs), 
    pill bottles or boxes that contain a computer chip that records the date and time of each 
    opening (or “actuation”). Before researchers can use EAMD data, they must apply a series of 
    decision rules to transform actuation data into adherence data. 
    The purpose of this R package ('oncmap') is to transform EAMD actuations in the form of a raw .csv file, 
    information about the patient, regimen, and non-monitored periods into two daily adherence values -- 
    Dose Taken and Correct Dose Taken.
Bayesian logistic regression model with optional
  EXchangeability-NonEXchangeability parameter modelling for flexible
  borrowing from historical or concurrent data-sources. The safety model
  can guide dose-escalation decisions for adaptive oncology Phase I
  dose-escalation trials which involve an arbitrary number of
  drugs. Please refer to Neuenschwander et al. (2008)
  <doi:10.1002/sim.3230> and Neuenschwander et al. (2016)
  <doi:10.1080/19466315.2016.1174149> for details on the methodology.
Offers a rich collection of data focused on cancer research, covering survival rates, genetic studies, biomarkers, and epidemiological insights. 
    Designed for researchers, analysts, and bioinformatics practitioners, the package includes datasets on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer, among others. 
    It aims to facilitate advanced research, analysis, and understanding of cancer epidemiology, genetics, and treatment outcomes.
Aids in the analysis of genes influencing cancer survival by including a principal function, calculator(), which calculates the P-value for each provided gene under the optimal cutoff in cancer survival studies. Grounded in methodologies from significant works, this package references Therneau's 'survival' package (Therneau, 2024; <https://CRAN.R-project.org/package=survival>) and the survival analysis extensions by Therneau and Grambsch (2000, ISBN 0-387-98784-3). It also integrates the 'survminer' package by Kassambara et al. (2021; <https://CRAN.R-project.org/package=survminer>), enhancing survival curve visualizations with 'ggplot2'.
Implements methods to fit a parametric Bayesian multi-state model
    to tumor response data.
    The model can be used to sample from the predictive distribution to impute
    missing data and calculate probability of success for custom decision
    criteria in early clinical trials during an ongoing trial.
    The inference is implemented using 'stan'.
Allows for building drug response models using screening data between bulk RNA-Seq and a drug response metric and two additional tools for biomarker discovery that have been developed by the Huang Laboratory at University of Minnesota. 
    There are 3 main functions within this package. 
    (1) calcPhenotype is used to build drug response models on RNA-Seq data and impute them on any other RNA-Seq dataset given to the model. 
    (2) GLDS is used to calculate the general level of drug sensitivity, which can improve biomarker discovery.
    (3) IDWAS can take the results from calcPhenotype and link the imputed response back to available genomic (mutation and CNV alterations) to identify biomarkers. 
    Each of these functions comes from a paper from the Huang research laboratory. Below gives the relevant paper for each function. 
    calcPhenotype - Geeleher et al, Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines.
    GLDS - Geeleher et al, Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models.
    IDWAS -  Geeleher et al, Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.
Provide functionality for cancer subtyping using nearest centroids or machine learning methods based on TCGA data.
Construct and evaluate directed tree structures that
   model the process of occurrence of genetic alterations during carcinogenesis
   as described in Szabo, A. and Boucher, K (2002)  <doi:10.1016/S0025-5564(02)00086-X>.
Measures different aspects of page content, structure and performance for SEO (Search Engine Optimization).
    Aspects covered include HTML tags used in SEO, duplicate and near-duplicate content, structured data, on-site linking structure and popularity transfer, and many other amazing things. 
    This package can be used to generate a real, full SEO audit report, which serves to detect errors or inefficiencies on a page that can be corrected in order to optimise its performance on search engines.
Designed to enhance data validation and management processes by employing a set of functions that read a set of rules from a 'CSV' or 'Excel' file and apply them to a dataset. Funded by the National Renewable Energy Laboratory and Possibility Lab, maintained by the Moore Institute for Plastic Pollution Research.
Two-stage design for single-arm phase II 
		trials with time-to-event endpoints (e.g., clinical trials on 
		immunotherapies among cancer patients) can be calculated using this package. 
		Two notable advantages of the package: 1) It provides flexible choices from 
		three design methods (optimal, minmax, and admissible), and 2) the power of
		the design is more accurately calculated using the exact variance in the
		one-sample log-rank test. The package can be used for 1) planning the sample
		sizes and other design parameters, and 2) conducting the interim and final
		analyses for the Go/No-go decisions. More details about the design method
		can be found in: Wu, J, Chen L, Wei J, Weiss H, Chauhan A. (2020).
		<doi:10.1002/pst.1983>.
Get operating characteristics of one-arm clinical trial designs for time-to-event endpoint through simulation and perform analysis with time-to-event data.
Maximum homogeneity clustering algorithm for one-dimensional data
    described in W. D. Fisher (1958) <doi:10.1080/01621459.1958.10501479>
    via dynamic programming.
Quickly create numeric matrices for machine learning algorithms
    that require them. It converts factor columns into onehot vectors.
Estimates one-inflated positive Poisson (OIPP) and
    one-inflated zero-truncated negative binomial (OIZTNB) regression
    models. A suite of ancillary statistical tools are also provided,
    including: estimation of positive Poisson (PP) and zero-truncated
    negative binomial (ZTNB) models; marginal effects and their standard
    errors; diagnostic likelihood ratio and Wald tests; plotting;
    predicted counts and expected responses; and random variate
    generation. The models and tools, as well as four applications, are
    shown in Godwin, R. T. (2024). "One-inflated zero-truncated count
    regression models" arXiv preprint <doi:10.48550/arXiv.2402.02272>.
The identity provider ['OneLogin']<http://onelogin.com> is used for 
  authentication via Single Sign On (SSO). This package provides an R interface to their API.
Implements the One Rule (OneR) Machine Learning classification algorithm (Holte, R.C. (1993) <doi:10.1023/A:1022631118932>) with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.
The log-rank test is performed to assess the survival outcomes between two group. 
             When there is no proper control group or obtaining such data is cumbersome, one sample
             log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003)<doi:10.1093/jnci/djt227> and variation of the 
             test for small sample sizes which is detailed in FD Liddell (1984)<doi:10.1136/jech.38.1.85> paper. Visualization function in the package
             generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest.
Useful functions for one-sample (individual level data)
    Mendelian randomization and instrumental variable analyses. The
    package includes implementations of; the Sanderson and Windmeijer
    (2016) <doi:10.1016/j.jeconom.2015.06.004> conditional F-statistic,
    the multiplicative structural mean model Hernán and Robins (2006)
    <doi:10.1097/01.ede.0000222409.00878.37>, and two-stage predictor
    substitution and two-stage residual inclusion estimators explained by
    Terza et al. (2008) <doi:10.1016/j.jhealeco.2007.09.009>.
This ONEST software implements the method of assessing the pathologist agreement in reading PD-L1 assays (Reisenbichler et al. (2020 <doi:10.1038/s41379-020-0544-x>)), to determine the minimum number of evaluators needed to estimate agreement involving a large number of raters. Input to the program should be binary(1/0) pathology data, where “0” may stand for negative and “1” for positive. Additional examples were given using the data from Rimm et al. (2017 <doi:10.1001/jamaoncol.2017.0013>).
Provide principally an eponymic function that numerically computes 
  the Le Cam's one-step estimator for an independent and identically distributed sample. 
  One-step estimation is asymptotically 
  efficient (see L. Le Cam (1956) <https://projecteuclid.org/euclid.bsmsp/1200501652>) 
  and can be computed faster than the maximum likelihood estimator for large observation 
  samples, see e.g. Brouste et al. (2021) <doi:10.32614/RJ-2021-044>.
Allows code to be run only once on a given computer, using
  lockfiles. Typical use cases include startup messages shown only when a
  package is loaded for the very first time.
We introduce an R function
        one_two_sample() which can deal with one and two (normal)
        samples, Ying-Ying Zhang, Yi Wei (2012) <doi:10.2991/asshm-13.2013.29>. 
        For one normal sample x, the function reports
        descriptive statistics, plot, interval estimation and test of
        hypothesis of x. For two normal samples x and y, the function
        reports descriptive statistics, plot, interval estimation and
        test of hypothesis of x and y, respectively. It also reports
        interval estimation and test of hypothesis of mu1-mu2 (the
        difference of the means of x and y) and sigma1^2 / sigma2^2
        (the ratio of the variances of x and y), tests whether x and y
        are from the same population, finds the correlation coefficient
        of x and y if x and y have the same length.
Performs one-way tests in independent groups designs including homoscedastic and heteroscedastic tests. These are one-way analysis of variance (ANOVA), Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander-Govern test, James second order test, Kruskal-Wallis test, Scott-Smith test, Box F test, Johansen F test, Generalized tests equivalent to Parametric Bootstrap and Fiducial tests, Alvandi's F test, Alvandi's generalized p-value, approximate F test, B square test, Cochran test, Weerahandi's generalized F test, modified Brown-Forsythe test, adjusted Welch's heteroscedastic F test, Welch-Aspin test, Permutation F test. The package performs pairwise comparisons and graphical approaches. Also, the package includes Student's t test, Welch's t test and Mann-Whitney U test for two samples. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots (Dag et al., 2018, <https://journal.r-project.org/archive/2018/RJ-2018-022/RJ-2018-022.pdf>).
Quaternions and Octonions are four- and eight- dimensional
  extensions of the complex numbers.  They are normed division
  algebras over the real numbers and find applications in spatial
  rotations (quaternions), and string theory and relativity
  (octonions).  The quaternions are noncommutative and the octonions
  nonassociative.  See the package vignette for more details.
It implements the online Bayesian methods for change point analysis. It can
            also perform missing data imputation with methods from 'VIM'. The reference 
            is Yigiter A, Chen J, An L, Danacioglu N (2015) <doi:10.1080/02664763.2014.1001330>. 
            The link to the package is <https://CRAN.R-project.org/package=onlineBcp>.
Implement a new stopping rule to detect anomaly in the covariance structure of high-dimensional online data. The detection procedure can be applied to Gaussian or non-Gaussian data with a large number of components. Moreover, it allows both spatial and temporal dependence in data. The dependence can be estimated by a data-driven procedure. The level of threshold in the stopping rule can be determined at a pre-selected average run length. More detail can be seen in Li, L. and Li, J. (2020) "Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks." <arXiv:1911.07762>.
A framework for fitting adaptive forecasting models. Provides a way to use forecasts as input to models, e.g. weather forecasts for energy related forecasting. The models can be fitted recursively and can easily be setup for updating parameters when new data arrives. See the included vignettes, the website <https://onlineforecasting.org> and the paper "onlineforecast: An R package for adaptive and recursive forecasting" <https://journal.r-project.org/articles/RJ-2023-031/>.
Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.
Transactions occurring for a UK-based and registered, non-store online 
    retail between 01/12/2010 and 09/12/2011 (Chen et. al., 2012, <doi:10.1145/1835804.1835882>). 
    This dataset is included in this package with the donor's permission, Dr. Daqing Chen.
Fits two-dimensional data by means of orthogonal nonlinear least-squares using Levenberg-Marquardt minimization and provides functionality for fit diagnostics and plotting.
The Ontario Marginalization Index is a socioeconomic model that is built on Statistics Canada census data.
    The model consists of four dimensions: In 2021, these dimensions were updated to "Material Resources" (previously called "Material Deprivation"), "Households and Dwellings" (previously called "Residential Instability"), "Age and Labour Force" (previously called "Dependency"), and "Racialized and Newcomer Populations" (previously called "Ethnic Concentration").
    This update reflects a movement away from deficit-based language. 2021 data will load with these new dimension names, wheras 2011 and 2016 data will load with the historical dimension names.
    Each of these dimensions are imported for a variety of geographic levels (DA, CD, etc.) for the 2021, 2011 and 2016 administrations of the census.
    These data sets contribute to community analysis of equity with respect to Ontario's Anti-Racism Act.
    The Ontario Marginalization Index data is retrieved from the Public Health Ontario website: <https://www.publichealthontario.ca/en/data-and-analysis/health-equity/ontario-marginalization-index>.
    The shapefile data is retrieved from the Statistics Canada website: <https://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/bound-limit-eng.cfm>.
R Interface to 'ONNX' - Open Neural Network Exchange <https://onnx.ai/>. 
             'ONNX' provides an open source format for machine learning models. 
             It defines an extensible computation graph model, as well as definitions
             of built-in operators and standard data types.
Different measures which can be used to quantify similarities between regions. These measures are isonymy, isonymy between, Lasker distance, coefficients of Hedrick and Nei. In addition, it calculates biodiversity indices such as Margalef, Menhinick, Simpson, Shannon, Shannon-Wiener, Sheldon, Heip, Hill Numbers, Geometric Mean and Cressie and Read statistics.
Growing collection of helper functions for point pattern analysis. Most functions
  are designed to work with the 'spatstat' (<http://spatstat.org>) package. The focus of 
  most functions are either null models or summary functions for spatial point patterns. 
  For a detailed description of all null models and summary functions, see 
  Wiegand and Moloney (2014, ISBN:9781420082548).
Client for the 'Office of National Statistics'
    ('ONS') API <https://api.beta.ons.gov.uk/v1>.
Helps to create 'ggplot2' charts in the style used by the National 
    Road Safety Observatory (ONSV). The package includes functions to 
    customize 'ggplot2' objects with new theme and colors.
Wrapper functions for customizing HTML 
  tables from the 'gt' package to the ONSV style.
Tools for annotating characters (character matrices) with anatomical and phenotype ontologies. Includes functions for visualising character annotations and creating simple queries using ontological relationships.
Provides tools to build and work with an ontology of linked (open) 
    data in a tidy workflow. It is inspired by the Food and Agrilculture 
    Organizations (FAO) caliper platform 
    <https://www.fao.org/statistics/caliper/web/> and 
    makes use of the Simple Knowledge Organisation System (SKOS).
Functions for reading ontologies into R as lists and manipulating sets of ontological terms - 'ontologyX: A suite of R packages for working with ontological data', Greene et al 2017 <doi:10.1093/bioinformatics/btw763>.
Create R plots visualising ontological terms and the relationships between them with various graphical options - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.
Calculate similarity between ontological terms and sets of ontological terms based on term information content and assess statistical significance of similarity in the context of a collection of terms sets - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.
Provides new tools for analyzing discrete trait data integrating bio-ontologies and phylogenetics. It expands on the previous work of Tarasov et al. (2019) <doi:10.1093/isd/ixz009>. The PARAMO pipeline allows to reconstruct ancestral phenomes treating groups of morphological traits as a single complex character. The pipeline incorporates knowledge from ontologies during the amalgamation of individual character stochastic maps.
  Here we expand the current PARAMO functionality by adding new statistical methods for inferring evolutionary phenome dynamics using non-homogeneous Poisson process (NHPP). The new functionalities include: (1) reconstruction of evolutionary rate shifts of phenomes across lineages and time; (2) reconstruction of morphospace dynamics through time; and (3) estimation of rates of phenome evolution at different levels of anatomical hierarchy (e.g., entire body or specific regions only). The package also includes user-friendly tools for visualizing evolutionary rates of different anatomical regions using vector images of the organisms of interest.
Provides functions to calculate the out-of-bag learning curve for random forests for any measure that is available in the 'mlr' package. Supported random forest packages are 'randomForest' and 'ranger' and trained models of these packages with the train function of 'mlr'. The main function is OOBCurve() that calculates the out-of-bag curve depending on the number of trees. With the OOBCurvePars() function out-of-bag curves can also be calculated for 'mtry', 'sample.fraction' and 'min.node.size' for the 'ranger' package.
Intended to create standard human-in-the-loop validity tests for typical automated content analysis such as topic modeling and dictionary-based methods. This package offers a standard workflow with functions to prepare, administer and evaluate a human-in-the-loop validity test. This package provides functions for validating topic models using word intrusion, topic intrusion (Chang et al. 2009,  <https://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models>) and word set intrusion (Ying et al. 2021) <doi:10.1017/pan.2021.33> tests. This package also provides functions for generating gold-standard data which are useful for validating dictionary-based methods. The default settings of all generated tests match those suggested in Chang et al. (2009) and Song et al. (2020) <doi:10.1080/10584609.2020.1723752>.
Provides the class unions that must be
  preloaded in order for the basic tools in the OOMPA (Object-Oriented 
  Microarray and Proteomics Analysis) project to be defined and loaded.
  It also includes vectorized operations for row-by-row means,
  variances, and t-tests. Finally, it provides new color schemes.
  Details on the packages in the OOMPA project can be found at
  <http://oompa.r-forge.r-project.org/>.
This is a data-only package to provide example data for
  other packages that are part of the "Object-Oriented Microrray and
  Proteomics Analysis" suite of packages. These are described in more
  detail at the package URL.
Helper functions for coding object-oriented programming with
    a focus on R6. Includes functions for assertions and testing, looping,
    and re-usable design patterns including Abstract and Decorator
    classes.
Implementation of optimistic optimization methods for global optimization of deterministic or stochastic functions. The algorithms feature guarantees of the convergence to a global optimum. They require minimal assumptions on the (only local) smoothness, where the smoothness parameter does not need to be known. They are expected to be useful for the most difficult functions when we have no information on smoothness and the gradients are unknown or do not exist. Due to the weak assumptions, however, they can be mostly effective only in small dimensions, for example, for hyperparameter tuning.
A comprehensive and cohesive API for the out-of-sample forecasting workflow: 
             data preparation, forecasting - including both traditional econometric time series models and 
             modern machine learning techniques - forecast combination, model and error analysis, and 
             forecast visualization.
Estimates out-of-sample R² through bootstrap or cross-validation as a measure of predictive performance. In addition, a standard error for this point estimate is provided, and confidence intervals are constructed.
Implements the out-of-treatment testing from Kuelpmann and Kuzmics (2020) <doi:10.2139/ssrn.3441675> based on the Vuong Test introduced in Vuong (1989) <doi:10.2307/1912557>. 
    Out-of treatment testing allows for a direct, pairwise likelihood comparison of theories, calibrated with pre-existing data.
Quantifies hypothesis to data fit for repeated measures 
    and longitudinal data, as described by Thorngate (1987) 
    <doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015) 
    <doi:10.1177/2158244015604192>. Hypothesis and data are encoded as
    pairwise relative orderings which are then compared to determine the
    percentage of orderings in the data that are matched by the hypothesis.
Data integration Web application for biobanks by 'OBiBa'. 'Opal' is
    the core database application for biobanks. Participant data, once
    collected from any data source, must be integrated and stored in a central
    data repository under a uniform model. 'Opal' is such a central repository.
    It can import, process, validate, query, analyze, report, and export data.
    'Opal' is typically used in a research center to analyze the data acquired at
    assessment centres. Its ultimate purpose is to achieve seamless
    data-sharing among biobanks. This 'Opal' client allows to interact with 'Opal'
    web services and to perform operations on the R server side. 'DataSHIELD'
    administration tools are also provided.
The online principal component method can process the online data set. The philosophy of the package is described in Guo G. (2018) <doi:10.1080/10485252.2018.1531130>.
An optimized method for distribution-preserving class-proportional down-sampling of bio-medical data.
Several function related to Experimental Design are implemented here, see
  "Optimal Experimental Design with R" by Rasch D. et. al (ISBN 9781439816974).
An R wrapper of OpenAI API endpoints (see
    <https://platform.openai.com/docs/introduction> for details). This package
    covers Models, Completions, Chat, Edits, Images, Embeddings, Audio, Files,
    Fine-tunes, Moderations, and legacy Engines endpoints.
Tools to analyse, interpret and understand air pollution
    data. Data are typically regular time series and air quality
    measurement, meteorological data and dispersion model output can be
    analysed. The package is described in Carslaw and Ropkins (2012,
    <doi:10.1016/j.envsoft.2011.09.008>) and subsequent papers.
Combine the air quality data analysis methods of 'openair'
    with the JavaScript 'Leaflet' (<https://leafletjs.com/>) library.
    Functionality includes plotting site maps, "directional analysis"
    figures such as polar plots, and air mass trajectories.
Based on the 'httr2' framework, the 'OpenAI' interface supports streaming calls and model training. 
             For more details on the API methods implemented, see the 'OpenAI' platform documentation at <https://platform.openai.com/docs/api-reference>.
A set of tools to extract bibliographic content from
    'OpenAlex' database using API <https://docs.openalex.org>.
Provides an interface to connect R with the <https://github.com/IDEMSInternational/open-app-builder> 'OpenAppBuilder' platform, enabling users to retrieve and work with user and notification data for analysis and processing. It is designed for developers and analysts to seamlessly integrate data from 'OpenAppBuilder' into R workflows via a 'Postgres' database connection, allowing direct querying and import of app data into R.
Creates a client with queries for the UK 'Open Banking' ('Open Data') API.
Interface to make HTTP requests to 'OpenBlender' API services. Go to <https://openblender.io> for more information.
Geocode with the OpenCage API, either from place name to longitude 
    and latitude (forward geocoding) or from longitude and latitude to the name 
    and address of a location (reverse geocoding), see 
    <https://opencagedata.com/>.
This package provides an interface to OpenCL, allowing R to leverage computing power of GPUs and other HPC accelerator devices.
A system for embedded scientific computing and reproducible research with R.
    The OpenCPU server exposes a simple but powerful HTTP api for RPC and data interchange
    with R. This provides a reliable and scalable foundation for statistical services or 
    building R web applications. The OpenCPU server runs either as a single-user development
    server within the interactive R session, or as a multi-user Linux stack based on Apache2. 
    The entire system is fully open source and permissively licensed. The OpenCPU website
    has detailed documentation and example apps.
Non-spatial and spatial open-population capture-recapture analysis.
Exposes some of the available 'OpenCV' <https://opencv.org/> algorithms,
    such as a QR code scanner, and edge, body or face detection. These can either be 
    applied to analyze static images, or to filter live video footage from a camera device.
The Open Data Format (ODF) is a new, non-proprietary, multilingual, metadata enriched, and zip-compressed data format with metadata structured in the Data Documentation Initiative (DDI) Codebook standard. This package allows reading and writing of data files in the Open Data Format (ODF) in R, and displaying metadata in different languages. For further information on the Open Data Format, see <https://opendataformat.github.io/>.
Access data from the "City of Toronto
    Open Data Portal" (<https://open.toronto.ca>) directly from R.
Enables the usage of the OpenDota API from <https://www.opendota.com/>, get game lists, and download JSON's of parsed replays from
    the OpenDota API. Also has functionality to execute own code to extract the specific parts of the JSON file.
An implementation of DuMouchel's (1999) <doi:10.1080/00031305.1999.10474456>
  Bayesian data mining method for the market basket problem.
  Calculates Empirical Bayes Geometric Mean (EBGM) and posterior quantile scores
  using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell
  counts in large, sparse contingency tables. Can be used to find unusually high
  reporting rates of adverse events associated with products. In general, can be
  used to mine any database where the co-occurrence of two variables or items is
  of interest. Also calculates relative and proportional reporting ratios.
  Builds on the work of the 'PhViD' package, from which much of the code is
  derived. Some of the added features include stratification to adjust for
  confounding variables and data squashing to improve computational efficiency.
  Includes an implementation of the EM algorithm for hyperparameter estimation
  loosely derived from the 'mederrRank' package.
Access data and processing functionalities of 'openEO' compliant back-ends in R.
The 'openFDA' API facilitates access to Federal Drug Agency (FDA)
  data on drugs, devices, foodstuffs, tobacco, and more with 'httr2'. This
  package makes the API easily accessible, returning objects which the user can
  convert to JSON data and parse. Kass-Hout TA, Xu Z, Mohebbi M et al. (2016)
  <doi:10.1093/jamia/ocv153>.
Social media sites often embed cards when links are shared, based on
    metadata in the 'Open Graph Protocol' (<https://ogp.me/>). This supports extracting
    that metadata from a website. It further allows for the creation of tags to
    add to a website to support the 'Open Graph Protocol' and provides a list of the
    standard tags and their required properties.
The comprehensive knowledge of epigenetic modifications in plants, encompassing histone modifications in regulating gene expression, is not completely ingrained. It is noteworthy that histone deacetylation and histone H3 lysine 27 trimethylation (H3K27me3) play a role in repressing transcription in eukaryotes. In contrast, histone acetylation (H3K9ac) and H3K4me3 have been inevitably linked to the stimulation of gene expression, which significantly influences plant development and plays a role in plant responses to biotic and abiotic stresses. To our knowledge this the first multiclass classifier for predicting histone modification in plants. <doi:10.1186/s12864-019-5489-4>.
Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.
Supplemental functions and data for 'OpenIntro' resources, which 
    includes open-source textbooks and resources for introductory statistics 
    (<https://www.openintro.org/>). The package contains datasets used in our 
    open-source textbooks along with custom plotting functions for reproducing 
    book figures. Note that many functions and examples include color 
    transparency; some plotting elements may not show up properly (or at all) 
    when run in some versions of Windows operating system.
Tools for the analysis of land use and cover (LUC) time series. It 
    includes support for loading spatiotemporal raster data and synthesized 
    spatial plotting. Several LUC change (LUCC) metrics in regular or irregular 
    time intervals can be extracted and visualized through one- and multistep 
    sankey and chord diagrams. A complete intensity analysis according to 
    Aldwaik and Pontius (2012) <doi:10.1016/j.landurbplan.2012.02.010> is 
    implemented, including tools for the generation of standardized multilevel 
    output graphics.
A client for the Open-Meteo API that retrieves Open-Meteo
    weather data in a tidy format. No API key is required. The API specification
    is located at <https://open-meteo.com/en/docs>.
Provides a client for the open-source monitoring and alerting
  toolkit, 'Prometheus', that emits metrics in the 'OpenMetrics' format. Allows
  users to automatically instrument 'Plumber' and 'Shiny' applications, collect
  standard process metrics, as well as define custom counter, gauge, and
  histogram metrics of their own.
'Mindat' ('mindat.org') is one of the world's most widely used databases of mineral species and their distribution. Many scientists in mineralogy, geochemistry, petrology, and other Earth and planetary disciplines have been using the 'Mindat' data. Still, an open data service and the machine interface have never been fully established. To meet the overwhelming data needs, the 'Mindat' team has built an API (<https://api.mindat.org/schema/redoc/>) for data access.'OpenMindat' R package provides valuable functions to bridge the data highway, connecting users' data requirements to the 'Mindat' API server and assist with retrieval and initial processing to improve efficiency further and lower the barrier of data query and access to scientists. 'OpenMindat' provides friendly and extensible data retrieval functions, including the subjects of geomaterials (e.g., rocks, minerals, synonyms, variety, mixture, and commodity), localities, and the IMA (International Mineralogical Association)-approved mineral list. 'OpenMindat' R package will accelerate the process of data-intensive studies in mineral informatics and lead to more scientific discoveries.
We provide an R interface to 'OpenML.org' which is an online machine learning platform where researchers can access open data, download and upload data sets, share their machine learning tasks and experiments and organize them online to work and collaborate with other researchers. 
    The R interface allows to query for data sets with specific properties, and allows the downloading and uploading of data sets, tasks, flows and runs. 
    See <https://www.openml.org/guide/api> for more information.
The 'openMSE' package is designed for building operating models, 
    doing simulation modelling and management strategy evaluation for fisheries.
    'openMSE' is an umbrella package for the 'MSEtool' (Management Strategy Evaluation
    toolkit), 'DLMtool' (Data-Limited Methods toolkit), and 
    SAMtool (Stock Assessment Methods toolkit) packages. By loading and installing
    'openMSE', users have access to the full functionality contained within
    these packages. Learn more about 'openMSE' at <https://openmse.com/>.
Create structural equation models that can be manipulated programmatically.
    Models may be specified with matrices or paths (LISREL or RAM)
    Example models include confirmatory factor, multiple group, mixture
    distribution, categorical threshold, modern test theory, differential
    Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares.
    equations, state space, and many others.
	Support and advanced package binaries available at <http://openmx.ssri.psu.edu>.
    The software is described in Neale, Hunter, Pritikin, Zahery, Brick,
    Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) <doi:10.1007/s11336-014-9435-8>.
An interface to the Apache OpenNLP tools (version 1.5.3).
  The Apache OpenNLP library is a machine learning based toolkit for the
  processing of natural language text written in Java.
  It supports the most common NLP tasks, such as tokenization, sentence
  segmentation, part-of-speech tagging, named entity extraction, chunking,
  parsing, and coreference resolution.
  See <https://opennlp.apache.org/> for more information.
Apache OpenNLP jars and basic English language models.
Provides analyse, interpret and understand noise pollution data. Data are typically regular time series measured with sound meter. The package is partially described in Fogola, Grasso, Masera and Scordino (2023, <DOI:10.61782/fa.2023.0063>).
Allows access to a proof-of-concept database containing Open Access species range models and relevant metadata. Access to the database is via both 'PostgreSQL' connection and API <https://github.com/EnquistLab/Biendata-Frontend>, allowing diverse use-cases.
Analyze repertory grids, a qualitative-quantitative 
    data collection technique devised by George A. Kelly in the 1950s. Today, grids are used across 
    various domains ranging from clinical psychology to marketing. The package contains functions 
    to quantitatively analyze and visualize repertory grid data (e.g. 'Fransella', 'Bell', & 'Bannister', 
    2004, ISBN: 978-0-470-09080-0). The package is part of the The package is part of the 
    <https://openrepgrid.org/> project.
Shiny UI to identify cliques of related constructs in repertory grid data. 
    See Burr, King, & Heckmann (2020) <doi:10.1080/14780887.2020.1794088> for a description 
    of the interpretive clustering (IC) method.
The client streamlines access to the services provided by <https://api.openrouteservice.org>.
  It allows you to painlessly query for directions, isochrones, time-distance matrices, geocoding, elevation, points of interest, and more.
Creativity research involves the need to score open-ended problems.
  Usually done by humans, automatic scoring using AI becomes more
  and more accurate. This package provides a simple interface to
  the 'Open Scoring' API <https://openscoring.du.edu/docs>, leading creativity scoring technology by
  Organiscak et al. (2023) <doi:10.1016/j.tsc.2023.101356>. With it,
  you can score your own data directly from an R script.
Provides functionalities and data structures to retrieve, analyze and visualize aviation 
    data. It includes a client interface to the 'OpenSky' API <https://opensky-network.org>. It allows 
    retrieval of flight information, as well as aircraft state vectors.
Raman and (FT)IR spectral analysis tool for plastic particles and
    other environmental samples (Cowger et al. 2021,
    <doi:10.1021/acs.analchem.1c00123>). With read_any(), Open Specy provides a
    single function for reading individual, batch, or map spectral data files
    like .asp, .csv, .jdx, .spc, .spa, .0, and .zip. process_spec() simplifies
    processing spectra, including smoothing, baseline correction,
    range restriction and flattening, intensity conversions, wavenumber
    alignment, and min-max normalization. Spectra can be identified in batch
    using an onboard reference library (Cowger et al. 2020,
    <doi:10.1177/0003702820929064>) using match_spec(). A Shiny app is available
    via run_app() or online at <https://openanalysis.org/openspecy/>.
Bindings to OpenSSL libssl and libcrypto, plus custom SSH key parsers.
    Supports RSA, DSA and EC curves P-256, P-384, P-521, and curve25519. Cryptographic
    signatures can either be created and verified manually or via x509 certificates. 
    AES can be used in cbc, ctr or gcm mode for symmetric encryption; RSA for asymmetric
    (public key) encryption or EC for Diffie Hellman. High-level envelope functions 
    combine RSA and AES for encrypting arbitrary sized data. Other utilities include key
    generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure random
    number generator, and 'bignum' math methods for manually performing crypto 
    calculations on large multibyte integers.
Accesses high resolution raster maps using the OpenStreetMap
    protocol. Dozens of road, satellite, and topographic map servers are directly
    supported, including Apple, Mapnik, Bing, and stamen. Additionally raster maps
    may be constructed using custom tile servers.  Maps can be
    plotted using either base graphics, or ggplot2. This package is not affiliated
    with the OpenStreetMap.org mapping project.
Setup and connect to 'OpenTripPlanner' (OTP) <http://www.opentripplanner.org/>.
    OTP is an open source platform for multi-modal and multi-agency
    journey planning written in 'Java'. The package allows you to manage a local version or 
    connect to remote OTP server to find walking, cycling, driving, or transit routes.
    This package has been peer-reviewed by rOpenSci (v. 0.2.0.0).
Implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include 'InterVA4' by Byass et al (2012) <doi:10.3402/gha.v5i0.19281>, 'InterVA5' by Byass at al (2019) <doi:10.1186/s12916-019-1333-6>, 'InSilicoVA' by McCormick et al (2016) <doi:10.1080/01621459.2016.1152191>, 'NBC' by Miasnikof et al (2015) <doi:10.1186/s12916-015-0521-2>, and a replication of 'Tariff' method by James et al (2011) <doi:10.1186/1478-7954-9-31> and Serina, et al. (2015) <doi:10.1186/s12916-015-0527-9>. It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics. The 'NBC' method is implemented by the 'nbc4va' package that can be installed from <https://github.com/rrwen/nbc4va>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the 'Tariff' method.
Simplifies the creation of Excel .xlsx files by providing a
    high level interface to writing, styling and editing worksheets.
    Through the use of 'Rcpp', read/write times are comparable to the
    'xlsx' and 'XLConnect' packages with the added benefit of removing the
    dependency on Java.
Simplifies the creation of 'xlsx' files by
    providing a high level interface to writing, styling and editing
    worksheets.
Misc methods to form online predictions, for regression-oriented 
    time-series, by combining a finite set of forecasts provided by the user. See 
             Cesa-Bianchi and Lugosi (2006) <doi:10.1017/CBO9780511546921> for an overview.
Provides a collection of utilities that allow programming with 
    R's operators. Routines allow classifying operators, 
    translating to and from an operator and its underlying function, and inverting 
    some operators (e.g. comparison operators), etc. All methods can be extended
    to custom infix operators.
A set of binary operators for common tasks such as regex
        manipulation.
Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) <doi:10.1016/j.imu.2022.101113>.
Implementation of the Open Perimetry Interface (OPI) for simulating and controlling visual field machines using R. The OPI is a standard for interfacing with visual field testing machines (perimeters) first started as an open source project with support of Haag-Streit in 2010. It specifies basic functions that allow many visual field tests to be constructed. As of February 2022 it is fully implemented on the Haag-Streit Octopus 900 and 'CrewT ImoVifa' ('Topcon Tempo') with partial implementations on the Centervue Compass, Kowa AP 7000 and Android phones. It also has a cousin: the R package 'visualFields', which has tools for analysing and manipulating visual field data.
A toolbox for working with public opinion data from Argentina. It facilitates access to microdata and the calculation of indicators of the Trust in Government Index (ICG), prepared by the Torcuato Di Tella University. Although we will try to document everything possible in English, by its very nature Spanish will be the main language. El paquete fue pensado como una caja de herramientas para el trabajo con datos de opinión pública de Argentina. El mismo  facilita el acceso a los microdatos y el cálculos de indicadores del Índice de Confianza en el Gobierno (ICG), elaborado por la Universidad Torcuato Di Tella.
Designed for performing impact analysis of
  opinions in a digital text document (DTD). The 
  package allows a user to assess the extent to which a theme
  or subject within a document impacts the overall opinion 
  expressed in the document. The package can be applied to a wide 
  range of opinion-based DTD, including commentaries on social media
  platforms (such as 'Facebook', 'Twitter' and 'Youtube'), 
  online products reviews, and so on. 
  The utility of 'opitools' was originally demonstrated 
  in Adepeju and Jimoh (2021) <doi:10.31235/osf.io/c32qh> in the 
  assessment of COVID-19 impacts on neighbourhood policing using 
  Twitter data. Further examples can be found in the vignette of 
  the package.
Provides functions for optimal policy learning in socioeconomic applications helping users to learn the most effective policies based 
	on data in order to maximize empirical welfare. Specifically, 'OPL' allows to find "treatment assignment rules" that maximize the overall 
	welfare, defined as the sum  of the policy effects estimated over all the policy beneficiaries. Documentation about 'OPL' is provided by  
	several international articles via Athey et al (2021, <doi:10.3982/ECTA15732>), Kitagawa et al (2018, <doi:10.3982/ECTA13288>),
        Cerulli (2022, <doi:10.1080/13504851.2022.2032577>), the paper by Cerulli (201, <doi:10.1080/13504851.2020.1820939>) 
	and the book by Gareth et al (2013, <doi:10.1007/978-1-4614-7138-7>).
Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations.
A decision support tool for prioritizing conservation projects.
    Prioritizations can be developed by maximizing expected feature richness,
    expected phylogenetic diversity, the number of features that meet
    persistence targets, or identifying a set of projects that meet persistence
    targets for minimal cost. Constraints (e.g. lock in specific actions) and
    feature weights can also be specified to further customize prioritizations.
    After defining a project prioritization problem, solutions can be obtained
    using exact algorithms, heuristic algorithms, or random processes. In
    particular, it is recommended to install the 'Gurobi' optimizer (available
    from <https://www.gurobi.com>) because it can identify optimal solutions
    very quickly. Finally, methods are provided for comparing different
    prioritizations and evaluating their benefits. For more information, see
    Hanson et al. (2019) <doi:10.1111/2041-210X.13264>.
Estimates ordinal probit switching regression models - a Heckman
    type selection model with an ordinal selection and continuous outcomes.
    Different model specifications are allowed for each treatment/regime. For
    more details on the method, see Wang & Mokhtarian (2024) <doi:10.1016/j.tra.2024.104072>
    or Chiburis & Lokshin (2007) <doi:10.1177/1536867X0700700202>.
Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) <doi:10.18637/jss.v083.i05>.
Given a certain coverage level, obtains simultaneous confidence
    bands for the survival and cumulative hazard functions such that the area
    between is minimized. Produces an approximate solution based on local time
    arguments.
Computes A-, MV-, D- and E-optimal or near-optimal block designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The algorithms used in this package are based on the treatment exchange and array exchange algorithms of Debusho, Gemechu and Haines (2016, unpublished). The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.
Defines thresholds for breaking data into a number of
  discrete levels, minimizing the (mean) squared error within all bins.
Estimates optimal number of biomarkers for two-group
        classification based on microarray data.
Fast, optimal, and reproducible clustering algorithms for
 circular, periodic, or framed data. The algorithms introduced here
 are based on a core algorithm for optimal framed clustering the authors
 have developed (Debnath & Song 2021) <doi:10.1109/TCBB.2021.3077573>.
 The runtime of these algorithms is O(K N log^2 N), where K is the number
 of clusters and N is the number of circular data points. On a desktop
 computer using a single processor core, millions of data points can be
 grouped into a few clusters within seconds. One can apply the algorithms
 to characterize events along circular DNA molecules, circular RNA
 molecules, and circular genomes of bacteria, chloroplast, and
 mitochondria. One can also cluster climate data along any given
 longitude or latitude. Periodic data clustering can be formulated as
 circular clustering. The algorithms offer a general high-performance
 solution to circular, periodic, or framed data clustering.
Aids practitioners to optimally design experiments
    that measure the slope divided by the intercept and provides confidence intervals for the ratio.
Tetra-allele cross often referred as four-way cross or double cross or four-line cross are those type of mating designs in which every cross is obtained by mating amongst four inbred lines. A tetra-allele cross can be obtained by crossing the resultant of two unrelated diallel crosses. A common triallel cross involving four inbred lines A, B, C and D can be symbolically represented as (A X B) X (C X D) or (A, B, C, D) or (A B C D) etc. Tetra-allele cross can be broadly categorized as Complete Tetra-allele Cross (CTaC) and Partial Tetra-allele Crosses (PTaC). Rawlings and Cockerham (1962)<doi:10.2307/2527461> firstly introduced and gave the method of analysis for tetra-allele cross hybrids using the analysis method of single cross hybrids under the assumption of no linkage. The set of all possible four-way mating between several genotypes (individuals, clones, homozygous lines, etc.) leads to a CTaC. If there are N number of inbred lines involved in a CTaC, the the total number of crosses, T = N*(N-1)*(N-2)*(N-3)/8. When more number of lines are to be considered, the total number of crosses in CTaC also increases. Thus, it is almost impossible for the investigator to carry out the experimentation with limited available resource material. This situation lies in taking a fraction of CTaC with certain underlying properties, known as PTaC.
Calculates D-, Ds-, A-, I- and L-optimal designs for non-linear models, via an implementation of the cocktail algorithm (Yu, 2011, <doi:10.1007/s11222-010-9183-2>). Compares designs via their efficiency, and augments any design with a controlled efficiency. An efficient rounding function has been provided to transform approximate designs to exact designs.
Tools to assist in safely applying user generated objective and 
   derivative function to optimization programs. These are primarily function 
   minimization methods with at most bounds and masks on the parameters.
   Provides a way to check the basic computation of objective functions that 
   the user provides, along with proposed gradient and Hessian functions, 
   as well as to wrap such functions to avoid failures when inadmissible parameters 
   are provided. Check bounds and masks. Check scaling or optimality conditions. 
   Perform an axial search to seek lower points on the objective function surface. 
   Includes forward, central and backward gradient approximation codes.
Optimal group-sequential designs minimise some function of the expected and maximum sample size whilst controlling the type I error rate and power at a specified level. 'OptGS' provides functions to quickly search for near-optimal group-sequential designs for normally distributed outcomes. The methods used are described in Wason, JMS (2015) <doi:10.18637/jss.v066.i02>.
Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of 'Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013).
Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <arXiv:2202.06374> for details of methods.
Implements a simulation study to assess the strengths and
    weaknesses of causal inference methods for estimating policy effects
    using panel data. See Griffin et al. (2021)
    <doi:10.1007/s10742-022-00284-w> and Griffin et al. (2022)
    <doi:10.1186/s12874-021-01471-y> for a description of our methods.
The restricted optimal design method is implemented to optimally 
 allocate a set of items that require calibration to a group of examinees.
 The optimization process is based on the method described in detail by 
 Ul Hassan and Miller in their works published in (2019) 
 <doi:10.1177/0146621618824854> and (2021) <doi:10.1016/j.csda.2021.107177>.
 To use the method, preliminary item characteristics must be provided as input.
 These characteristics can either be expert guesses or based on previous
 calibration with a small number of examinees. The item characteristics
 should be described in the form of parameters for an Item Response
 Theory (IRT) model. These models can include the Rasch model, the
 2-parameter logistic model, the 3-parameter logistic model, or a mixture
 of these models. The output consists of a set of rules for each item
 that determine which examinees should be assigned to each item. The
 efficiency or gain achieved through the optimal design is quantified by
 comparing it to a random allocation. This comparison allows for an
 assessment of how much improvement or advantage is gained by using the
 optimal design approach. This work was supported by the Swedish Research Council
 (Vetenskapsrådet) Grant 2019-02706.
Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>.
Likelihood based optimal partitioning and indicator
  species analysis. Finding the best binary partition for each species
  based on model selection, with the possibility to take into account
  modifying/confounding variables as described
  in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>.
  The package implements binary and multi-level response models,
  various measures of uncertainty, Lorenz-curve based thresholding,
  with native support for parallel computations.
A single function 'options.ifunset(...)' is contained herewith, which allows the user to set a global option ONLY if it is not already set. By this token, for package maintainers this function can be used in preference to the standard 'options(...)' function, making provision for THEIR end user to place 'options(...)' directives within their '.Rprofile' file, which will not be overridden at the point when a package is loaded.
Parse options from the command-line using a simple, clean syntax. 
    It requires little or no specification and supports short and long options,
    GNU-, Java- or Microsoft- style syntaxes, verb commands and more.
A set of standard benchmark optimization functions for R and
    a common interface to sample them.
Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.
Algorithms for D-, A-, I-, and c-optimal designs. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at <https://www.gurobi.com> and the file gurobi_inst.txt, respectively.
Functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) <doi:10.2307/2342192> or Wright (2012) <doi:10.1080/00031305.2012.733679> allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits.
This is a tool to find the optimal rerandomization threshold in non-sequential experiments. We offer three procedures based 
	on assumptions made on the residuals distribution: (1) normality assumed (2) excess kurtosis assumed (3) entire distribution assumed.
	Illustrations are included. Also included is a routine to unbiasedly estimate Frobenius norms of variance-covariance matrices.
	Details of the method can be found in "Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments"
	Adam Kapelner, Abba M. Krieger, Michael Sklar and David Azriel (2020) <arXiv:1905.03337>.
Identifies an optimal transformation of a surrogate marker such that the proportion of treatment effect explained can be inferred based on the transformation of the surrogate and nonparametrically estimates two model-free quantities of this proportion. Details are described in Wang et al (2020) <doi:10.1093/biomet/asz065>.
Functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) <doi:10.1177/0962280218821394> for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) <doi:10.1002/bimj.200900242> for diagnostic markers.
Identify the optimal timing for new treatment initiation during multiple state disease transition, including  multistate model fitting, simulation of mean residual lifetime for a given transition state, and estimation of confidence interval. The method is referred to de Wreede, L., Fiocco, M., & Putter, H. (2011) <doi:10.18637/jss.v038.i07>.
Computes confidence regions on the location of response surface optima. Response surface models can be up to cubic polynomial models in up to 5 controllable factors, or Thin Plate Spline models in 2 controllable factors.
Provides a set of commands to manage an abstract
        optimization method. The goal is to provide a building block
        for a large class of specialized optimization methods. This
        package manages: the number of variables, the minimum and
        maximum bounds, the number of non linear inequality
        constraints, the cost function, the logging system, various
        termination criteria, etc...
Tools for checking that the output of an optimization algorithm is indeed at a local mode of the objective function.  This is accomplished graphically by calculating all one-dimensional "projection plots" of the objective function, i.e., varying each input variable one at a time with all other elements of the potential solution being fixed.  The numerical values in these plots can be readily extracted for the purpose of automated and systematic unit-testing of optimization routines.
Provides general purpose tools for helping users to implement steepest
             gradient descent methods for function optimization; for details see
             Ruder (2016) <arXiv:1609.04747v2>. Currently, the Steepest 2-Groups
             Gradient Descent and the Adaptive Moment Estimation (Adam) are the
             methods implemented. Other methods will be implemented in the future.
Flexible optimizer with numerous input specifications for detailed
  parameterisation. Designed for complex loss functions with state and 
  parameter space constraints. Visualization tools for validation and analysis
  of the convergence are included.
Provides a unified object-oriented framework for numerical 
    optimizers in R. Allows for both minimization and maximization with any 
    optimizer, optimization over more than one function argument, measuring of 
    computation time, setting a time limit for long optimization tasks.
Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) <doi:10.1038/ncomms11877> and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) <doi:10.1016/j.jenvman.2020.110248>.
A wrapper for 'optim' for nonlinear regression problems; see Nocedal J and Write S (2006, ISBN: 978-0387-30303-1).  Performs ordinary least squares (OLS), iterative re-weighted least squares (IRWLS), and maximum likelihood (MLE). Also includes the robust outlier detection (ROUT) algorithm; see Motulsky, H and Brown, R (2006)<doi:10.1186/1471-2105-7-123>.
Calculates autoecological data (optima and tolerance ranges) of a biological species
    given an environmental matrix. The package calculates by weighted averaging, using the number of occurrences to adjust the 
    tolerance assigned to each taxon to estimate optima and tolerance range in cases where taxa 
    have unequal occurrences. See the detailed methodology by Birks et al. (1990) <doi:10.1098/rstb.1990.0062>, and a 
    case example by Potapova and Charles (2003) <doi:10.1046/j.1365-2427.2003.01080.x>.
Provides a parallel version of the L-BFGS-B method of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time.
Provides a building block for optimization algorithms
        based on a simplex. The 'optimsimplex' package may be used in the
        following optimization methods: the simplex method of Spendley
        et al. (1962) <doi:10.1080/00401706.1962.10490033>, the method of 
        Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308>, Box's algorithm for
        constrained optimization (1965) <doi:10.1093/comjnl/8.1.42>, the 
        multi-dimensional search by Torczon (1989) 
        <https://www.cs.wm.edu/~va/research/thesis.pdf>, etc...
Intended to assist in the choice of the sampling strategy to implement in a survey.
Assessment and diagnostics for comparing competing
    clustering solutions, using predictive models. The main intended
    use is for comparing clustering/classification solutions of
    ecological data (e.g. presence/absence, counts, ordinal scores) to
    1) find an optimal partitioning solution, 2) identify
    characteristic species and 3) refine a classification by merging
    clusters that increase predictive performance. However, in a more
    general sense, this package can do the above for any set of
    clustering solutions for i observations of j variables.
Provides a replacement and extension of the optim()
    function to call to several function minimization codes in R in a single
    statement. These methods handle smooth, possibly box constrained functions 
    of several or many parameters. Note that function 'optimr()' was prepared to
    simplify the incorporation of minimization codes going forward. Also implements some
    utility codes and some extra solvers, including safeguarded Newton methods.
    Many methods previously separate are now included here.
    This is the version for CRAN.
Introduces optional types with some() and none, as well as match_with() from functional languages.
Efficient Monte Carlo Algorithms for the price and the sensitivities of Asian and European Options under Geometric Brownian Motion.
Simple mechanisms for defining and interpreting package options. Provides
    helpers for interpreting environment variables, global options, defining
    default values and more.
Utilizes the Black-Scholes-Merton option pricing model to 
    calculate key option analytics and perform graphical analysis of various option strategies.
    Provides functions to calculate the option premium and option
    greeks of European-style options.
Optimal scaling of a data vector, relative to a set of targets, is
    obtained through a least-squares transformation subject to appropriate measurement
    constraints. The targets are usually predicted values from a statistical
    model. If the data are nominal level, then the transformation must be
    identity-preserving. If the data are ordinal level, then the
    transformation must be monotonic. If the data are discrete, then tied data
    values must remain tied in the optimal transformation. If the data are
    continuous, then tied data values can be untied in the optimal
    transformation.
A framework for the optimization of breeding programs via optimum contribution selection and mate allocation. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and for preparing and plotting pedigrees. The methods are described in:\n Wellmann, R., and Pfeiffer, I. (2009) <doi:10.1017/S0016672309000202>.\n Wellmann, R., and Bennewitz, J. (2011) <doi:10.2527/jas.2010-3709>.\n Wellmann, R., Hartwig, S., Bennewitz, J. (2012) <doi:10.1186/1297-9686-44-34>.\n de Cara, M. A. R., Villanueva, B., Toro, M. A., Fernandez, J. (2013) <doi:10.1111/mec.12560>.\n Wellmann, R., Bennewitz, J., Meuwissen, T.H.E. (2014) <doi:10.1017/S0016672314000196>.\n Wellmann, R. (2019) <doi:10.1186/s12859-018-2450-5>.
Solver for linear, quadratic, and rational programs with linear, quadratic, and rational constraints. A unified interface to different R packages is provided. Optimization problems are transformed into equivalent formulations and solved by the respective package. For example, quadratic programming problems with linear, quadratic and rational constraints can be solved by augmented Lagrangian minimization using package 'alabama', or by sequential quadratic programming using solver 'slsqp'.  Alternatively, they can be reformulated as optimization problems with second order cone constraints and solved with package 'cccp'.
A collection of functions that aid in calculating the optimum
  time to stock hatchery reared fish into a body of water given the growth, 
  mortality and cost of raising a particular number of individuals to a certain 
  length.
The popular population genetic software 'Treemix' by
    'Pickrell and Pritchard' (2012) <DOI:10.1371/journal.pgen.1002967>
    estimates the number of migration edges on a population tree.
    However, it can be difficult to determine the number of migration
    edges to include. Previously, it was customary to stop adding migration
    edges when 99.8% of variation in the data was explained, but 'OptM'
    automates this process using an ad hoc statistic based on the
    second-order rate of change in the log likelihood.  'OptM' also
    has added functionality for various threshold modeling
    to compare with the ad hoc statistic.
Distance based bipartite matching using minimum cost flow, oriented
    to matching of treatment and control groups in observational studies ('Hansen'
    and 'Klopfer' 2006 <doi:10.1198/106186006X137047>). Routines are provided to
    generate distances from generalised linear models (propensity score
    matching), formulas giving variables on which to limit matched distances,
    stratified or exact matching directives, or calipers, alone or in
    combination.
A command line parser inspired by Python's 'optparse' library to
    be used with Rscript to write "#!" shebang scripts that accept short and
    long flag/options.
Solves linear systems of form Ax=b via Gauss elimination, 
  LU decomposition, Gauss-Seidel, Conjugate Gradient Method (CGM) and Cholesky methods.
Computes A-, MV-, D- and E-optimal or near-optimal row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all pairwise treatment contrasts. The algorithms used in this package are based on the array exchange and treatment exchange algorithms adopted from Debusho, Gemechu and Haines (2016, unpublished) algorithms after adjusting for the row-column designs setup. The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.
Splits initial strata into refined strata that optimize covariate balance. For more information, please email the author for a copy of the accompanying manuscript. To solve the linear program, the 'Gurobi' commercial optimization software is recommended, but not required. The 'gurobi' R package can be installed following the instructions at <https://www.gurobi.com/documentation/9.1/refman/ins_the_r_package.html>.
Calculating the stability of random forest with certain numbers of trees. The non-linear relationship between stability and numbers of trees is described using a logistic regression model and used to estimate the optimal number of trees.
Subsampling based variable selection for low dimensional generalized linear models. The methods repeatedly subsample the data minimizing an information criterion (AIC/BIC) over a sequence of nested models for each subsample. Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models.
The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. 
             The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.
Optimal testing under general dependence. The R package implements procedures proposed in Wang, Han, and Tong (2022). The package includes parameter estimation procedures, the computation for the posterior probabilities, and the testing procedure.
Use optimization to estimate weights that balance covariates for binary, multinomial, and continuous treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates.
Provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data.  The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift.  See <http://i.giwebb.com/index.php/research/association-discovery/> for more information in relation to the OPUS Miner algorithm.
Provides details such as Morphine Equivalent Dose (MED), 
    brand name and opioid content which are calculated of all oral opioids 
    authorized for sale by Health Canada and the FDA based on their Drug Identification Number (DIN) or National Drug Code (NDC). 
    MEDs are calculated based on recommendations by Canadian Institute for Health Information (CIHI) and Von Korff et al (2008)
    and information obtained from Health Canada's Drug Product Database's monthly data dump or FDA Daily database for Canadian and US databases respectively. 
    Please note in no way should output from this package be a substitute for medical advise.  
    All medications should only be consumed on prescription from a licensed healthcare provider.
Turn 'tidymodels' workflows into objects containing the
    sufficient sequential equations to perform predictions. These smaller
    objects allow for low dependency prediction locally or directly in
    databases.
Implements orbit counting using a fast combinatorial approach.
	Counts orbits of nodes and edges from edge matrix or data frame, or a
	graph object from the graph package.
Functions to perform subspace clustering and classification.
Provides clustering of genes with similar 
  dose response (or time course) profiles. It implements the method 
  described by Lin et al. (2012).
I tend to repeat the same code chunks over and over again. At
    first, this was fine for me and I paid little attention to such redundancies.
    A little later, when I got tired of manually replacing Linux filepaths with
    the referring Windows versions, and vice versa, I started to stuff some very
    frequently used work-steps into functions and, even later, into a proper R
    package. And that's what this package is - a hodgepodge of various R functions
    meant to simplify (my) everyday-life coding work without, at the same time,
    being devoted to a particular scope of application.
Implements ordered beta regression models, which are for modeling continuous variables with upper and lower bounds, such as
   survey sliders, dose-response relationships and indexes. For more information, see
   Kubinec (2023) <doi:10.31235/osf.io/2sx6y>. The package is a front-end to the R package 'brms', which 
   facilitates a range of regression specifications, including hierarchical, dynamic and
   multivariate modeling.
Algorithms for ordinal causal discovery. This package aims to enable users to discover causality for observational ordinal categorical data with greedy and exhaustive search. See Ni, Y., & Mallick, B. (2022) <https://proceedings.mlr.press/v180/ni22a/ni22a.pdf> "Ordinal Causal Discovery. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530–1540".
Provides the setup and calculations needed
        to run a likelihood-based continual reassessment method (CRM)
        dose finding trial and performs simulations to assess design
        performance under various scenarios. 3 dose finding designs
        are included in this package: ordinal proportional odds model
        (POM) CRM, ordinal continuation ratio (CR) model CRM, and the
        binary 2-parameter logistic model CRM.
        These functions allow customization of design characteristics
        to vary sample size, cohort sizes, target dose-limiting
        toxicity (DLT) rates, discrete or continuous dose levels,
        combining ordinal grades 0 and 1 into one category, and
        incorporate safety and/or stopping rules.
        For POM and CR model designs, ordinal toxicity grades are
        specified by common terminology criteria for adverse events
        (CTCAE) version 4.0.
        Function 'pseudodata' creates the necessary starting models
        for these 3 designs, and function 'nextdose' estimates the
        next dose to test in a cohort of patients for a target DLT
        rate.
        We also provide the function 'crmsimulations' to assess the
        performance of these 3 dose finding designs under various
        scenarios.
Estimate location-shift models or rating-scale models accounting for response styles (RSRS) for the regression analysis of ordinal responses.
Functions for extracting text and tables from 
  PDF-based order documents. It provides an n-gram-based approach for identifying 
  the language of an order document. It furthermore uses R-package 'pdftools' to 
  extract the text from an order document. In the case that the PDF document is 
  only including an image (because it is scanned document), R package 'tesseract' 
  is used for OCR. Furthermore, the package provides functionality for identifying 
  and extracting order position tables in order documents based on a clustering approach.
Functions to test/check/verify/investigate the ordering of vectors. 
    The 'is_[strictly_]*' family of functions test vectors for 
    'sorted', 'monotonic', 'increasing', 'decreasing' order; 'is_constant' 
    and 'is_incremental' test for the degree of ordering. `ordering` 
    provides a numeric indication of ordering -2 (strictly decreasing) to 
    2 (strictly increasing).
Order, create and store reports from R.  By defining a
    lightweight interface around the inputs and outputs of an
    analysis, a lot of the repetitive work for reproducible research
    can be automated.  We define a simple format for organising and
    describing work that facilitates collaborative reproducible
    research and acknowledges that all analyses are run multiple
    times over their lifespans.
Set of tools to generate samples of k-th order statistics and others quantities of interest from new families of distributions. 
    The main references for this package are: C. Kleiber and S. Kotz (2003) Statistical size distributions in economics and actuarial sciences; Gentle, J. (2009), Computational Statistics, Springer-Verlag; 
    Naradajah, S. and Rocha, R. (2016), <DOI:10.18637/jss.v069.i10> and Stasinopoulos, M. and Rigby, R. (2015), <DOI:10.1111/j.1467-9876.2005.00510.x>.
    The families of distributions are: Benini distributions, Burr distributions, Dagum distributions, Feller-Pareto distributions, Generalized Pareto distributions, Inverse Pareto distributions, The Inverse Paralogistic distributions, Marshall-Olkin G distributions, exponentiated G distributions, beta G distributions, 
    gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, 
    beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, 
    exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, 
    exponentiated exponential Poisson G distributions, Poisson-inverse gaussian distributions, Skew normal type 1 distributions, Skew student t distributions, Singh-Maddala distributions, Sinh-Arcsinh distributions, Sichel distributions, Zero inflated Poisson distributions.
All the methods in this package generate a vector of uniform order statistics using a beta distribution and use an inverse cumulative distribution function for some distribution to give a vector of random order statistic variables for some distribution. This is much more efficient than using a loop since it is directly sampling from the order statistic distribution.
In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.
Additive proportional odds model for ordinal data using Laplace P-splines. The combination of Laplace approximations and P-splines enable fast and flexible inference in a Bayesian framework. Specific approximations are proposed to account for the asymmetry in the marginal posterior distributions of non-penalized parameters. For more details, see Lambert and Gressani (2023) <doi:10.1177/1471082X231181173> ; Preprint: <arXiv:2210.01668>).
Calculates ordinated diet breadth with some plotting functions.
Implementation of cumulative link (mixed) models also known
    as ordered regression models, proportional odds models, proportional
    hazards models for grouped survival times and ordered logit/probit/...
    models. Estimation is via maximum likelihood and mixed models are fitted
    with the Laplace approximation and adaptive Gauss-Hermite quadrature.
    Multiple random effect terms are allowed and they may be nested, crossed or
    partially nested/crossed. Restrictions of symmetry and equidistance can be
    imposed on the thresholds (cut-points/intercepts). Standard model
    methods are available (summary, anova, drop-methods, step,
    confint, predict etc.) in addition to profile methods and slice
    methods for visualizing the likelihood function and checking
    convergence.
Provides a function for fitting various penalized Bayesian
    cumulative link ordinal response models when the number of parameters
    exceeds the sample size. These models have been described in 
    Zhang and Archer (2021) <doi:10.1186/s12859-021-04432-w>.
A regression framework for response variables which are continuous
    self-rating scales such as the Visual Analog Scale (VAS) used in pain
    assessment, or the Linear Analog Self-Assessment (LASA) scales in quality
    of life studies. These scales measure subjects' perception of an intangible
    quantity, and cannot be handled as ratio variables because of their inherent
    non-linearity. We treat them as ordinal variables, measured on a continuous
    scale. A function (the g function) connects the scale with an underlying
    continuous latent variable. The link function is the inverse of the CDF of the
    assumed underlying distribution of the latent variable. A variety of
    link functions are currently implemented. Such models are described in Manuguerra et al (2020) <doi:10.18637/jss.v096.i08>.
The ordinal forest (OF) method allows ordinal regression with high-dimensional
  and low-dimensional data. After having constructed an OF prediction rule using a training dataset, 
  it can be used to predict the values of the ordinal target variable for new observations.
  Moreover, by means of the (permutation-based) variable importance measure of OF, it is also
  possible to rank the covariates with respect to their importance in the prediction of the 
  values of the ordinal target variable.
  OF is presented in Hornung (2020).
  NOTE: Starting with package version 2.4, it is also possible to obtain class probability 
  predictions in addition to the class point predictions. Moreover, the variable importance values
  can also be based on the class probability predictions. Preliminary results indicate that
  this might lead to a better discrimination between influential and non-influential covariates.
  The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() 
  (prediction of the target variable values of new observations).
  References:
  Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4–17. 
  <doi:10.1007/s00357-018-9302-x>.
Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.
It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019).
Fits ordinal regression models with elastic net penalty.
    Supported model families include cumulative probability, stopping ratio, 
    continuation ratio, and adjacent category. These families are a subset of 
    vector glm's which belong to a model class we call the elementwise link 
    multinomial-ordinal (ELMO) class. Each family in this class links a vector 
    of covariates to a vector of class probabilities. Each of these families 
    has a parallel form, which is appropriate for ordinal response data, as 
    well as a nonparallel form that is appropriate for an unordered categorical
    response, or as a more flexible model for ordinal data. The parallel model
    has a single set of coefficients, whereas the nonparallel model has a set of
    coefficients for each response category except the baseline category. It is 
    also possible to fit a model with both parallel and nonparallel terms, which 
    we call the semi-parallel model. The semi-parallel model has the flexibility 
    of the nonparallel model, but the elastic net penalty shrinks it toward the 
    parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) 
    <doi:10.18637/jss.v099.i06>.
Ordinal patterns describe the dynamics of a time series by looking at the ranks of subsequent observations. By comparing ordinal patterns of two times series, Schnurr (2014) <doi:10.1007/s00362-013-0536-8> defines a robust and non-parametric dependence measure: the ordinal pattern coefficient. Functions to calculate this and a method to detect a change in the pattern coefficient proposed in Schnurr and Dehling (2017) <doi:10.1080/01621459.2016.1164706> are provided. Furthermore, the package contains a function for calculating the ordinal pattern frequencies. Generalized ordinal patterns as proposed by Schnurr and Fischer (2022) <doi:10.1016/j.csda.2022.107472> are also considered.
Implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with 'rjags'. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper.
Simultaneously evaluate multiple ordinal outcome measures. Applied data analysts in particular are faced with uncertainty in choosing appropriate statistical tests for ordinal data. The included 'shiny' application allows users to simulate outcomes given different ordinal data distributions.
We consider the problem of estimating two isotonic regression curves g1* and g2* under the constraint that they are ordered, i.e. g1* <= g2*. Given two sets of n data points y_1, ..., y_n and z_1, ..., z_n that are observed at (the same) deterministic design points x_1, ..., x_n, the estimates are obtained by minimizing the Least Squares criterion L(a, b) = sum_{i=1}^n (y_i - a_i)^2 w1(x_i) + sum_{i=1}^n (z_i - b_i)^2 w2(x_i) over the class of pairs of vectors (a, b) such that a and b are isotonic and a_i <= b_i for all i = 1, ..., n. We offer two different approaches to compute the estimates: a projected subgradient algorithm where the projection is calculated using a PAVA as well as Dykstra's cyclical projection algorithm.
Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with a pre-specified correlation matrix and marginal distributions.
             The details of the method are explained in Demirtas et al. (2015) <DOI:10.1080/10543406.2014.920868>.
Selection, fusion, and/or smoothing of ordinally scaled independent variables using a group lasso, fused lasso or generalized ridge penalty, as well as non-linear principal components analysis for ordinal variables using a second-order difference/smoothing penalty.
Ordination comprises several multivariate exploratory and
    explanatory techniques with theoretical foundations in geometric data
    analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and
    applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for
    foundations.
    Greenacre (2010, ISBN:978-84-923846) shows how the most established of
    these, including principal components analysis, correspondence analysis,
    multidimensional scaling, factor analysis, and discriminant analysis,
    rely on eigen-decompositions or singular value decompositions of
    pre-processed numeric matrix data.
    These decompositions give rise to a set of shared coordinates along which
    the row and column elements can be measured. The overlay of their
    scatterplots on these axes, introduced by Gabriel (1971)
    <doi:10.1093/biomet/58.3.453>, is called a biplot.
    'ordr' provides inspection, extraction, manipulation, and visualization
    tools for several popular ordination classes supported by a set of recovery
    methods. It is inspired by and designed to integrate into 'tidyverse'
    workflows provided by Wickham et al (2019) <doi:10.21105/joss.01686>.
Provides an alternative to R's built-in functionality for handling
    regular expressions, based on the Onigmo library. Offers first-class
    compiled regex objects, partial matching and function-based substitutions,
    amongst other features.
The Sequence of Physical Processes (SPP) framework is a way of interpreting the transient data derived from oscillatory rheological tests. It is designed to allow both the linear and non-linear deformation regimes to be understood within a single unified framework. This code provides a convenient way to determine the SPP framework metrics for a given sample of oscillatory data. It will produce a text file containing the SPP metrics, which the user can then plot using their software of choice. It can also produce a second text file with additional derived data (components of tangent, normal, and binormal vectors), as well as pre-plotted figures if so desired. It is the R version of the Package SPP by Simon Rogers Group for Soft Matter (Simon A. Rogers, Brian M. Erwin, Dimitris Vlassopoulos, Michel Cloitre (2011) <doi:10.1122/1.3544591>).
An implementation of the Ordered Forest estimator as developed 
    in Lechner & Okasa (2019) <arXiv:1907.02436>. The Ordered Forest flexibly
    estimates the conditional probabilities of models with ordered categorical
    outcomes (so-called ordered choice models). Additionally to common machine 
    learning algorithms the 'orf' package provides functions for estimating
    marginal effects as well as statistical inference thereof and thus provides
    similar output as in standard econometric models for ordered choice. The
    core forest algorithm relies on the fast C++ forest implementation from
    the 'ranger' package (Wright & Ziegler, 2017) <arXiv:1508.04409>.
Automates and standardizes the import of raw data from Oregon RFID (radio-frequency identification) ORMR (Oregon RFID Multi-Reader) and ORSR (Oregon RFID Single Reader) antenna readers. Compiled data can then be combined within multi-reader arrays for further analysis, including summarizing tag and reader detections, determining tag direction, and calculating antenna efficiency.
A system to help you organize projects. Most analyses have three (or more) main sections: code, results, and data, each with different requirements (version control/sharing/encryption). You provide folder locations and 'org' helps you take care of the details.
Provides functions for quickly creating R and Python scripts, as 
    well as 'Rmarkdown' or Quarto documents with automatically assigned name 
    prefixes. Prefixes are either file counts (e.g. "001") or dates 
    (e.g. "2022-09-26").
Organic/biological mass spectrometry data analysis.
Provides functionality to process text files created by Emacs' Org mode, and decompose the content to the smallest components (headlines, body, tag, clock entries etc).  Emacs is an extensible, customizable text editor and Org mode is for keeping notes, maintaining TODO lists, planning projects.  Allows users to analyze org files as data frames in R, e.g., to convieniently group tasks by tag into project and calculate total working hours.  Also provides some help functions like search.parent, gg.pie (visualise working hours in ggplot2) and tree.headlines (visualise headline stricture in tree format) to help user managing their complex org files.
Helper functions for Org files (<https://orgmode.org/>):
  a generic function 'toOrg' for transforming R objects into Org
  markup (most useful for data frames; there are also methods for
  Dates/POSIXt) and a function to read Org tables into data frames.
A user-friendly R-based software package for
        gene clustering. Clusters are given by genes matched to
        prespecified profiles across various ordered treatment groups.
        It is particularly useful for analyzing data obtained from
        short time-course or dose-response microarray experiments.
Representations, conversions and display of orientation SO(3) data.
 See the orientlib help topic for details.
A general framework for the application of cross-validation schemes
    to particular functions. By allowing arbitrary lists of results, origami
    accommodates a range of cross-validation applications. This implementation
    was first described by Coyle and Hejazi (2018) <doi:10.21105/joss.00512>.
A visualization tool for multivariate data. This package maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multi-criteria decision-making.
Automatically adding 'pkg::' to a function, i.e. mutate()
    becomes dplyr::mutate(). It is up to the user to determine which
    packages should be used explicitly, whether to include base R packages
    or use the functionality on selected text, a file, or a complete
    directory. User friendly logging is provided in the 'RStudio' Markers
    pane. Lives in the spirit of 'lintr' and 'styler'. Can also be used
    for checking which packages are actually used in a project.
Functions to handle ordinal relations reflected within the feature space. Those function allow to search for ordinal relations in multi-class datasets. One can check whether proposed relations are reflected in a specific feature representation. Furthermore, it provides functions to filter, organize and further analyze those ordinal relations.
Obtaining Bayes Expected A Posteriori (EAP) individual score 
    estimates based on linear and non-linear extended Exploratoy Factor Analysis
    solutions that include a correlated-residual structure.
Algorithm of online regularized k-means to deal with online multi(single) view data.
 The philosophy of the package is described in Guo G. (2020) 
 <doi:10.1080/02331888.2020.1823979>.
Objects and methods to handle and solve the min-sum location problem, also known as Fermat-Weber problem. The min-sum location problem search for a point such that the weighted sum of the distances to the demand points are minimized. See "The Fermat-Weber location problem revisited" by Brimberg, Mathematical Programming, 1, pg. 71-76, 1995. <DOI:10.1007/BF01592245>.
	     General global optimization algorithms are used to solve the problem, along with the adhoc Weiszfeld method, see "Sur le point pour lequel la Somme des distances de n points donnes est minimum", by Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pg. 355-386, 1937 or "On the point for which the sum of the distances to n given points is minimum", by E. Weiszfeld and F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.
Help and demo in Spanish of the orloca package. (Ayuda y demo en espanol del paquete orloca.) Objetos y metodos para manejar y resolver el problema de localizacion de suma minima, tambien conocido como problema de Fermat-Weber. El problema de localizacion de suma minima busca un punto tal que la suma ponderada de las distancias a los puntos de demanda se minimice. Vease "The Fermat-Weber location problem revisited" por Brimberg, Mathematical Programming, 1, pag. 71-76, 1995. <DOI: 10.1007/BF01592245>.
	     Se usan algoritmos generales de optimizacion global para resolver el problema, junto con el metodo adhoc Weiszfeld, vease "Sur le point pour lequel la Somme des distance de n points donnes est minimum", por Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pag. 355-386, 1937 o "On the point for which the sum of the distances to n given points is minimum", por E. Weiszfeld y F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.
A big data version for fitting cumulative probability models using the orm() function.  See Liu et al. (2017) <DOI:10.1002/sim.7433> for details.
An extension to the Regression Modeling Strategies package that
    facilitates plotting ordinal regression  model predictions together with
    confidence intervals for each dependent variable level. 
    It also adds a functionality to plot the  model summary as a modifiable 
    object.
Data input/output functions for data that conform to the
    Digital Imaging and Communications in Medicine (DICOM) standard, part
    of the Rigorous Analytics bundle.
Functions for the input/output and visualization of
    medical imaging data that follow either the 'ANALYZE', 'NIfTI' or 'AFNI'
    formats.  This package is part of the Rigorous Analytics bundle.
Image analysis techniques for positron emission tomography
    (PET) that form part of the Rigorous Analytics bundle.
A data set package with the "Orsi" and "Park/Durand" fronts as
    'SpatialLinesDataFrame' objects. The Orsi et al. (1995) fronts are published at
    the Southern Ocean Atlas Database Page, and the Park et al. (2019) fronts are published at the 'SEANOE' 
    Altimetry-derived Antarctic Circumpolar Current fronts page, please see package CITATION for details.
Convert odds ratio to relative risk in cohort studies with partial data information (Wang (2013) <doi:10.18637/jss.v055.i05>).
A modified version of alternating logistic regressions (ALR) with estimation based on orthogonalized residuals (ORTH) is implemented, which use paired estimating equations to jointly estimate parameters in marginal mean and within-association models. The within-cluster association between ordinal responses is modeled by global pairwise odds ratios (POR). A finite-sample bias correction is provided to POR parameter estimates based on matrix multiplicative adjusted orthogonalized residuals (MMORTH) for correcting estimating equations, and different bias-corrected variance estimators such as BC1, BC2, and BC3.
Tools to analyze and infer orthology and paralogy relationships between glutamine synthetase proteins in seed plants.
Utilize an orthogonality constrained optimization algorithm of
    Wen & Yin (2013) <DOI:10.1007/s10107-012-0584-1> to solve a variety of
    dimension reduction problems in the semiparametric framework, such as
    Ma & Zhu (2012) <DOI:10.1080/01621459.2011.646925>, Ma & Zhu (2013) 
    <DOI:10.1214/12-AOS1072>, Sun, Zhu, Wang & Zeng (2019) <DOI:10.1093/biomet/asy064>
    and Zhou, Zhu & Zeng (2021) <DOI:10.1093/biomet/asaa087>. The package also
    implements some existing dimension reduction methods such as hMave by Xia, Zhang,
    & Xu (2010) <DOI:10.1198/jasa.2009.tm09372> and partial SAVE by Feng, Wen & Zhu (2013)
    <DOI:10.1080/01621459.2012.746065>. It also serves as a general purpose 
    optimization solver for problems with orthogonality constraints, i.e., in Stiefel 
    manifold. Parallel computing for approximating the gradient is enabled through 'OpenMP'.
Represents the basis functions for B-splines in a simple matrix
    formulation that facilitates, taking integrals, derivatives, and
    making orthogonal the basis functions.
Implements the orthogonal reparameterization
    approach recommended by Lancaster (2002) to estimate dynamic panel
    models with fixed effects (and optionally: panel specific
    intercepts). The approach uses a likelihood-based estimator and
    produces estimates that are asymptotically unbiased as N goes to
    infinity, with a T as low as 2.
A collection of functions to construct sets of orthogonal
        polynomials and their recurrence relations. Additional
        functions are provided to calculate the derivative, integral,
        value and roots of lists of polynomial objects.
Connects to Google cloud vision <https://cloud.google.com/vision> to perform label detection and repurpose this feature for image classification.
A first implementation of automated parsing of user stories, when used 
  to defined functional requirements for operational research mathematical 
  models. It allows reading user stories, splitting them on the who-what-why template,
  and classifying them according to the parts of the mathematical model that 
  they represent. Also provides semantic grouping of stories, 
  for project management purposes.
Microarray probe ID is not convenient for further enrichment analysis and target gene selection.
    The package is created for the rice microarray probe ID conversion.
    This package can convert microarray probe ID from GPL6864 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL6864>, GPL8852 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL8852>, and GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" <https://rapdb.dna.affrc.go.jp> is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research.
    For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes.
    Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID.
Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for a matrix, raster and single coordinates on a plain (Euclidean distance) or on a sphere (great-circle or orthodromic distance).
Optimal Subset Cardinality Regression (OSCAR) models offer
    regularized linear regression using the L0-pseudonorm, conventionally
    known as the number of non-zero coefficients. The package estimates an
    optimal subset of features using the L0-penalization via
    cross-validation, bootstrapping and visual diagnostics. Effective
    Fortran implementations are offered along the package for finding
    optima for the DC-decomposition, which is used for transforming the
    discrete L0-regularized optimization problem into a continuous
    non-convex optimization task. These optimization modules include DBDC
    ('Double Bundle method for nonsmooth DC optimization' as described in
    Joki et al. (2018) <doi:10.1137/16M1115733>) and LMBM ('Limited
    Memory Bundle Method for large-scale nonsmooth optimization' as
    in Haarala et al. (2004) <doi:10.1080/10556780410001689225>). The
    OSCAR models are comprehensively exemplified in Halkola et al. (2023) 
    <doi:10.1371/journal.pcbi.1010333>). Multiple regression model families
    are supported: Cox, logistic, and Gaussian.
The supplied code allows for the generation of discrete time series of oscillating species. General shapes can be selected by means of individual functions, which are widely customizable by means of function arguments. All code was developed in the Biological Information Processing Group at the BioQuant Center at Heidelberg University, Germany.
Functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, <doi:10.1007/s00180-017-0713-7> and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, <arXiv:1703.05157>.
Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone.
Ordnance Survey ('OS') is the national mapping agency for Great 
    Britain and produces a large variety of mapping and geospatial products. 
    Much of OS's data is available via the OS Data Hub <https://osdatahub.os.uk/>, 
    a platform that hosts both free and premium data products. 'osdatahub' 
    provides a user-friendly way to access, query, and download these data.
A suite of functions for the design of case-control and two-phase studies, and the analysis of data that arise from them. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model. For additional detail see: Haneuse S, Saegusa T and Lumley T (2011)<doi:10.18637/jss.v043.i11>.
Provides routines for finding an Optimal System of Distinct Representatives (OSDR),
 as defined by D.Gale (1968) <doi:10.1016/S0021-9800(68)80039-0>.
Methods to generate a design in the input space that sequentially fills the output space of a black-box function. The output space-filling designs are helpful in inverse design or feature-based modeling problems. 
             See Wang, Shangkun, Adam P. Generale, Surya R. Kalidindi, and V. Roshan Joseph. (2024), Sequential designs for filling output spaces, Technometrics, 66, 65–76. for details. This work is supported by U.S. National Foundation grant CMMI-1921646.
An interface for interacting with 'OSF' (<https://osf.io>). 'osfr'
    enables you to access open research materials and data, or create and
    manage your own private or public projects.
Expands quoted language by recursively replacing any symbol that
    points to quoted language with the language it points to.  The recursive
    process continues until only symbols that point to non-language objects
    remain.  The resulting quoted language can then be evaluated normally.  This
    differs from the traditional 'quote'/'eval' pattern because it resolves
    intermediate language objects that would interfere with evaluation.
Aims to support all features of the system credential store,
    including non-portable ones. Supports 'Keychain' on 'macOS', and
    'Credential Manager' on 'Windows'. See the 'keyring' package if you
    need a portable 'API'.
Function library for the identification and separation of exponentially
    decaying signal components in continuous-wave optically stimulated luminescence measurements.
    A special emphasis is laid on luminescence dating with quartz, which is known for
    systematic errors due to signal components with unequal physical behaviour.
    Also, this package enables an easy to use signal decomposition of
    data sets imported and analysed with the R package 'Luminescence'.
    This includes the optional automatic creation of HTML reports. Further information and tutorials
    can be found at <https://luminescence.de>.
Interface to 'OpenStreetMap API' for fetching and saving data
    from/to the 'OpenStreetMap' database
    (<https://wiki.openstreetmap.org/wiki/API_v0.6>).
Classify Open Street Map (OSM) features into meaningful functional 
    or analytical categories. Designed for OSM PBF files, e.g. from <https://download.geofabrik.de/> 
    imported as spatial data frames. A classification consists of a list of categories that are related to 
    certain OSM tags and values. Given a layer from an OSM PBF file and a classification, the main 
    osm_classify() function returns a classification data table giving, for each feature, the primary and 
    alternative categories (if there is overlap) assigned, and the tag(s) and value(s) matched on. 
    The package also contains a classification of OSM features by economic function/significance, 
    following Krantz (2023) <https://www.ssrn.com/abstract=4537867>.
Download and import of 'OpenStreetMap' ('OSM') data as 'sf'
    or 'sp' objects.  'OSM' data are extracted from the 'Overpass' web
    server (<https://overpass-api.de/>) and processed with very fast 'C++'
    routines for return to 'R'.
Match, download, convert and import Open Street Map data extracts 
    obtained from several providers.
Functionality to handle and project lat-long coordinates, easily download background maps
    and add a correct scale bar to 'OpenStreetMap' plots in any map projection.
A novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization.
    To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge
    that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was
    introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri-
    Factorization" which will be submitted to BBRC.
Provides bindings to the 'OSQP' solver. The 'OSQP' solver is a numerical optimization package or solving convex quadratic programs written in 'C' and based on the alternating direction method of multipliers. See <doi:10.48550/arXiv.1711.08013> for details.
An interface between R and the 'OSRM' API. 'OSRM' is a routing
    service based on 'OpenStreetMap' data. See <http://project-osrm.org/> for more
    information. This package enables the computation of routes, trips, isochrones and
    travel distances matrices (travel time and kilometric distance).
Wrapper around the 'Open Source Routing Machine (OSRM)'
        API <http://project-osrm.org/>. 'osrmr' works with API versions
        4 and 5 and can handle servers that run locally as well as the
        'OSRM' webserver.
A system for calculating the minimum total sample size needed to achieve a prespecified power or the optimal allocation for each treatment group with a fixed total sample size to maximize the power.
Identifies the optimal transformation of a surrogate marker and estimates the proportion of treatment explained (PTE) by the optimally-transformed surrogate at an earlier time point when the primary outcome of interest is a censored time-to-event outcome; details are described in Wang et al (2021) <doi:10.1002/sim.9185>.
O-statistics, or overlap statistics, measure the degree of community-level trait overlap. 
    They are estimated by fitting nonparametric kernel density functions to each species’ trait 
    distribution and calculating their areas of overlap. For instance, the median pairwise overlap 
    for a community is calculated by first determining the overlap of each species pair 
    in trait space, and then taking the median overlap of each species pair in a community. 
    This median overlap value is called the O-statistic (O for overlap).
    The Ostats() function calculates separate univariate overlap statistics for each trait, 
    while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits. 
    O-statistics can be evaluated against null models to obtain standardized effect sizes. 
    'Ostats' is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale 
    drivers of biodiversity across the National Ecological Observatory Network (NEON)." 
    For more information on this project, see the Macrosystems Biodiversity Website 
    (<https://neon-biodiversity.github.io/>). Calculation of O-statistics is described in
    Read et al. (2018) <doi:10.1111/ecog.03641>, and a teaching module for introducing the
    underlying biological concepts at an undergraduate level is described in Grady et al.
    (2018) <http://tiee.esa.org/vol/v14/issues/figure_sets/grady/abstract.html>.
Function for growing survival trees ensemble ('Naz Gul', 'Nosheen Faiz', 'Dan Brawn', 'Rafal Kulakowski', 'Zardad Khan', and 'Berthold Lausen' (2020) <arXiv:2005.09043>) is given. 
             The trees are grown by the method of random survival forest ('Marvin Wright', 'Andreas Ziegler' (2017) <doi:10.18637/jss.v077.i01>). 
             The survival trees grown are assessed for both individual and collective performances. 
             The ensemble can give promising results on fewer survival trees selected in the final ensemble.
Inspired by 'S-PLUS' function objects.summary(), provides a
    function with the same name that returns data class, storage mode,
    mode, type, dimension, and size information for R objects in the
    specified environment. Various filtering and sorting options are also
    proposed.
Interact seamlessly with Open Target Genetics' GraphQL endpoint to query and retrieve tidy data tables, facilitating the analysis of genetic data. For more information about the Open Target Genetics API (<https://genetics.opentargets.org/api>).
Providing mean partition for ensemble clustering by optimal transport alignment(OTA), uncertainty measures for both partition-wise and cluster-wise assessment and multiple visualization functions to show uncertainty, for instance, membership heat map and plot of covering point set. A partition refers to an overall clustering result. Jia Li, Beomseok Seo, and Lin Lin (2019) <doi:10.1002/sam.11418>. Lixiang Zhang, Lin Lin, and Jia Li (2020) <doi:10.1093/bioinformatics/btaa165>.
Functions for creating ensembles of optimal trees for regression, classification (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). (2019) <doi:10.1007/s11634-019-00364-9>) and class membership probability estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A, Adler, W & Lausen, B (2016) <doi:10.1007/978-3-319-25226-1_34>) are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Three different methods of tree selection for the case of classification are given. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data.
Sample from the limiting distributions of empirical Wasserstein
    distances under the null hypothesis and under the alternative. Perform a 
    two-sample test on multivariate data using these limiting distributions and 
    binning.
Generating and validating One-time Password based on 
    Hash-based Message Authentication Code (HOTP) 
    and Time Based One-time Password (TOTP)
    according to RFC 4226 <https://datatracker.ietf.org/doc/html/rfc4226> and
    RFC 6238 <https://datatracker.ietf.org/doc/html/rfc6238>.
A wrapper for the 'OpenTripPlanner' <http://www.opentripplanner.org/>
    REST API. Queries are submitted to the relevant 'OpenTripPlanner' API resource, the response
    is parsed and useful R objects are returned.
In the context of data fusion, the package provides a set of functions dedicated to the solving of 'recoding problems' using optimal transportation theory (Gares, Guernec, Savy (2019) <doi:10.1515/ijb-2018-0106> and Gares, Omer (2020) <doi:10.1080/01621459.2020.1775615>). From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed.
Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform  distribution covering the whole Euclidean space. Parameters are estimated by  (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <https://jmlr.org/papers/v18/16-382.html>.
Provide methods for estimating optimal treatment regimes in survival contexts 
    with Kaplan-Meier-like estimators when no unmeasured confounding assumption is 
    satisfied (Jiang, R., Lu, W., Song, R., and Davidian, M. (2017) <doi:10.1111/rssb.12201>) 
    and when no unmeasured confounding assumption fails to hold and a binary instrument 
    is available (Xia, J., Zhan, Z., Zhang, J. (2022) <arXiv:2210.05538>).
A penalized regression framework that can simultaneously estimate 
    the optimal treatment strategy and identify important variables. 
    Appropriate for either censored or uncensored continuous response.
An implementation of several functions for feature extraction in 
    ordinal time series datasets. Specifically, some of the features proposed by
    Weiss (2019) <doi:10.1080/01621459.2019.1604370> can be computed.  
    These features can be used to perform inferential tasks or to feed machine
    learning algorithms for ordinal time series, among others. The package also includes some
    interesting datasets containing financial time series. Practitioners from a 
    broad variety of fields could benefit from the general framework provided 
    by 'otsfeatures'.
An R autograding extension for Otter-Grader (<https://otter-grader.readthedocs.io>). It supports 
    grading R scripts, R Markdown documents, and R Jupyter Notebooks.
Tools for converting Open-Source Tools for Training Resources
    (OTTR) courses into Leanpub or Coursera courses. 'ottrpal' is for use
    with the OTTR Template repository to create courses.
Summarizes the taxonomic composition, diversity contribution of the rare and abundant community by using OTU (operational taxonomic unit) table which was generated by analyzing pipeline of 'QIIME' or 'mothur'. The rare biosphere in this package is subset by the relative abundance threshold (for details about rare biosphere please see Lynch and Neufeld (2015) <doi:10.1038/nrmicro3400>).
Analyses of OTU tables produced by 16S rRNA gene amplicon sequencing, as well as example data. It contains the data and scripts used in the paper Linz, et al. (2017) "Bacterial community composition and dynamics spanning five years in freshwater bog lakes," <doi: 10.1128/mSphere.00169-17>.
Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.
The Open University Learning Analytics Dataset (OULAD) is available from 
    Kuzilek et al. (2017) <doi:10.1038/sdata.2017.171>.  The 'ouladFormat' 
    package loads, cleans and formats the OULAD for data analysis (each row of the
    returned data set is an individual student). The package’s main function,
    combined_dataset(), allows the user to choose whether the returned
    data set includes assessment, demographics, virtual learning environment (VLE),
    or registration variables etc.
Bayesian reconstruction of disease outbreaks using epidemiological
    and genetic information. Jombart T, Cori A, Didelot X, Cauchemez S, Fraser
    C and Ferguson N. 2014. <doi:10.1371/journal.pcbi.1003457>. Campbell, F,
    Cori A, Ferguson N, Jombart T. 2019. <doi:10.1371/journal.pcbi.1006930>.
Empirical or simulated disease outbreak data, provided either as
    RData or as text files.
Standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate. These outcome rates allow survey researchers to measure the quality of survey data using definitions published by the American Association of Public Opinion Research (AAPOR). For details on these standards, see AAPOR (2016) <https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx>.
Many treatment effect estimators can be written as weighted outcomes.
    These weights have established use cases like checking covariate balancing via packages like 'cobalt'.
    This package takes the original estimator objects and outputs these outcome weights.
    It builds on the general framework of Knaus (2024) <doi:10.48550/arXiv.2411.11559>.
    This version is compatible with the 'grf' package and provides an internal implementation of Double Machine Learning.
Provides a random forest based implementation of the method
    described in Chapter 7.1.2 (Regression model based anomaly detection)
    of Chandola et al. (2009) <doi:10.1145/1541880.1541882>. It works as
    follows: Each numeric variable is regressed onto all other variables
    by a random forest. If the scaled absolute difference between observed
    value and out-of-bag prediction of the corresponding random forest is
    suspiciously large, then a value is considered an outlier. The package
    offers different options to replace such outliers, e.g. by realistic
    values found via predictive mean matching. Once the method is trained
    on a reference data, it can be applied to new data.
Ensemble functions for outlier/anomaly detection. There is a new ensemble method proposed using 
    Item Response Theory. Existing outlier ensemble methods from Schubert et al (2012) <doi:10.1137/1.9781611972825.90>, 
    Chiang et al (2017) <doi:10.1016/j.jal.2016.12.002> and Aggarwal and Sathe (2015) <doi:10.1145/2830544.2830549>
    are also included.
A collection of some tests commonly used for identifying
        outliers.
Programs for detecting and cleaning outliers in single time series and in time series from homogeneous and heterogeneous databases using an Orthogonal Greedy Algorithm (OGA) for saturated linear regression models. The programs implement the procedures presented in the paper entitled "Efficient outlier detection in heterogeneous time series databases" by Pedro Galeano, Daniel Peña and Ruey S. Tsay (2024), working paper, Universidad Carlos III de Madrid.
Provides implementations of some of the most important outlier detection algorithms. 
    Includes a tutorial mode option that shows a description of each algorithm and provides 
    a step-by-step execution explanation of how it identifies outliers from the given data 
    with the specified input parameters. References include the works of Azzedine Boukerche, 
    Lining Zheng, and Omar Alfandi (2020) <doi:10.1145/3381028>, Abir Smiti (2020) 
    <doi:10.1016/j.cosrev.2020.100306>, and Xiaogang Su, Chih-Ling Tsai (2011) 
    <doi:10.1002/widm.19>.
Potential outliers are identified for all combinations of a dataset's variables. O3 plots are described in Unwin(2019) <doi:10.1080/10618600.2019.1575226>. The available methods are HDoutliers() from the package 'HDoutliers', FastPCS() from the package 'FastPCS', mvBACON() from 'robustX', adjOutlyingness() from 'robustbase', DectectDeviatingCells() from 'cellWise', covMcd() from 'robustbase'.
Outlier detection method that flags suspicious values within observations,
  constrasting them against the normal values in a user-readable format, potentially
  describing conditions within the data that make a given outlier more rare.
  Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>.
  Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.
Provides a method to find the outlier in custom data by quantile random forests method. Introduced by Meinshausen Nicolai (2006) <https://dl.acm.org/doi/10.5555/1248547.1248582>. It directly calls the ranger() function of the 'ranger' package to perform data fitting and prediction. We also implement the evaluation of outlier prediction results. Compared with random forest detection of outliers, this method has higher accuracy and stability on large datasets.
Create regression tables for publication.
    Currently supports 'lm', 'glm', 'survreg', and 'ivreg' outputs.
An approach to outlier detection in RNA-seq and related data
    based on five statistics. 'OutSeekR' implements an outlier test by comparing
    the distributions of these statistics in observed data with those of
    simulated null data.
Estimates rates for continuous character evolution under Brownian motion and a new set of Ornstein-Uhlenbeck based Hansen models that allow both the strength of the pull and stochastic motion to vary across selective regimes. Beaulieu et al (2012).
Detection of overdispersion in count data for multiple regression analysis.
    Log-linear count data regression is one of the most popular techniques for predictive 
    modeling where there is a non-negative discrete quantitative dependent variable. In 
    order to ensure the inferences from the use of count data models are appropriate, 
    researchers may choose between the estimation of a Poisson model and a negative binomial
    model, and the correct decision for prediction from a count data estimation is directly
    linked to the existence of overdispersion of the dependent variable, conditional to the 
    explanatory variables. Based on the studies of Cameron and Trivedi (1990)
    <doi:10.1016/0304-4076(90)90014-K> and Cameron and Trivedi (2013, ISBN:978-1107667273), 
    the overdisp() command is a contribution to researchers, providing a fast and secure 
    solution for the detection of overdispersion in count data. Another advantage is that 
    the installation of other packages is unnecessary, since the command runs in the basic 
    R language.
Provides functions to fit kernel density functions to
    data on temporal activity patterns of animals; estimate coefficients
    of overlapping of densities for two species; and calculate bootstrap
    estimates of confidence intervals. As in Ridout and Linkie (2009) <doi:10.1198/jabes.2009.08038>.
Functions for estimating the overlapping area of two or more kernel density estimations from empirical data.
Tests the observed overlapping polygon area in a collection of polygons against a null model of random rotation, as explained in De la Cruz et al. (2017) <doi:10.13140/RG.2.2.12825.72801>.
Simplifies MCMC setup by automatically looping through sampling 
    functions and saving the results.  Reduces the memory footprint of running 
    MCMC and saves samples to disk as the chain runs.  Allows samples from the 
    chain to be analyzed while the MCMC is still running.  Provides functions 
    for commonly performed operations such as calculating Metropolis acceptance 
    ratios and creating adaptive Metropolis samplers.  References: Roberts and 
    Rosenthal (2009) <doi:10.1198/jcgs.2009.06134>.
Overture Maps offers free and open geospatial map data sourced from various providers and standardized to a common schema. This tool allows you to download Overture Maps data for a specific region of interest and convert it to several different file formats. For more information, visit <https://overturemaps.org/download/>.
An integrated R interface to the 'Overture' API 
  (<https://docs.overturemaps.org/>). Allows R users to return 'Overture' data as 
  'dbplyr' data frames or materialized 'sf' spatial data frames.
Makes it easy to display descriptive information on a data
    set.  Getting an easy overview of a data set by displaying and
    visualizing sample information in different tables (e.g., time and
    scope conditions).  The package also provides publishable 'LaTeX' code
    to present the sample information.
Provides functions to construct confidence intervals for the Overlap Coefficient (OVL). OVL measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. Implements methods based on the work of Franco-Pereira, A.M., Nakas, C.T., Reiser, B., and Pardo, M.C. (2021) <doi:10.1177/09622802211046386>.
This tool was designed to assess the sensitivity of research findings to omitted variables when estimating causal effects using propensity score (PS) weighting. This tool produces graphics and summary results that will enable a researcher to quantify the impact an omitted variable would have on their results. Burgette et al. (2021) describe the methodology behind the primary function in this package, ov_sim. The method is demonstrated in Griffin et al. (2020) <doi:10.1016/j.jsat.2020.108075>.
Open the current working directory (or a given directory path) in your computer's file manager.
Tools for collecting municipal-level data <http://www.transparencia.gov.br/swagger-ui.html> from several Brazilian governmental social programs.
An implementation of optimal weight exchange algorithm Yang(2013) <doi:10.1080/01621459.2013.806268> for three models.
    They are Crossover model with subject dropout, crossover model with proportional
    first order residual effects and interference model. You can use it to find either
    A-opt or D-opt approximate designs. Exact designs can be automatically rounded
    from approximate designs and relative efficiency is provided as well.
Evaluates the Owen Q-function for an integer value of the
    degrees of freedom, by applying Owen's algorithm (1965)
    <doi:10.1093/biomet/52.3-4.437>.  It is useful for the calculation of
    the power of equivalence tests.
Import data from 'Our World in Data', an organisation which publishes research and data on global economic and social issues.
Accesses OpenWeatherMap's (owm) <https://openweathermap.org/> API.
   'owm' itself is a service providing weather data in the past, in the future and now.
   Furthermore, 'owm' serves weather map layers usable in frameworks like 'leaflet'.
   In order to access the API, you need to sign up for an API key. There are free and paid plans.
   Beside functions for fetching weather data from 'owm', 'owmr' supplies
   tools to tidy up fetched data (for fast and simple access) and to show it on leaflet maps.
Provides an Interface to Web-Services defined as standards by the Open Geospatial Consortium (OGC), including Web Feature Service
 (WFS) for vector data, Web Coverage Service (WCS), Catalogue Service (CSW) for ISO/OGC metadata, Web Processing Service (WPS) for data processes,
 and associated standards such as the common web-service specification (OWS) and OGC Filter Encoding. Partial support is provided for the Web Map 
 Service (WMS). The purpose is to add support for additional OGC service standards such as Web Coverage Processing Service (WCPS), the Sensor 
 Observation Service (SOS), or even new standard services emerging such OGC API or SensorThings.
Short hand if-else function to easily switch the values depending
  on a logical condition.
A set of tools that enables using 'OxCal' from within R. 'OxCal' (<https://c14.arch.ox.ac.uk/oxcal.html>) is a standard archaeological tool intended to provide 14C calibration and analysis of archaeological and environmental chronological information. 'OxcAAR' allows simple calibration with 'Oxcal' and plotting of the results as well as the execution of sophisticated ('OxCal') code and the import of the results of bulk analysis and complex Bayesian sequential calibration.
Provides utilities for processing of Oxy-Bisulfite microarray data 
  (e.g. via the Illumina Infinium platform, <http://www.illumina.com>) 
  with tandem arrays, one using conventional
  bisulfite conversion, the other using oxy-bisulfite conversion.
Collects a list of your third party R packages, and
    scans them with the 'OSS' Index provided by 'Sonatype', reporting back
    on any vulnerabilities that are found in the third party packages you
    use.
Functions for plotting Australia's coastline and state
  boundaries.
Data on the most popular baby names by sex and year, and for each state in Australia, as provided by the state and territory governments. The quality and quantity of the data varies with the state.
Maps of Australian coastline and administrative regions. Data 
 can be drawn or accessed directly as simple features objects. Includes
 simple functions for country or state maps of Australia and in-built data
 sets of administrative regions from the Australian Bureau of Statistics 
 <https://www.abs.gov.au/>. Layers include electoral divisions and local 
 government areas, simplified from the original sources but with sufficient 
 detail to allow mapping of a local municipality.
Contains functions for data preparation,
	prediction of transition probabilities,
	estimating semi-parametric regression models
	and for implementing nonparametric estimators
	for other quantities. See Meira-Machado and
	Roca-Pardiñas (2011) <doi:10.18637/jss.v038.i03>.
It provides tools for conducting performance attribution for equity portfolios. The package uses two methods: the Brinson method and a regression-based analysis.
Precision agriculture spatial data
 depuration and homogeneous zones (management zone) delineation.
 The package includes functions that performs protocols for data cleaning
 management zone delineation and zone comparison; protocols are described in 
 Paccioretti et al., (2020) <doi:10.1016/j.compag.2020.105556>.
Implements partition-assisted clustering and multiple alignments of networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set. Please see Li et al. (2017) <doi:10.1371/journal.pcbi.1005875> for detail method description.
Functions to easily convert data to binary formats other programs/machines can understand.
It provides utility functions for investigating changes within R
    packages. The pkgInfo() function extracts package information such as
    exported and non-exported functions as well as their arguments. The
    pkgDiff() function compares this information for two versions of a package
    and creates a diff file viewable in a browser.
Search for R packages on CRAN directly from the R console, based on the packages' titles, short and long descriptions, or other fields. Combine multiple keywords with logical operators ('and', 'or'), view detailed information on any package and keep track of the latest package contributions to CRAN. If you don't want to search from the R console, use the comfortable R Studio add-in.
Provides essential checklists for R package developers, whether
    you're creating your first package or beginning a new project. This tool
    guides you through each step of the development process, including specific
    considerations for submitting your package to the Comprehensive R Archive
    Network (CRAN). Simplify your workflow and ensure adherence to best
    practices with 'packagepal'.
Helper functions for package creation, building and
    maintenance. Designed to work with a build system such as 'GNU make' or
    package 'fakemake' to help you to conditionally work through the stages of
    package development (such as spell checking, linting, testing, before
    building and checking a package).
Compute and visualize the cross-sectional and longitudinal number
    and rank percentile of package downloads from Posit/RStudio's CRAN mirror.
Algorithms to find arrangements of non-overlapping circles.
Enforces good practice and provides convenience functions to make work with 'JavaScript' 
  not just easier but also scalable. It is a robust wrapper to 'NPM', 'yarn', and 'webpack' that 
  enables to compartmentalize 'JavaScript' code, leverage 'NPM' and 'yarn' packages, include
  'TypeScript', 'React', or 'Vue' in web applications, and much more.
Various useful functions for statisticians: describe data, plot Kaplan-Meier curves with numbers of subjects at risk, compare data sets, display spaghetti-plot, build multi-contingency tables...
Several functions are provided to implement a MBPLSDA : components search, optimal model components number search, optimal model validity test by permutation tests, observed values evaluation of optimal model parameters and predicted categories, bootstrap values evaluation of optimal model parameters and predicted cross-validated categories. The use of this package is described in Brandolini-Bunlon et al (2019. Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data. Metabolomics, 15(10):134).
Manage the R packages your project depends on in an isolated,
    portable, and reproducible way.
An implementation of both the equality and inequality constrained lasso
    functions for the algorithm described in "Penalized and Constrained Optimization"
    by James, Paulson, and Rusmevichientong (Journal of the American Statistical Association, 2019;
    see <http://www-bcf.usc.edu/~gareth/research/PAC.pdf> for a full-text version of the paper). 
    The algorithm here is designed to allow users to define linear constraints (either equality
    or inequality constraints) and use a penalized regression approach to solve the constrained
    problem. The functions here are used specifically for constraints with the lasso formulation,
    but the method described in the PaC paper can be used for a variety of scenarios. In addition
    to the simple examples included here with the corresponding functions, complete code to 
    entirely reproduce the results of the paper is available online through the Journal of the
    American Statistical Association.
Tools to more conveniently perform tasks associated with
        add-on packages. pacman conveniently wraps library and package
        related functions and names them in an intuitive and consistent
        fashion.  It seeks to combine functionality from lower level
        functions which can speed up workflow.
Procrustes analyses to infer co-phylogenetic
    matching between pairs of phylogenetic trees.
Routines for two different test types, the Constant Conditional Correlation (CCC) test and the Vectorial Independence (VI) test are provided (Kurz and Spanhel (2022) <doi:10.1214/22-EJS2051>). The tests can be applied to check whether a conditional copula coincides with its partial copula. Functions to test whether a regular vine copula satisfies the so-called simplifying assumption or to test a single copula within a regular vine copula to be a (j-1)-th order partial copula are available. The CCC test comes with a decision tree approach to allow testing in high-dimensional settings.
Supplementary utils for CRAN maintainers and R packages developers.
  Validating the library, packages and lock files.
  Exploring a complexity of a specific package like evaluating its size in bytes with all dependencies.
  The shiny app complexity could be explored too.
  Assessing the life duration of a specific package version.
  Checking a CRAN package check page status for any errors and warnings.
  Retrieving a DESCRIPTION or NAMESPACE file for any package version. 
  Comparing DESCRIPTION or NAMESPACE files between different package versions.
  Getting a list of all releases for a specific package.
  The Bioconductor is partly supported.
Run Paris Agreement Capital Transition Assessment ('PACTA') analyses
    on multiple loan books in a structured way. Provides access to standard
    'PACTA' metrics and additional 'PACTA'-related metrics for multiple loan books.
    Results take the form of 'csv' files and plots and are exported to
    user-specified project paths.
Support for a variety of commonly used precision agriculture operations. Includes functions to download and process raw satellite images from Sentinel-2 <https://documentation.dataspace.copernicus.eu/APIs/OData.html>. Includes functions that download vegetation index statistics for a given period of time, without the need to download the raw images <https://documentation.dataspace.copernicus.eu/APIs/SentinelHub/Statistical.html>. There are also functions to download and visualize weather data in a historical context. Lastly, the package also contains functions to process yield monitor data. These functions can build polygons around recorded data points, evaluate the overlap between polygons, clean yield data, and smooth yield maps.
Visualizes the coverage depth of a complete plastid genome as well as the equality of its inverted repeat regions in relation to the circular, quadripartite genome structure and the location of individual genes. For more information, please see Gruenstaeudl and Jenke (2020) <doi:10.1186/s12859-020-3475-0>.
Given a vector of Taylor series coefficients of sufficient length
    as input, the function returns the numerator and denominator coefficients
    for the Padé approximant of appropriate order (Baker, 1975)
    <ISBN:9780120748556>.
Transforms datetime data into a format ready for analysis.
    It offers two core functionalities; aggregating data to a higher level interval
    (thicken) and imputing records where observations were absent (pad).
Provides access to material from the book "Processing and Analyzing Financial Data with R" by Marcelo Perlin (2017) available at <https://sites.google.com/view/pafdr/home>.
Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) <doi:10.1371/journal.pone.0137796>. Thong Pham et al. (2016) <doi:10.1038/srep32558>. Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>. Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.
Provides functions to read, process and visualize pairwise sequence 
 alignments in the 'PAF' format used by 'minimap2' and other whole-genome aligners. 
 'minimap2' is described by Li H. (2018) <doi:10.1093/bioinformatics/bty191>.
Use the paged media properties in CSS and the JavaScript
  library 'paged.js' to split the content of an HTML document into discrete
  pages. Each page can have its page size, page numbers, margin boxes, and
  running headers, etc. Applications of this package include books, letters,
  reports, papers, business cards, resumes, and posters.
Quickly and easily add a mini map to your 'rmarkdown' html documents.
A simple way to add page numbers to base/ggplot/lattice graphics.
Pageview data from the 'Wikimedia' sites, such as
    'Wikipedia' <https://www.wikipedia.org/>, from entire projects to per-article
    levels of granularity, through the new RESTful API and data source <https://wikimedia.org/api/rest_v1/?doc>.
Latent group structures are a common challenge in panel data analysis. Disregarding group-level heterogeneity can introduce bias. Conversely, estimating individual coefficients for each cross-sectional unit is inefficient and may lead to high uncertainty.
    This package addresses the issue of unobservable group structures by implementing the pairwise adaptive group fused Lasso (PAGFL) by Mehrabani (2023) <doi:10.1016/j.jeconom.2022.12.002>. PAGFL identifies latent group structures and group-specific coefficients in a single step.    
    On top of that, we extend the PAGFL to time-varying coefficient functions.
Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.
Bayesian hierarchical methods for pathway analysis of genomewide association data: Normal/Bayes factors and Sparse Normal/Adaptive lasso. The Frequentist Fisher's product method is included as well.
Estimation of the number of colonization events between islands of the same archipelago for a species. It uses rarefaction curves to control for both field and genetic sample sizes as it was described in Coello et al. (2022) <doi:10.1111/jbi.14341>.
XKCD described a supposedly "bad" colormap that it called a "Painbow" (see <https://xkcd.com/2537/>). But simple tests demonstrate that under some circumstances, the colormap can perform very well, and people can find information that is difficult to detect with the ggplot2 default and even supposedly "good" colormaps like viridis. This library let's you use the Painbow in your own ggplot graphs.
Functions for creating color palettes, visualizing
    palettes, modifying colors, and assigning colors for plotting.
Color palettes generated from paintings.
Plots matrices of colours as grids of coloured squares - aka heatmaps, 
	guaranteeing legible row and column names, 
	without transformation of values, 
	without re-ordering rows or columns,
	and without dendrograms.
Many datasets and a set of graphics (based on ggplot2), statistics, effect sizes and hypothesis tests are provided for analysing paired data with S4 class.
Creates an interactive scatterplot matrix using the D3 JavaScript
    library. See <https://d3js.org/> for more information on D3.
Improving graphics by ameliorating order effects, using Eulerian tours 
    and Hamiltonian decompositions of  graphs. References for the methods presented 
    here are C.B. Hurley and R.W. Oldford (2010) <doi:10.1198/jcgs.2010.09136> and 
    C.B. Hurley and R.W. Oldford (2011) <doi:10.1007/s00180-011-0229-5>.
Performs the explicit calculation
    -- not estimation! -- of the Rasch item parameters for dichotomous and
    polytomous item responses, using a pairwise comparison approach. Person
    parameters (WLE) are calculated according to Warm's weighted likelihood
    approach.
Calculation of the parametric, nonparametric confidence intervals
 for the difference or ratio of location parameters, nonparametric confidence interval
 for the Behrens-Fisher problem and for the difference, ratio and odds-ratio of binomial
 proportions for comparison of independent samples. Common wrapper functions to split 
 data sets and apply confidence intervals or tests to these subsets.
 A by-statement allows calculation of CI separately for the levels of further factors. 
 CI are not adjusted for multiplicity.
The goal of 'pak' is to make package installation faster and
    more reliable. In particular, it performs all HTTP operations in
    parallel, so metadata resolution and package downloads are fast.
    Metadata and package files are cached on the local disk as well. 'pak'
    has a dependency solver, so it finds version conflicts before
    performing the installation. This version of 'pak' supports CRAN,
    'Bioconductor' and 'GitHub' packages as well.
Provides a comprehensive suite of tools for analyzing Pakistan's Quarterly National Accounts data. Users can gain detailed insights into Pakistan's economic performance, visualize quarterly trends, and detect patterns and anomalies in key economic indicators. Compare sector contributions—including agriculture, industry, and services—to understand their influence on economic growth or decline. Customize analyses by filtering and manipulating data to focus on specific areas of interest. Ideal for policymakers, researchers, and analysts aiming to make informed, data-driven decisions based on timely and detailed economic insights.
Provides tools for analyzing Pakistan's Population Censuses data via the 'PakPC2023' and 'PakPC2017' R packages. Designed for researchers, policymakers, and professionals, the app enables in-depth numerical and graphical analysis, including detailed cross-tabulations and insights. With diverse statistical models and visualization options, it supports informed decision-making in social and economic policy. This tool enhances users' ability to explore and interpret census data, providing valuable insights for effective planning and analysis across various fields.
Provides data sets and functions for exploration of Pakistan Population Census 2017 (<http://www.pbscensus.gov.pk/>).
Provides data sets and functions for exploration of Pakistan Population Census 2023 (<https://www.pbs.gov.pk/>).
Provides data set and functions for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Child questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Household questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Household Listing questionnaire data for Punjab, Pakistan.
Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan.
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household.
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age 5-17 questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Maternal Mortality questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Men questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
References and cites 'R' and 'R' packages on the fly in 'R
    Markdown' and 'Quarto'. 'pakret' provides a minimalistic API that
    generates preformatted citations of 'R' and 'R' packages, and adds
    their reference to a '.bib' file directly from within your document.
Several tests of quantitative palaeoenvironmental reconstructions 
  from microfossil assemblages, including the null model tests of the 
  statistically significant of reconstructions developed by Telford and Birks
  (2011) <doi:10.1016/j.quascirev.2011.03.002>, and tests of the effect of 
  spatial autocorrelation on transfer function model performance using methods 
  from Telford and Birks (2009) <doi:10.1016/j.quascirev.2008.12.020> and 
  Trachsel and Telford (2016) <doi:10.5194/cp-12-1215-2016>. Age-depth models with 
  generalized mixed-effect regression from Heegaard et al (2005)
  <doi:10.1191/0959683605hl836rr> are also included.
Provides functionality to support data preparation and exploration for
  palaeobiological analyses, improving code reproducibility and accessibility. The
  wider aim of 'palaeoverse' is to bring the palaeobiological community together 
  to establish agreed standards. The package currently includes functionality for 
  data cleaning, binning (time and space), exploration, summarisation and 
  visualisation. Reference datasets (i.e. Geological Time Scales <https://stratigraphy.org/chart>)
  and auxiliary functions are also provided. Details can be found in:
  Jones et al., (2023) <doi: 10.1111/2041-210X.14099>.
Implements sparse regression with paired covariates (<doi:10.1007/s11634-019-00375-6>). The paired lasso is designed for settings where each covariate in one set forms a pair with a covariate in the other set (one-to-one correspondence). For the optional correlation shrinkage, install ashr (<https://github.com/stephens999/ashr>) and CorShrink (<https://github.com/kkdey/CorShrink>) from GitHub (see README).
Implementation of the Partitioned Local Depth (PaLD) 
    approach which provides a measure of local depth and the cohesion of a point 
    to another which  (together with a universal threshold for distinguishing 
    strong and weak ties) may be used to reveal local and global structure in 
    data, based on methods described in Berenhaut, Moore, and Melvin (2022)
    <doi:10.1073/pnas.2003634119>. No extraneous inputs, distributional 
    assumptions, iterative procedures nor optimization criteria are employed. 
    This package includes functions for computing local depths and cohesion as
    well as flexible functions for plotting community networks and displays of 
    cohesion against distance.
Provides functions for fitting abundance distributions over environmental gradients to the species in ecological communities, and tools for simulating the fossil assemblages from those abundance models for such communities, as well as simulating assemblages across various patterns of sedimentary history and sampling. These tools are for particular use with fossil records with detailed age models and abundance distributions used for calculating environmental gradients from ordinations or other indices based on fossil assemblages.
Includes functions to wrap most endpoints of the 'PaleobioDB'
    API and functions to visualize and process the fossil data. The API
    documentation for the Paleobiology Database can be found at
    <https://paleobiodb.org/data1.2/>.
Simulation of species diversification, fossil records, and phylogenies. While the literature on species birth-death simulators is extensive, including important software like 'paleotree' and 'APE', we concluded there were interesting gaps to be filled regarding possible diversification scenarios. Here we strove for flexibility over focus, implementing a large array of regimens for users to experiment with and combine. In this way, 'paleobuddy' can be used in complement to other simulators as a flexible jack of all trades, or, in the case of scenarios implemented only here, can allow for robust and easy simulations for novel situations. Environmental data modified from that in 'RPANDA': Morlon H. et al (2016) <doi:10.1111/2041-210X.12526>.
Contains various tools for conveniently downloading and editing taxon-specific datasets from the Paleobiology Database <https://paleobiodb.org>, extracting information on abundance, temporal distribution of subtaxa and taxonomic diversity through deep time, and visualizing these data in relation to phylogeny and stratigraphy.
Fill missing symmetrical data with mirroring, calculate Procrustes alignments with or without scaling, and compute standard or vector correlation and covariance matrices (congruence coefficients) of 3D landmarks. Tolerates missing data for all analyses.
This extension of the poems pattern-oriented modeling (POM) framework
    provides a collection of modules and functions customized for paleontological
    time-scales, and optimized for single-generation transitions and large populations,
    across multiple generations.
Provides tools for transforming, a posteriori time-scaling, and
    modifying phylogenies containing extinct (i.e. fossil) lineages. In particular,
    most users are interested in the functions timePaleoPhy, bin_timePaleoPhy,
    cal3TimePaleoPhy and bin_cal3TimePaleoPhy, which date cladograms of
    fossil taxa using stratigraphic data. This package also contains a large number
    of likelihood functions for estimating sampling and diversification rates from
    different types of data available from the fossil record (e.g. range data,
    occurrence data, etc). paleotree users can also simulate diversification and
    sampling in the fossil record using the function simFossilRecord, which is a
    detailed simulator for branching birth-death-sampling processes composed of
    discrete taxonomic units arranged in ancestor-descendant relationships. Users
    can use simFossilRecord to simulate diversification in incompletely sampled
    fossil records, under various models of morphological differentiation (i.e.
    the various patterns by which morphotaxa originate from one another), and
    with time-dependent, longevity-dependent and/or diversity-dependent rates of
    diversification, extinction and sampling. Additional functions allow users to
    translate simulated ancestor-descendant data from simFossilRecord into standard
    time-scaled phylogenies or unscaled cladograms that reflect the relationships
    among taxon units.
Facilitates analysis of paleontological sequences of trait values.  
    Functions are provided to fit, using maximum likelihood, simple 
    evolutionary models (including unbiased random walks, directional 
    evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and 
    complex models (punctuation, mode shifts).
Hexadecimal codes are typically used to represent colors in R.
    Connecting these codes to their colors requires practice or memorization.
    'palette' provides a 'vctrs' class for working with color palettes, including
    printing and plotting functions. The goal of the class is to place visual
    representations of color palettes directly on or, at least, next to their
    corresponding character representations. Palette extensions also are provided
    for data frames using 'pillar'.
The choices of color palettes in R can be quite
    overwhelming with palettes spread over many packages with many
    different API's. This packages aims to collect all color palettes
    across the R ecosystem under the same package with a streamlined API.
Streamlines the steps for adding colour scales and associated legends 
         when working with base R graphics, especially for interactive use. Popular
         palettes are included and pretty legends produced when mapping a large 
         variety of vector classes to a colour scale. An additional helper for 
         adding axes and grid lines complements the base::plot() work flow.
Provides a comprehensive library for colour vectors and colour
    palettes using a new family of colour classes (palettes_colour and
    palettes_palette) that always print as hex codes with colour previews.
    Capabilities include: formatting, casting and coercion, extraction and
    updating of components, plotting, colour mixing arithmetic, and colour
    interpolation.
A set of palettes imported from 'Gimp' distributed 
  under GPL3 (<https://www.gimp.org/about/COPYING>), and 'Inkscape' 
  distributed under GPL2 (<https://inkscape.org/about/license/>).
Use Pokemon(R) inspired palettes with additional 'ggplot2' scales.
    Palettes are the colours in each Pokemon's sprite, ordered by how common
    they are in the image. The first 386 Pokemon are currently provided.
R package to compute Incoming Solar Radiation (insolation) for palaeoclimate studies. Features three solutions: Berger (1978), Berger and Loutre (1991) and Laskar et al. (2004). Computes daily-mean, season-averaged and annual means and for all latitudes, and polar night dates.
Functions to fit point process models using the Palm likelihood. First proposed by Tanaka, Ogata, and Stoyan (2008) <DOI:10.1002/bimj.200610339>, maximisation of the Palm likelihood can provide computationally efficient parameter estimation for point process models in situations where the full likelihood is intractable. This package is chiefly focused on Neyman-Scott point processes, but can also fit the void processes proposed by Jones-Todd et al. (2019) <DOI:10.1002/sim.8046>. The development of this package was motivated by the analysis of capture-recapture surveys on which individuals cannot be identified---the data from which can conceptually be seen as a clustered point process (Stevenson, Borchers, and Fewster, 2019 <DOI:10.1111/biom.12983>). As such, some of the functions in this package are specifically for the estimation of cetacean density from two-camera aerial surveys.
Size measurements, clutch observations, and blood isotope ratios for adult foraging Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer Archipelago near Palmer Station, Antarctica. Data were collected and made available by Dr. Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) Program.
'Google Pathways Language Model 2 (PaLM 2)' as a coding and writing assistant designed for 'R'. With a range of functions, including natural language processing and coding optimization, to assist 'R' developers in simplifying tedious coding tasks and content searching.
This is an implementation of model-based trees with global model
  parameters (PALM trees). The PALM tree algorithm is an extension to the MOB
  algorithm (implemented in the 'partykit' package), where some parameters are
  fixed across all groups. Details about the method can be found in Seibold,
  Hothorn, Zeileis (2016) <arXiv:1612.07498>. The package offers coef(),
  logLik(), plot(), and predict() functions for PALM trees.
Colour palettes for data, based on some well known public data
    sets. Includes helper functions to map absolute values to known palettes, and 
    capture the work of image colour mapping as raster data sets.
A comprehensive collection of color palettes, colormaps, and
    tools to evaluate them. See Kovesi (2015) <doi:10.48550/arXiv.1509.03700>.
Processing Chlorophyll Fluorescence & P700 Absorbance data generated by WALZ hardware. Four models are provided for the regression of Pi curves, which can be compared with each other in order to select the most suitable model for the data set. Control plots ensure the successful verification of each regression. Bundled output of alpha, ETRmax, Ik etc. enables fast and reliable further processing of the data.
Functions for easily reading and processing binary data files created by
    'Pamguard' (<https://www.pamguard.org/>). All functions for directly reading the
    binary data files are based on 'MATLAB' code written by Michael Oswald.
We propose a pair of summary measures for the predictive power of a prediction
    function based on a regression model. The regression model can be linear
    or nonlinear, parametric, semi-parametric, or nonparametric, and correctly
    specified or mis-specified. The first measure, R-squared, is an extension of
    the classical R-squared statistic for a linear model, quantifying the prediction
    function's ability to capture the variability of the response. The second
    measure, L-squared, quantifies the prediction function's bias for predicting the
    mean regression function. When used together, they give a complete summary of
    the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) <arXiv:1611.03063> for more details.
Data are partitioned (clustered) into k clusters "around medoids", which is
    a more robust version of K-means implemented in the function pam() in the 'cluster' package.
    The PAM algorithm is described in Kaufman and Rousseeuw (1990) <doi:10.1002/9780470316801>.
    Please refer to the pam() function documentation for more references.
    Clustered data is plotted as a split heatmap allowing visualisation of representative
    "group-clusters" (medoids) in the data as separated fractions of the graph while those
    "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.
Simulation functions to assess or explore the power of a dataset to estimates significant random effects (intercept or slope) in a mixed model. The functions are based on the "lme4" and "lmerTest" packages.
A collection of miscellaneous functions for passive acoustics. 
    Much of the content here is adapted to R from code written by other people.
    If you have any ideas of functions to add, please contact Taiki Sakai.
The Piece-wise exponential (Additive Mixed) Model
    (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a
    powerful model class for the analysis of survival (or time-to-event) data,
    based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated, competing risks and recurrent events data.
    pammtools provides tidy workflow for survival analysis with PAMMs,
    including data simulation, transformation and other functions for data
    preprocessing and model post-processing as well as visualization.
Tools for loading and processing passive acoustic data. Read in data
    that has been processed in 'Pamguard' (<https://www.pamguard.org/>), apply a suite
    processing functions, and export data for reports or external modeling tools. Parameter 
    calculations implement methods by Oswald et al (2007) <doi:10.1121/1.2743157>,
    Griffiths et al (2020) <doi:10.1121/10.0001229> and Baumann-Pickering et al (2010)
    <doi:10.1121/1.3479549>.
Implements the Panel Data Approach Method for program evaluation as developed in Hsiao, Ching and Ki Wan (2012). pampe estimates the effect of an intervention by comparing the evolution of the outcome for a unit affected by an intervention or treatment to the evolution of the unit had it not been affected by the intervention.
Some functions for sample classification in microarrays.
A variety of tools relevant to the analysis
    of marine soundscape data. There are tools for downloading AIS (automatic identification system)
    data from Marine Cadastre <https://hub.marinecadastre.gov>,
    connecting AIS data to GPS coordinates, plotting summaries of various soundscape
    measurements, and downloading relevant environmental variables (wind, swell height) from the
    National Center for Atmospheric Research data server <https://rda.ucar.edu/datasets/ds084.1/>.
    Most tools were developed to work well with output from 'Triton' software, but can be adapted
    to work with any similar measurements.
It provides functions and examples for maximum likelihood estimation for
              generalized linear mixed models and Gibbs sampler for multivariate linear
              mixed models with incomplete data, as described in Schafer JL (1997)
              "Imputation of missing covariates under a multivariate linear mixed model".
              Technical report 97-04, Dept. of Statistics, The Pennsylvania State University.
Identification of the most appropriate pharmacotherapy for each 
    patient based on genomic alterations is a major challenge in personalized 
    oncology. 'PANACEA' is a collection of personalized anti-cancer drug 
    prioritization approaches utilizing network methods. The methods utilize 
    personalized "driverness" scores from 'driveR' to rank drugs, mapping these
    onto a protein-protein interaction network. The "distance-based" method 
    scores each drug based on these scores and distances between drugs and genes
    to rank given drugs. The "RWR" method propagates these scores via a 
    random-walk with restart framework to rank the drugs. The methods are 
    described in detail in Ulgen E, Ozisik O, Sezerman OU. 2023. PANACEA: 
    network-based methods for pharmacotherapy prioritization in personalized 
    oncology. Bioinformatics <doi:10.1093/bioinformatics/btad022>.
Provides the tools needed to benchmark the R2 value corresponding to a certain acceptable noise level while also providing a rescaling function based on that noise level yielding a new value of R2 we refer to as R2k which is independent of both the number of degrees of freedom and the noise distribution function.
Provides function for performing Bayesian survival regression using 
    Horseshoe prior in the accelerated failure time model with log normal assumption 
    in order to achieve high dimensional pan-cancer variable selection as developed in
    Maity et. al. (2019) <doi:10.1111/biom.13132>.
Full dynamic system to describe and forecast the spread and the severity
    of a developing pandemic, based on available data. These data are number of infections, 
    hospitalizations, deaths and recoveries notified each day. The system consists of three 
    transitions, infection-infection, infection-hospital and hospital-death/recovery. 
    The intensities of these transitions are dynamic and estimated using non-parametric local
    linear estimators. The package can be used to provide forecasts and survival indicators 
    such as the median time spent in hospital and the probability that a patient who has been
    in hospital for a number of days can leave it alive. Methods are described in Gámiz, 
    Mammen, Martínez-Miranda, and Nielsen (2024) <doi:10.48550/arXiv.2308.09918> and 
    <doi:10.48550/arXiv.2308.09919>.
Contains some functions catching all messages, 'stdout' and other
    useful information while evaluating R code and other helpers to return user
    specified text elements (like: header, paragraph, table, image, lists etc.)
    in 'pandoc' markdown or several type of R objects similarly automatically
    transformed to markdown format. Also capable of exporting/converting (the
    resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This
    latter reporting feature is supported in brew syntax or with a custom reference
    class with a smarty caching 'backend'.
Provides a set of tools to install, manage and run several
    'Pandoc' versions.
The document converter 'pandoc' <https://pandoc.org/> is widely used
    in the R community. One feature of 'pandoc' is that it can produce and consume
    JSON-formatted abstract syntax trees (AST). This allows to transform a given
    source document into JSON-formatted AST, alter it by so called filters and pass
    the altered JSON-formatted AST back to 'pandoc'. This package provides functions
    which allow to write such filters in native R code. 
    Although this package is inspired by the Python package 'pandocfilters' 
    <https://github.com/jgm/pandocfilters/>, it provides additional convenience functions which make it simple to use the 'pandocfilters' package as a 
    report generator. Since 'pandocfilters' inherits most of it's functionality
    from 'pandoc' it can create documents in many formats 
    (for more information see <https://pandoc.org/>) but is also bound to the same
    limitations as 'pandoc'.
API wrapper that contains functions to retrieve data from the 'Pandora' databases. Web services for API: <https://pandora.earth/>.
Aggregate Business Tendency Survey Data (and other qualitative
    surveys) to time series at various aggregation levels. Run aggregation of
    survey data in a speedy, re-traceable and a easily deployable way.
    Aggregation is substantially accelerated by use of data.table.
    This package intends to provide an interface that is less general and abstract than data.table but rather geared towards
    survey researchers.
A high performance package implementing random effects and/or
    sample selection models for panel count data. The details of the models are discussed in Peng and Van den Bulte (2023) <doi:10.2139/ssrn.2702053>.
Understanding the dynamics of potentially heterogeneous variables is important in statistical applications.
    This package provides tools for estimating the degree of heterogeneity across cross-sectional units in the panel data analysis.
    The methods are developed by Okui and Yanagi (2019) <doi:10.1016/j.jeconom.2019.04.036> and Okui and Yanagi (2020) <doi:10.1093/ectj/utz019>.
Implements a set of methodological tools
	     that enable researchers to apply matching methods to
	     time-series cross-sectional data. Imai, Kim, and Wang
	     (2023) <http://web.mit.edu/insong/www/pdf/tscs.pdf> 
	     proposes a nonparametric generalization of the
	     difference-in-differences estimator, which does not rely
	     on the linearity assumption as often done in
	     practice. Researchers first select a method of matching
	     each treated observation for a given unit in a
	     particular time period with control observations from
	     other units in the same time period that have a similar
	     treatment and covariate history. These methods include
	     standard matching methods based on propensity score and
	     Mahalanobis distance, as well as weighting methods. Once 
	     matching and refinement is done,  
	     treatment effects can be estimated with 
	     standard errors. The package also offers diagnostics for researchers to assess the quality 
	     of their results.
Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" <doi:10.1080/01621459.2019.1604367>.
Provides an object type and associated tools for storing and 
  wrangling panel data. Implements several methods for creating regression
  models that take advantage of the unique aspects of 
  panel data. Among other capabilities, automates the "within-between" 
  (also known as "between-within" and "hybrid") panel regression specification
  that combines the desirable aspects of both fixed effects and random effects 
  econometric models and fits them as multilevel models 
  (Allison, 2009 <doi:10.4135/9781412993869.d33>; 
  Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be 
  estimated via generalized estimating equations 
  (GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian 
  estimation is (optionally) supported via 'Stan'. 
  Supports estimation of asymmetric effects models via first differences
  (Allison, 2019 <doi:10.1177/2378023119826441>) as well as a generalized
  linear model extension thereof using GEE.
Create an automated regression table that is well-suited for models that are estimated with multiple dependent variables. 'panelsummary' extends 'modelsummary' (Arel-Bundock, V. (2022) <doi:10.18637/jss.v103.i01>) by allowing regression tables to be split into multiple sections with a simple function call. Utilize familiar arguments such as fmt, estimate, statistic, vcov, conf_level, stars, coef_map, coef_omit, coef_rename, gof_map, and gof_omit from 'modelsummary' to clean the table, and additionally, add a row for the mean of the dependent variable without external manipulation.
Generalized Least Squares (GLS) estimation of Seemingly Unrelated Regression
             (SUR) systems on unbalanced panel in the one/two-way cases also taking into 
             account the possibility of cross equation restrictions.  Methodological details
             can be found in Biørn (2004) <doi:10.1016/j.jeconom.2003.10.023> and Platoni,
             Sckokai, Moro (2012) <doi:10.1080/07474938.2011.607098>.
Estimation of two- and three-way dynamic panel threshold regression models (Di Lascio and Perazzini (2024) <https://repec.unibz.it/bemps104.pdf>; Di Lascio and Perazzini (2022, ISBN:978-88-9193-231-0); Seo and Shin (2016) <doi:10.1016/j.jeconom.2016.03.005>) through the generalized method of moments based on the first difference transformation and the use of instrumental variables. The models can be used to find a change point detection in the time series. In addition, random number generation is also implemented.
We extend two general methods of moment estimators to panel vector 
    autoregression models (PVAR) with p lags of endogenous variables, predetermined 
    and strictly exogenous variables. This general PVAR model contains the first 
    difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, 
    Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator 
    by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also 
    provide specification tests (Hansen overidentification test, lag selection 
    criterion and stability test of the PVAR polynomial) and classical structural 
    analysis for PVAR models such as orthogonal and generalized impulse response 
    functions, bootstrapped confidence intervals for impulse response analysis and 
    forecast error variance decompositions.
Visualizes panel data. It has three main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. For details, see <doi:10.18637/jss.v107.i07>.
Leading/lagging a panel, creating dummy variables,
             taking panel differences, looking for panel autocorrelations,
             and more. Implemented via a 'data.table' back end.
Tools to interact with the 'Pangaea' Database
    (<https://www.pangaea.de>), including functions for searching for data,
    fetching 'datasets' by 'dataset' 'ID', and working with the 'Pangaea'
    'OAI-PMH' service.
A central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an  explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations.  In contrast to the classic power-transformations, the methods in this package allows for  theoretical driven user input and the possibility to compare with a non-parametric transformation.
Provides a customisable R 'shiny' app for immersively
    visualising, mapping and annotating panospheric (360 degree) imagery.
    The flexible interface allows annotation of any geocoded images using
    up to 4 user specified dropdown menus. The app uses 'leaflet' to
    render maps that display the geo-locations of images and panellum
    <https://pannellum.org/>, a lightweight panorama viewer for the web,
    to render images in virtual 360 degree viewing mode. Key functions
    include the ability to draw on & export parts of 360 images for
    downstream applications. Users can also draw polygons and points on
    map imagery related to the panoramic images and export them for
    further analysis. Downstream applications include using annotations to
    train Artificial Intelligence/Machine Learning (AI/ML) models and
    geospatial modelling and analysis of camera based survey data.
An interface to the API for 'Pan-STARRS1', a data archive of
    the PS1 wide-field astronomical survey.  The package allows access to
    the PS1 catalog and to the PS1 images.  (see
    <https://outerspace.stsci.edu/display/PANSTARRS/> for more
    information).  You can use it to plan astronomical observations, make
    guidance pictures, find magnitudes in five broadband filters (g, r, i,
    z, y) and more.
Sankey diagrams are a powerfull and visually attractive way
  to visualize the flow of conservative substances through a system.
  They typically consists of a network of nodes, and fluxes between them,
  where the total balance in each internal node is 0, i.e. input equals output.
  Sankey diagrams are typically used to display energy systems, material flow accounts etc.
  Unlike so-called alluvial plots, Sankey diagrams also allow for cyclic flows:
  flows originating from a single node can, either direct or indirect, contribute to the input of that same node.
  This package, named after the Greek aphorism Panta Rhei (everything flows),
  provides functions to create publication-quality diagrams, using data in tables (or spread sheets)
  and a simple syntax.
Tools to create dynamic, submission-ready manuscripts, which
  conform to American Psychological Association manuscript guidelines. We
  provide R Markdown document formats for manuscripts (PDF and Word) and
  revision letters (PDF). Helper functions facilitate reporting statistical
  analyses or create publication-ready tables and plots.
Positive predictive value (PPV) defined as the conditional probability of clinical trial assay (CTA) being positive given Companion diagnostic device (CDx) being positive is a key performance parameter for evaluating the clinical validity utility of a companion diagnostic test in clinical bridging studies. When bridging study patients are enrolled based on CTA assay results, Binomial-based confidence intervals (CI) may are not appropriate for PPV CI estimation. Bootstrap CIs which are not restricted by the Binomial assumption may be used for PPV CI estimation only when PPV is not 100%. Bootstrap CI is not valid when PPV is 100% and becomes a single value of [1, 1]. We proposed a risk ratio-based method for constructing CI for PPV. By simulation we illustrated that the coverage probability of the proposed CI is close to the nominal value even when PPV is high and negative percent agreement (NPA) is close to 100%. There is a lack of R package for PPV CI calculation. we developed a publicly available R package along with this shiny app to implement the proposed approach and some other existing methods.
A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based
	     reports and to prettify the output of various estimated models.
This is a data only package, that provides distances from a paper plane experiment.
A simple interface in the form of R6 classes for executing tasks in
    parallel, tracking their progress, and displaying accurate progress bars.
Tool for producing Pen's parade graphs, useful for visualizing inequalities in income, wages or other variables, as proposed by Pen (1971, ISBN: 978-0140212594). Income or another economic variable is captured by the vertical axis, while the population is arranged in ascending order of income along the horizontal axis. Pen's income parades provide an easy-to-interpret visualization of economic inequalities.
Define parameter spaces, constraints and
    dependencies for arbitrary algorithms, to program on such spaces. Also
    includes statistical designs and random samplers. Objects are
    implemented as 'R6' classes.
Creation and selection of PARAllel FACtor Analysis (PARAFAC)
    models of longitudinal microbiome data. You can import your own data with
    our import functions or use one of the example datasets to create your own
    PARAFAC models. Selection of the optimal number of components can be done
    using assessModelQuality() and assessModelStability(). The selected model can
    then be plotted using plotPARAFACmodel(). The Parallel Factor
    Analysis method was originally described by Caroll and Chang (1970)
    <doi:10.1007/BF02310791> and Harshman (1970)
    <https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.
A fast parallelized alternative to R's native 'dist' function to
    calculate distance matrices for continuous, binary, and multi-dimensional
    input matrices, which supports a broad variety of 41 predefined distance
    functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user-
    defined functions written in C++. For ease of use, the 'parDist' function
    extends the signature of the 'dist' function and uses the same parameter
    naming conventions as distance methods of existing R packages. The package
    is mainly implemented in C++ and leverages the 'RcppParallel' package to
    parallelize the distance computations with the help of the 'TinyThread'
    library. Furthermore, the 'Armadillo' linear algebra library is used for
    optimized matrix operations during distance calculations. The curiously
    recurring template pattern (CRTP) technique is applied to avoid virtual
    functions, which improves the Dynamic Time Warping calculations while
    the implementation stays flexible enough to support different DTW step
    patterns and normalization methods.
Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk,
  and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in
  parallel).
Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package.  For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.
Unified parallelization framework for multiple
    back-end, designed for internal package and interactive usage.  The
    main operation is parallel mapping over lists.  Supports 'local',
    'multicore', 'mpi' and 'BatchJobs' mode.  Allows tagging of the
    parallel operation with a level name that can be later selected by the
    user to switch on parallel execution for exactly this operation.
See Miroshnikov and Conlon (2014) <doi:10.1371/journal.pone.0108425>. Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.
Application of the Partitioning-Around-Medoids (PAM) clustering algorithm described in Schubert, E. and Rousseeuw, P.J.:
        "Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms." Information Systems,
        vol. 101, p. 101804, (2021). <doi:10.1016/j.is.2021.101804>.
	It uses a binary format for storing and retrieval of matrices developed for the 'jmatrix' package but the functionality of 'jmatrix'
	is included here, so you do not need to install it. Also, it is used by package 'scellpam', so if you have installed it, you do not need
	to install this package.
	PAM can be applied to sets of data whose dissimilarity matrix can be very big. It has been tested with up to 100.000 points.
	It does this with the help of the code developed for other package, 'jmatrix', which allows the matrix not to be loaded in 'R' memory (which
	would force it to be of double type) but it gets from disk, which allows using float (or even smaller data types). Moreover, the
	dissimilarity matrix is calculated in parallel if the computer has several cores so it can open many threads. The initial part
	of the PAM algorithm can be done with the BUILD or LAB algorithms; the BUILD algorithm has been implemented in parallel. The optimization
	phase implements the FastPAM1 algorithm, also in parallel. Finally, calculation of silhouette is available and also implemented in parallel.
Parallelise constraint based causality discovery and causal inference methods. The parallelised algorithms in the package will generate the same results as that of the 'pcalg' package but will be much more efficient.
Create a parallel coordinates plot, using `htmlwidgets` package and `d3.js`.
To calculate the raw,
    central and standardized
    moments from distribution
    parameters. To solve the
    distribution parameters based
    on user-provided mean, standard
    deviation, skewness and
    kurtosis. Normal, skew-normal,
    skew-t and Tukey g-&-h
    distributions are supported,
    for now.
Calculate parametric mortality and Fertility models, following packages 'BaSTA' in Colchero, Jones and Rebke (2012) <doi:10.1111/j.2041-210X.2012.00186.x> and 'BaFTA' <https://github.com/fercol/BaFTA>, summary statistics (e.g. ageing rates, life expectancy, lifespan equality, etc.), life table and product limit estimators from census data.
Utilities for processing the parameters of various
    statistical models. Beyond computing p values, CIs, and other indices
    for a wide variety of models (see list of supported models using the
    function 'insight::supported_models()'), this package implements
    features like bootstrapping or simulating of parameters and models,
    feature reduction (feature extraction and variable selection) as well
    as functions to describe data and variable characteristics (e.g.
    skewness, kurtosis, smoothness or distribution).
Allows specification and fitting of some parameter estimation
    examples inspired by time-resolved spectroscopy via a Shiny GUI.
Functions for parameter descriptions and operations in
    black-box optimization, tuning and machine learning. Parameters can be
    described (type, constraints, defaults, etc.), combined to parameter
    sets and can in general be programmed on. A useful OptPath object
    (archive) to log function evaluations is also provided.
Performs statistical tests to compare coefficients and residual 
    variance across models. Also provides graphical methods for 
    assessing heterogeneity in coefficients and residuals. Currently supports 
    linear and generalized linear models.
A convenient framework for aggregating and disaggregating continuously
    varying parameters (for example, case fatality ratio, with age) for proper
    parametrization of lower-resolution compartmental models (for example, with
    broad age categories) and subsequent upscaling of model outputs to high resolution
    (for example, as needed when calculating age-sensitive measures like years-life-lost).
NOTE: 'PARAMLINK' HAS BEEN SUPERSEDED BY THE 'PED SUITE'
    PACKAGES (<https://magnusdv.github.io/pedsuite/>). 'PARAMLINK' IS
    MAINTAINED ONLY FOR LEGACY PURPOSES AND SHOULD NOT BE USED IN NEW
    PROJECTS. A suite of tools for analysing pedigrees with marker data,
    including parametric linkage analysis, forensic computations,
    relatedness analysis and marker simulations. The core of the package
    is an implementation of the Elston-Stewart algorithm for pedigree
    likelihoods, extended to allow mutations as well as complex
    inbreeding. Features for linkage analysis include singlepoint LOD
    scores, power analysis, and multipoint analysis (the latter through a
    wrapper to the 'MERLIN' software). Forensic applications include
    exclusion probabilities, genotype distributions and conditional
    simulations. Data from the 'Familias' software can be imported and
    analysed in 'paramlink'. Finally, 'paramlink' offers many utility
    functions for creating, manipulating and plotting pedigrees with or
    without marker data (the actual plotting is done by the 'kinship2'
    package).
Parametric linkage analysis of monogenic traits in medical
    pedigrees. Features include singlepoint analysis, multipoint analysis
    via 'MERLIN' (Abecasis et al. (2002) <doi:10.1038/ng786>),
    visualisation of log of the odds (LOD) scores and summaries of linkage
    peaks. Disease models may be specified to accommodate phenocopies,
    reduced penetrance and liability classes. 'paramlink2' is part of the
    'pedsuite' package ecosystem, presented in 'Pedigree Analysis in R'
    (Vigeland, 2021, ISBN:9780128244302).
An interface to simplify organizing parameters used in a package,
    using external configuration files. This attempts to provide a cleaner
    alternative to options().
This function obtains a Random Number Generator (RNG) or collection of RNGs that replicate the required parameter(s) of a distribution for a time series of data. Consider the case of reproducing a time series data set of size 20 that uses an autoregressive (AR) model with phi = 0.8 and standard deviation equal to 1. When one checks the arima.sin() function's estimated parameters, it's possible that after a single trial or a few more, one won't find the precise parameters. This enables one to look for the ideal RNG setting for a simulation that will accurately duplicate the desired parameters.
Run simulations or other functions while easily varying parameters
    from one iteration to the next. Some common use cases would be grid search
    for machine learning algorithms, running sets of simulations (e.g.,
    estimating statistical power for complex models), or bootstrapping under
    various conditions. See the 'paramtest' documentation for more information
    and examples.
An implementation of Horn's technique for numerically and graphically evaluating the components or factors retained in a principle components analysis (PCA) or common factor analysis (FA). Horn's method contrasts eigenvalues produced through a PCA or FA on a number of random data sets of uncorrelated variables with the same number of variables and observations as the experimental or observational data set to produce eigenvalues for components or factors that are adjusted for the sample error-induced inflation. Components with adjusted eigenvalues greater than one are retained. paran may also be used to conduct parallel analysis following Glorfeld's (1995) suggestions to reduce the likelihood of over-retention.
Fast, flexible framework for implementing Bayesian optimization of model 
	hyperparameters according to the methods described in Snoek et al. <arXiv:1206.2944>.
	The package allows the user to run scoring function in parallel, save intermediary 
	results, and tweak other aspects of the process to fully utilize the computing resources
	available to the user.
Complex graphical representations of data are best explored using interactive elements.
  'parcats' adds interactive graphing capabilities to the 'easyalluvial' package.
  The 'plotly.js' parallel categories diagrams offer a good framework for
  creating interactive flow graphs that allow manual drag and drop sorting of dimensions
  and categories, highlighting single flows and displaying mouse over information. The
  'plotly.js' dependency is quite heavy and therefore is outsourced into a separate package.
Construct parser combinator functions, higher order functions that
  parse input. Construction of such parsers is transparent and easy. Their main
  application is the parsing of structured text files like those generated by
  laboratory instruments. Based on a paper by Hutton (1992)
  <doi:10.1017/S0956796800000411>.
Parallelized version of the "segment" function from Bioconductor package "DNAcopy", utilizing multi-core computation on host CPU
Reviews other packages during code review by looking at their
    dependencies, code style, code complexity, and how internally defined
    functions interact with one another.
Utilities for the Pareto, piecewise Pareto and generalized Pareto distribution
    that are useful for reinsurance pricing. In particular, the package provides
    a non-trivial algorithm that can be used to match the expected losses of a 
    tower of reinsurance layers with a layer-independent collective risk model.
    The theoretical background of the matching algorithm and most other methods
    are described in Ulrich Riegel (2018) <doi:10.1007/s13385-018-0177-3>.
Statistical functions to describe a Pareto Positive Stable (PPS) 
    distribution and fit it to real data. Graphical and statistical tools to 
    validate the fits are included.
Fits Parametric Frailty Models by maximum marginal likelihood.
             Possible baseline hazards:
                 exponential, Weibull, inverse Weibull (Fréchet),
                 Gompertz, lognormal, log-skew-normal, and loglogistic.
             Possible Frailty distributions:
                gamma, positive stable, inverse Gaussian and lognormal.
Offers tools to estimate and visualize levels of major pollutants
             (CO, NO2, SO2, Ozone, PM2.5 and PM10) across the conterminous
             United States for user-defined time ranges. Provides functions to
             retrieve pollutant data from the U.S. Environmental Protection
             Agency’s 'Air Quality System' (AQS) API service
             <https://aqs.epa.gov/aqsweb/documents/data_api.html> for
             interactive visualization through a 'shiny' application, allowing
             users to explore pollutant levels for a given location over time
             relative to the National Ambient Air Quality Standards (NAAQS).
Provides a parallel estimation method for generalized 
  linear models without compiling with a multithreaded LAPACK or BLAS.
Computes the All-Resolution Inference method in the permutation framework, i.e., simultaneous lower confidence bounds for the number of true discoveries. <doi:10.1002/sim.9725>.
Palettes inspired by Paris 2024 Olympic and Paralympic Games for 
    data visualizations. Length of color palettes is configurable.
Provision of a set of models and methods for use in the allocation and management of capital in financial portfolios.
Parallel estimation of the mutual information based on entropy
    estimates from k-nearest neighbors distances and algorithms for the
    reconstruction of gene regulatory networks
    (Sales et al, 2011 <doi:10.1093/bioinformatics/btr274>).
Executes simple parametric models for right-censored 
    survival data.  Functionality emulates capabilities in 'Minitab', including
    fitting right-censored data, assessing fit, plotting survival functions,
    and summary statistics and probabilities.
Enable optimization of parameters of ordinary differential equations.
             Therefore, using 'SUNDIALS' to solve the ODE-System (see Hindmarsh, Alan C., Peter N. Brown, Keith E. Grant, Steven L. Lee, Radu Serban, Dan E. Shumaker, and Carol S. Woodward. (2005) <doi:10.1145/1089014.1089020>).
             Furthermore, for optimization the particle swarm algorithm is used (see: Akman, Devin, Olcay Akman, and Elsa Schaefer. (2018) <doi:10.1155/2018/9160793> and Sengupta, Saptarshi, Sanchita Basak, and Richard Peters. (2018) <doi:10.3390/make1010010>).
Reads in multi-part parquet files. Will read in parquet files that have not been previously coalesced into one file. Convenient for reading in moderately sized, but split files.
Collection of functions to get files in parquet format.
    Parquet is a columnar storage file format <https://parquet.apache.org/>. 
    The files to convert can be of several formats 
    ("csv", "RData", "rds", "RSQLite", 
    "json", "ndjson", "SAS", "SPSS"...).
Implements tools for the analysis of partially ordered data, with a particular focus on the evaluation of multidimensional systems of indicators and on the analysis of poverty. References, Fattore M. (2016) <doi:10.1007/s11205-015-1059-6> Fattore M., Arcagni A. (2016) <doi:10.1007/s11205-016-1501-4> Arcagni A. (2017) <doi:10.1007/978-3-319-45421-4_19>.
Parse dates automatically, without the need of specifying a
    format. Currently it includes the git date parser.  It can also
    recognize and parse all ISO 8601 formats.
A system to increase the efficiency of dynamic web-scraping with 'RSelenium'
    by leveraging parallel processing. You provide a function wrapper for your 'RSelenium' 
    scraping routine with a set of inputs, and 'parsel' runs it in several browser instances. 
    Chunked input processing as well as error catching and logging ensures seamless 
    execution and minimal data loss, even when unforeseen 'RSelenium' errors occur. You can additionally 
    build safe scraping functions with minimal coding by utilizing constructor functions that act as wrappers 
    around 'RSelenium' methods.
Exports an enhanced version of the tools::parseLatex()
    function to handle 'LaTeX' syntax more accurately.  Also
    includes numerous functions for searching and modifying
    'LaTeX' source.
An implementation of a formal grammar and parser for R Markdown documents
    using the Boost Spirit X3 library. It also includes a collection of high level
    functions for working with the resulting abstract syntax tree.
Functions to load Research Patient Data Registry ('RPDR') text queries from Partners Healthcare institutions into R.
             The package also provides helper functions to manipulate data and execute common procedures
             such as finding the closest radiological exams considering a given timepoint, or creating a DICOM header database
             from the downloaded images. All functionalities are parallelized for fast and efficient analyses.
Perform flexible simulation studies using one or multiple computer cores.
          The package is set up to be usable on high-performance clusters in addition
          to being run locally, see examples on <https://github.com/SachaEpskamp/parSim>.
A common interface is provided to allow users to specify a
    model without having to remember the different argument names across
    different functions or computational engines (e.g. 'R', 'Spark',
    'Stan', 'H2O', etc).
It estimates the parameters of a partially linear regression censored model via maximum penalized likelihood through of ECME algorithm. The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124> but considering the SMN family.
A novel tool for generating a piecewise
        constant estimation list of increasingly complex predictors
        based on an intensive and comprehensive search over the entire
        covariate space.
Provides a 'partialised' class that extends the partialising 
    function of 'purrr' by making it easier to change the arguments. This is 
    similar to the function-like object in 'Julia' 
    (<https://docs.julialang.org/en/v1/manual/methods/#Function-like-objects>).
Tests for a comparison of two partially overlapping samples.
  A comparison of means using the partially overlapping samples t-test: 
  See Derrick, Russ, Toher and White (2017), Test 
  statistics for the comparison of means for two samples which include 
  both paired observations and independent observations, Journal of 
  Modern Applied Statistical Methods, 16(1). 
  A comparison of proportions using the partially overlapping samples z-test:
  See Derrick, Dobson-Mckittrick, Toher and White (2015), Test statistics
  for comparing two proportions with partially overlapping samples.
  Journal of Applied Quantitative Methods, 10(3).
Implements IV-estimator and Bayesian estimator for linear-in-means Spatial Autoregressive (SAR) model (see LeSage, 1997 <doi:10.1177/016001769702000107>; Lee, 2004 <doi:10.1111/j.1468-0262.2004.00558.x>; Bramoullé et al., 2009 <doi:10.1016/j.jeconom.2008.12.021>), while assuming that only a partial information about the network structure is available. Examples are when the adjacency matrix is not fully observed or when only consistent estimation of the network formation model is available (see Boucher and Houndetoungan <https://ahoundetoungan.com/files/Papers/PartialNetwork.pdf>).
A toolbox to create a particle swarm optimisation (PSO), the package contains two classes: the Particle and the Particle Swarm, this two class is used to run the PSO with methods to easily print, plot and save the result.
Simulating particle movement in 2D space has many
    application. The 'particles' package implements a particle simulator
    based on the ideas behind the 'd3-force' 'JavaScript' library.
    'particles' implements all forces defined in 'd3-force' as well as
    others such as vector fields, traps, and attractors.
Producing the time-dependent receiver operating characteristic (ROC) curve through parametric approaches. Tools for
    generating random data, fitting, predicting and check goodness of fit are prepared. The methods are
    developed from the theoretical framework of proportional hazard model and copula functions.
    Using this package, users can now simulate parametric time-dependent ROC and run experiment
    to understand the behavior of the curve under different scenario.
A fast and flexible framework for agglomerative partitioning.
    'partition' uses an approach called Direct-Measure-Reduce to create
    new variables that maintain the user-specified minimum level of
    information. Each reduced variable is also interpretable: the original
    variables map to one and only one variable in the reduced data set.
    'partition' is flexible, as well: how variables are selected to
    reduce, how information loss is measured, and the way data is reduced
    can all be customized.  'partition' is based on the Partition
    framework discussed in Millstein et al. (2020) 
    <doi:10.1093/bioinformatics/btz661>.
A collection of functions that can be used to estimate selection and complementarity effects,
	sensu Loreau & Hector (2001) <doi:10.1038/35083573>, even in cases where data are only available for
	a random subset of species (i.e. incomplete sample-level data). A full derivation and explanation of the 
	statistical corrections used here is available in Clark et al. (2019) <doi:10.1111/2041-210X.13285>.
Provides several measures ((dis)similarity, distance/metric,
    correlation, entropy) for comparing two partitions of the same set of
    objects. The different measures can be assigned to three different
    classes: Pair comparison (containing the famous Jaccard and Rand
    indices), set based, and information theory based.
    Many of the implemented measures can be found in
    Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006)
    <doi:10.1007/s00357-006-0017-z> and
    Meila M (2007) <doi:10.1016/j.jmva.2006.11.013>.
    Partitions are represented by vectors of class labels which allow a
    straightforward integration with existing clustering algorithms
    (e.g. kmeans()). The package is mostly based on the S4 object system.
partitionMetric computes a distance between two partitions
        of a set.
Additive partitions of integers.  Enumerates the
  partitions, unequal partitions, and restricted partitions of an
  integer; the three corresponding partition functions are also given.
  Set partitions and now compositions and riffle shuffles are
  included.
Miscellaneous utilities for parallelizing large
   computations.  Alternative to MapReduce.
   File splitting and distributed operations such as sort and aggregate.
   "Software Alchemy" method for parallelizing most statistical methods,
   presented in N. Matloff, Parallel Computation for Data Science,
   Chapman and Hall, 2015.  Includes a debugging aid.
Partitioning the R2 of GLMMs into variation explained by each 
    predictor and combination of predictors using semi-partial (part) R2 and
    inclusive R2. Methods are based on the R2 for GLMMs described in
    Nakagawa & Schielzeth (2013) and Nakagawa, Johnson & Schielzeth (2017).
Basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancements are expected, and the maintainer cannot provide any support.
Datetimes and timestamps are invariably an imprecise notation, with any
    partial representation implying some amount of uncertainty. To handle this,
    'parttime' provides classes for embedding partial missingness as a central
    part of its datetime classes. This central feature allows for more ergonomic
    use of datetimes for challenging datetime computation, including
    calculations of overlapping date ranges, imputations, and more thoughtful
    handling of ambiguity that arises from uncertain time zones. This package was
    developed first and foremost with pharmaceutical applications in mind, but
    aims to be agnostic to application to accommodate general use cases just as
    conveniently.
Visualize the partitions of simple decision trees, involving one or
    two predictors, on the scale of the original data. Provides an intuitive
    alternative to traditional tree diagrams, by visualizing how a decision tree
    divides the predictor space in a simple 2D plot alongside the original data.
    The 'parttree' package supports both classification and regression trees
    from 'rpart' and 'partykit', as well as trees produced by popular frontend
    systems like 'tidymodels' and 'mlr3'. Visualization methods are provided for
    both base R graphics and 'ggplot2'.
A computational toolbox for recursive partitioning.
  The core of the package is ctree(), an implementation of
  conditional inference trees which embed tree-structured 
  regression models into a well defined theory of conditional
  inference procedures. This non-parametric class of regression
  trees is applicable to all kinds of regression problems, including
  nominal, ordinal, numeric, censored as well as multivariate response
  variables and arbitrary measurement scales of the covariates. 
  Based on conditional inference trees, cforest() provides an
  implementation of Breiman's random forests. The function mob()
  implements an algorithm for recursive partitioning based on
  parametric models (e.g. linear models, GLMs or survival
  regression) employing parameter instability tests for split
  selection. Extensible functionality for visualizing tree-structured
  regression models is available. The methods are described in
  Hothorn et al. (2006) <doi:10.1198/106186006X133933>,
  Zeileis et al. (2008) <doi:10.1198/106186008X319331> and 
  Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.
A toolkit with infrastructure for representing, summarizing, and
  visualizing tree-structured regression and classification models. This
  unified infrastructure can be used for reading/coercing tree models from
  different sources ('rpart', 'RWeka', 'PMML') yielding objects that share
  functionality for print()/plot()/predict() methods. Furthermore, new and improved
  reimplementations of conditional inference trees (ctree()) and model-based
  recursive partitioning (mob()) from the 'party' package are provided based
  on the new infrastructure. A description of this package was published
  by Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.
Parse messy geographic coordinates from various character formats
    to decimal degree numeric values. Parse coordinates into
    their parts (degree, minutes, seconds); calculate hemisphere
    from coordinates; pull out individually degrees,
    minutes, or seconds; add and subtract degrees, minutes,
    and seconds. C++ code herein originally inspired from code
    written by Jeffrey D. Bogan, but then completely re-written.
An R package for polygenic trait analysis.
Anomaly detection method based on the paper "Truth will out: Departure-based process-level detection of stealthy attacks on control systems" from Wissam Aoudi, Mikel Iturbe, and Magnus Almgren (2018) <DOI:10.1145/3243734.3243781>. Also referred to the following implementation: <https://github.com/rahulrajpl/PyPASAD>.
Provide summary table of daily physical activity and per-person/grouped heat map for accelerometer data from SenseWear Armband. See <https://templehealthcare.wordpress.com/the-sensewear-armband/> for more information about SenseWear Armband.
Power and sample size calculation for testing fixed effect coefficients in multilevel linear mixed effect models with one or more than one independent populations. Laird, Nan M. and Ware, James H. (1982) <doi:10.2307/2529876>.
Power calculations are a critical component of any research study to determine the 
	minimum sample size necessary to detect differences between multiple groups. Here we 
	present an 'R' package, 'PASSED', that performs power and sample size calculations for 
	the test of two-sample means or ratios with data following beta, 
	gamma (Chang et al. (2011), <doi:10.1007/s00180-010-0209-1>), normal, 
	Poisson (Gu et al. (2008), <doi:10.1002/bimj.200710403>), binomial, geometric, and 
	negative binomial (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>) distributions.
An implementation of the unified framework for assessing partial association 
            between ordinal variables after adjusting for a set of covariates (Dungang Liu, Shaobo 
            Li, Yan Yu and Irini Moustaki (2020), accepted by the Journal of the American 
            Statistical Association). This package provides a set of tools to quantify, visualize, 
            and test partial associations between multiple ordinal variables. It can produce a number
            of $phi$ measures, partial regression plots, 3-D plots, and $p$-values for testing 
            $H_0: phi=0$ or $H_0: phi <= delta$.
Smooths the process of working with country names and codes via 
    powerful parsing, standardization, and conversion utilities arranged in a 
    simple, consistent API. Country name formats include multiple sources 
    including the Unicode Common Locale Data 
    Repository (CLDR, <http://cldr.unicode.org/>) common-sense standardized 
    names in hundreds of languages.
Simulates judgments of frequency and duration based on
    the Probability Associator Time (PASS-T) model. PASS-T is a memory
    model based on a simple competitive artificial neural network. It 
    can imitate human judgments of frequency and duration, which have
    been extensively studied in cognitive psychology
    (e.g. Hintzman (1970) <doi:10.1037/h0028865>, Betsch et al. (2010)
    <https://psycnet.apa.org/record/2010-18204-003>). The PASS-T model
    is an extension of the PASS model (Sedlmeier, 2002,
    ISBN:0198508638). The package provides an easy way to run
    simulations, which can then be compared with empirical data in
    human judgments of frequency and duration.
Create random passwords of letters, numbers and punctuation.
Plot both fixed and random effects of linear mixed models, multilevel models in a single spaghetti plot. The package allows to visualize the effect of a predictor on a criterion between different levels of a grouping variable. Additionally, confidence intervals can be displayed for fixed effects. Calculation of predicted values of random effects allows only models with one random intercept and/or one random slope to be plotted. Confidence intervals and predicted values of fixed effects are computed using the 'ggpredict' function from the 'ggeffects' package. Lüdecke, D. (2018) <doi:10.21105/joss.00638>.
A Boolean network is a particular kind of discrete dynamical system where the variables are simple binary switches. Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) <doi:10.1186/1478-811X-11-46>, stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) <doi:10.1186/1687-4153-2012-5>, and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) <doi:10.1371/journal.pcbi.1010536>. This package includes source code derived from the 'BoolNet' package, which is licensed under the Artistic License 2.0.
Methods to easily extract and manipulate palaeoclimate
  reconstructions for ecological and anthropological analyses, as described
  in Leonardi et al. (2023) <doi:10.1111/ecog.06481>.
Provides a 'RStudio' addin allowing to paste the content of the clipboard as a comment block or as 'roxygen' lines. This is very useful to insert an example in the 'roxygen' block.
Regularisation, decomposition and analysis of space-time series.
  The pastecs R package is a PNEC-Art4 and IFREMER (Benoit Beliaeff
  <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 functionalities to R.
Functions and data sets for the text Probability and Statistics
    with R.
Functions and data sets for the text Probability and Statistics
    with R, Second Edition.
Functions to patch specials in '.dvi' files,
        or entries in '.synctex' files.  Works with concordance=TRUE 
        in Sweave, knitr or R Markdown to link sources to previews.
This utility eases the debugging of literate documents
	     ('noweb' files) by patching the synchronization information
	     (the '.synctex(.gz)' file) produced by 'pdflatex' with
	     concordance information produced by 'Sweave' or 'knitr' and
	     'Sweave' or 'knitr' ; this allows for bilateral communication
	     between a text editor (visualizing the 'noweb' source) and
	     a viewer (visualizing the resultant 'PDF'), thus bypassing
	     the intermediate 'TeX' file.
The 'ggplot2' package provides a strong API for sequentially 
    building up a plot, but does not concern itself with composition of multiple
    plots. 'patchwork' is a package that expands the API to allow for 
    arbitrarily complex composition of plots by, among others, providing 
    mathematical operators for combining multiple plots. Other packages that try 
    to address this need (but with a different approach) are 'gridExtra' and 
    'cowplot'.
Converts TXT and XML data curated by the United States Patent and
    Trademark Office (USPTO). Allows conversion of bulk data after downloading
    directly from the USPTO bulk data website, eliminating need for users to
    wrangle multiple data formats to get large patent databases in tidy,
    rectangular format. Data details can be found on the USPTO website
    <https://bulkdata.uspto.gov/>. Currently, all 3 formats: 1. TXT data
    (1976-2001); 2. XML format 1 data (2002-2004); and 3. XML format 2 data
    (2005-current) can be converted to rectangular, CSV format.
    Relevant literature that uses data from USPTO includes Wada (2020)
    <doi:10.1007/s11192-020-03674-4> and Plaza & Albert (2008)
    <doi:10.1007/s11192-007-1763-3>.
Provides functions to simplify the 'PatentsView' API
    (<https://patentsview.org/apis/purpose>) query language,
    send GET and POST requests to the API's seven endpoints, and parse the data
    that comes back.
Facilitates the performance of several analyses, including simple and sequential path coefficient analysis, correlation estimate, drawing correlogram, Heatmap, and path diagram. When working with raw data, that includes one or more dependent variables along with one or more independent variables are available, the path coefficient analysis can be conducted. It allows for testing direct effects, which can be a vital indicator in path coefficient analysis. The process of preparing the dataset rule is explained in detail in the vignette file "Path.Analysis_manual.Rmd". You can find this in the folders labelled "data" and "~/inst/extdata". Also see: 1)the 'lavaan', 2)a sample of sequential path analysis in 'metan' suggested by Olivoto and Lúcio (2020) <doi:10.1111/2041-210X.13384>, 3)the simple 'PATHSAS' macro written in 'SAS' by Cramer et al. (1999) <doi:10.1093/jhered/90.1.260>, and 4)the semPlot() function of 'OpenMx' as initial tools for conducting path coefficient analyses and SEM (Structural Equation Modeling). To gain a comprehensive understanding of path coefficient analysis, both in theory and practice, see a 'Minitab' macro developed by Arminian, A. in the paper by Arminian et al. (2008) <doi:10.1080/15427520802043182>.
Provides path_chain class and functions, which facilitates loading and saving 
             directory structure in YAML configuration files via 'config' package. 
             The file structure you created during exploration can be transformed 
             into legible section in the config file, and then easily loaded for further usage.
Enrichment analysis enables researchers to uncover mechanisms 
    underlying a phenotype. However, conventional methods for enrichment 
    analysis do not take into account protein-protein interaction information, 
    resulting in incomplete conclusions. pathfindR is a tool for enrichment 
    analysis utilizing active subnetworks. The main function identifies active 
    subnetworks in a protein-protein interaction network using a user-provided 
    list of genes and associated p values. It then performs enrichment analyses 
    on the identified subnetworks, identifying enriched terms (i.e. pathways or, 
    more broadly, gene sets) that possibly underlie the phenotype of interest.
    pathfindR also offers functionalities to cluster the enriched terms and 
    identify representative terms in each cluster, to score the enriched terms 
    per sample and to visualize analysis results. The enrichment, clustering and 
    other methods implemented in pathfindR are described in detail in 
    Ulgen E, Ozisik O, Sezerman OU. 2019. pathfindR: An R Package for 
    Comprehensive Identification of Enriched Pathways in Omics Data Through 
    Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.
This is a data-only package, containing data needed to run the CRAN 
    package 'pathfindR', a package for enrichment analysis utilizing active 
    subnetworks. This package contains protein-protein interaction network data, 
    data related to gene sets and example input/output data.
R API for 'Pathling', a tool for querying and transforming electronic health record data that is represented using the 'Fast Healthcare Interoperability Resources' (FHIR) standard - see <https://pathling.csiro.au/docs>.
This wrapper houses PathLit API endpoints for R. The usage of
    these endpoints require the use of an API key which can be obtained at
    <https://www.pathlit.io/docs/cli/>.
Functions for computing fit indices for 
    evaluating the path component of latent variable structural equation models. 
    Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated 
    by Williams and O'Boyle (2011) <doi:10.1177/1094428110391472> and demonstrated by 
    O'Boyle and Williams (2011) <doi:10.1037/a0020539> and Williams, O'Boyle, & Yu (2020) 
    <doi:10.1177/1094428117736137>. Also included are fit indices described by 
    Hancock and Mueller (2011) <doi:10.1177/0013164410384856>.
In causal mediation analysis with multiple causally ordered mediators, a set of path-specific
  effects are identified under standard ignorability assumptions. This package implements an imputation
  approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis
  (Zhou and Yamamoto <doi:10.31235/osf.io/2rx6p>). It contains two main functions: paths() for estimating 
  path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified 
  using the nonparametric bootstrap.
Tools to import, clean, and visualize movement data,
    particularly from motion capture systems such as Optitrack's 
    'Motive', the Straw Lab's 'Flydra', or from other sources. We provide 
    functions to remove artifacts, standardize tunnel position and tunnel 
    axes, select a region of interest, isolate specific trajectories, fill
    gaps in trajectory data, and calculate 3D and per-axis velocity. For 
    experiments of visual guidance, we also provide functions that use 
    subject position to estimate perception of visual stimuli.
For a given graph containing vertices, edges, and a signal associated with the vertices, the 'PathwaySpace' package performs a convolution operation, which involves a weighted combination of neighboring vertices and their associated signals. The package then uses a decay function to project these signals, creating geodesic paths on a 2D-image space. 'PathwaySpace' could have various applications, such as visualizing and analyzing network data in a graphical format that highlights the relationships and signal strengths between vertices. It can be particularly useful for understanding the influence of signals through complex networks. By combining graph theory, signal processing, and visualization, the 'PathwaySpace' package provides a novel way of representing and analyzing graph data.
A systematic bioinformatics tool to develop a new pathway-based gene panel for tumor mutational burden (TMB) assessment (pathway-based tumor mutational burden, PTMB), using somatic mutations files in an efficient manner from either The Cancer Genome Atlas sources or any in-house studies as long as the data is in mutation annotation file (MAF) format. Besides, we develop a multiple machine learning method using the sample's PTMB profiles to identify cancer-specific dysfunction pathways, which can be a biomarker of prognostic and predictive for cancer immunotherapy.
A user friendly way to create patient level prediction models using
  the Observational Medical Outcomes Partnership Common Data Model. Given a cohort
  of interest and an outcome of interest, the package can use data in the Common
  Data Model to build a large set of features. These features can then be used to
  fit a predictive model with a number of machine learning algorithms. This is
  further described in Reps (2017) <doi:10.1093/jamia/ocy032>.
Identify the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model.
Creation of patient profile visualizations for
  exploration, diagnostic or monitoring purposes during a clinical trial.
  These static visualizations display a patient-specific overview
  of the evolution during the trial time frame of 
  parameters of interest (as laboratory, ECG, vital signs),
  presence of adverse events, exposure to a treatment; 
  associated with metadata patient information, 
  as demography, concomitant medication.
  The visualizations can be tailored for specific domain(s) or endpoint(s) of interest.
  Visualizations are exported into patient profile report(s)
  or can be embedded in custom report(s).
This is an extension of the 'testthat' package that
    lets you add parameters to your unit tests. Parameterized unit tests
    are often easier to read and more reliable, since they follow the DNRY
    (do not repeat yourself) rule.
Identifies the entries with patterned responses for psychometric scales. The patterns included in the package are identical (a, a, a), ascending (a, b, c), descending (c, b, a), alternative (a, b, a, b / a, b, c, a, b, c).
Provides a set of functions to efficiently recognize and clean the continuous dorsal pattern of a female brown anole lizard (Anolis sagrei) traced from 'ImageJ', an open platform for scientific image analysis (see <https://imagej.net> for more information), and extract common features such as the pattern sinuosity indices, coefficient of variation, and max-min width.
A comprehensive package for detecting and analyzing causal relationships in complex systems using pattern-based approaches. Key features include state space reconstruction, pattern identification, and causality strength evaluation.
Quantification of variation in organismal color patterns as
    obtained from image data. Patternize defines homology between pattern positions
    across images either through fixed landmarks or image registration. Pattern
    identification is performed by categorizing the distribution of colors using RGB
    thresholds or image segmentation.
Creates aesthetically pleasing and informative pie charts, ring charts, bar charts and box plots with colors, patterns, and images.
A modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). Example of use with microarray or RNA-Seq data are provided.
A cohesive framework for the spectral and spatial analysis of 
    colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013) 
    <doi:10.1111/2041-210X.12069> and Maia, Gruson, Endler & White (2019)
    <doi:10.1111/2041-210X.13174>.
Functions to process, format and store ActiGraph GT1M and GT3X accelerometer data.
Interface to Amazon Web Services <https://aws.amazon.com>,
    including storage, database, and compute services, such as 'Simple
    Storage Service' ('S3'), 'DynamoDB' 'NoSQL' database, and 'Lambda'
    functions-as-a-service.
Interface to 'Amazon Web Services' 'analytics' services,
    including 'Elastic MapReduce' 'Hadoop' and 'Spark' big data service,
    'Elasticsearch' search engine, and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' application integration
    services, including 'Simple Queue Service' ('SQS') message queue,
    'Simple Notification Service' ('SNS') publish/subscribe messaging, and
    more <https://aws.amazon.com/>.
Functions for making low-level API requests to Amazon Web Services
    <https://aws.amazon.com>. The functions handle building, signing, and
    sending requests, and receiving responses. They are designed to help build 
    higher-level interfaces to individual services, such as Simple Storage
    Service (S3).
Interface to 'Amazon Web Services' compute services,
    including 'Elastic Compute Cloud' ('EC2'), 'Lambda'
    functions-as-a-service, containers, batch processing, and more
    <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' cost management services,
    including cost and usage reports, budgets, pricing, and more
    <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' customer engagement
    services, including 'Simple Email Service', 'Connect' contact center
    service, and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' database services,
    including 'Relational Database Service' ('RDS'), 'DynamoDB' 'NoSQL'
    database, and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' developer tools services,
    including version control, continuous integration and deployment, and
    more <https://aws.amazon.com/products/developer-tools/>.
Interface to 'Amazon Web Services' end user computing
    services, including collaborative document editing, mobile intranet,
    and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' machine learning services,
    including 'SageMaker' managed machine learning service, natural
    language processing, speech recognition, translation, and more
    <https://aws.amazon.com/machine-learning/>.
Interface to 'Amazon Web Services' management and governance
    services, including 'CloudWatch' application and infrastructure
    monitoring, 'Auto Scaling' for automatically scaling resources, and
    more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' networking and content
    delivery services, including 'Route 53' Domain Name System service,
    'CloudFront' content delivery, load balancing, and more
    <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' security, identity, and
    compliance services, including the 'Identity & Access Management'
    ('IAM') service for managing access to services and resources, and
    more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' storage services,
    including 'Simple Storage Service' ('S3') and more
    <https://aws.amazon.com/>.
Automated pain scoring from paw withdrawal tracking data. Based on
             Jones et al. (2020) "A machine-vision approach for automated pain
             measurement at millisecond timescales" <doi:10.7554/eLife.57258>.
Parametric bootstrap (PB) has been used for 
             three-way ANOVA model with unequal group variances.
A lightweight package that adds
  progress bar to vectorized R functions
  ('*apply'). The implementation can easily be added
  to functions where showing the progress is
  useful (e.g. bootstrap). The type and style of the
  progress bar (with percentages or remaining time)
  can be set through options.
  Supports several parallel processing backends including future.
This R package provides power calculations via internal simulation methods. The package also provides a frontend to the now abandoned PBAT program (developed by Christoph Lange), and reads in the corresponding output and displays results and figures when appropriate. The license of this R package itself is GPL. However, to have the program interact with the PBAT program for some functionality of the R package, users must additionally obtain the PBAT program from Christoph Lange, and accept his license. Both the data analysis and power calculations have command line and graphical interfaces using tcltk.
Generates a position balanced or nearly position balanced block design with given parameters.  This package can also convert a given proper and equireplicate block design into a position balanced or nearly position balanced block design.
Design and implementation of Percentile-based Shewhart Control Charts for continuous data. Faraz (2019) <doi:10.1002/qre.2384>.
Conducts maximum likelihood analysis and simulation of the
    protracted birth-death model of diversification. See
    Etienne, R.S. & J. Rosindell 2012 <doi:10.1093/sysbio/syr091>;
    Lambert, A., H. Morlon & R.S. Etienne 2014, <doi:10.1007/s00285-014-0767-x>;
    Etienne, R.S., H. Morlon & A. Lambert 2014, <doi:10.1111/evo.12433>.
A simplified, efficient, interface to MPI for HPC clusters. It is
        a derivation and rethinking of the Rmpi package. pbdMPI embraces the
        prevalent parallel programming style on HPC clusters. Beyond the
        interface, a collection of functions for global work with
        distributed data and resource-independent RNG reproducibility is
        included. It is based on S4 classes and methods.
Utilizing scalable linear algebra packages mainly
        including 'BLACS', 'PBLAS', and 'ScaLAPACK' in double precision via
        'pbdMPI' based on 'ScaLAPACK' version 2.0.2.
'ZeroMQ' is a well-known library for high-performance
    asynchronous messaging in scalable, distributed applications.  This
    package provides high level R wrapper functions to easily utilize
    'ZeroMQ'. We mainly focus on interactive client/server programming
    frameworks. For convenience, a minimal 'ZeroMQ' library (4.2.2)
    is shipped with 'pbdZMQ', which can be used if no system installation
    of 'ZeroMQ' is available.  A few wrapper functions compatible with
    'rzmq' are also provided.
The PBIB designs are important type of incomplete block designs having wide area of their applications for example in agricultural experiments, in plant breeding, in sample surveys etc. This package constructs various series of PBIB designs and assists in checking all the necessary conditions of PBIB designs and the association scheme on which these designs are based on. It also assists in calculating the efficiencies of PBIB designs with any number of associate classes. The package also constructs Youden-m square designs which are Row-Column designs for the two-way elimination of heterogeneity. The incomplete columns of these Youden-m square designs constitute PBIB designs. With the present functionality, the package will be of immense importance for the researchers as it will help them to construct PBIB designs, to check if their PBIB designs and association scheme satisfy various necessary conditions for the existence, to calculate the efficiencies of PBIB designs based on any association scheme and to construct Youden-m square designs for the two-way elimination of heterogeneity. R. C. Bose and K. R. Nair (1939) <http://www.jstor.org/stable/40383923>.
A set of datasets and functions used in the book
  'Modele liniowe i mieszane w R, wraz z przykladami w analizie danych'.
  Datasets either come from real studies or are created to be as similar 
  as possible to real studies.
Make statistical inference on the probability of being in response, 
             the duration of response, and the cumulative response rate up to a given time point. 
             The method can be applied to analyze phase II randomized clinical trials with the endpoints 
             being time to treatment response and time to progression or death.
Provides a vectorized R function for calculating
    probabilities from a standard bivariate normal CDF.
Computes p-values based on (a) Satterthwaite or
    Kenward-Rogers degree of freedom methods and (b) parametric bootstrap
    for mixed effects models as implemented in the 'lme4'
    package. Implements parametric bootstrap test for generalized linear
    mixed models as implemented in 'lme4' and generalized linear
    models. The package is documented in the paper by Halekoh and
    Højsgaard, (2012, <doi:10.18637/jss.v059.i09>).  Please see
    'citation("pbkrtest")' for citation details.
Binding models which are useful when analysing protein-ligand interactions by techniques such as Biolayer Interferometry (BLI) or Surface Plasmon Resonance (SPR). Naman B. Shah, Thomas M. Duncan (2014) <doi:10.3791/51383>. Hoang H. Nguyen et al. (2015) <doi:10.3390/s150510481>. After initial binding parameters are known, binding curves can be simulated and parameters can be varied. The models within this package may also be used to fit a curve to measured binding data using non-linear regression.
A light-weight package helps you track and visualize
  the progress of parallel version of vectorized R functions (mc*apply).
  Parallelization (mc.core > 1) works only on *nix (Linux, Unix such as macOS) system due to
  the lack of fork() functionality, which is essential for mc*apply, on Windows.
Permutation based
    non-parametric analysis of CRISPR screen data. Details about this algorithm are published in the following paper published on BMC genomics, Jia et al. (2017) <doi:10.1186/s12864-017-3938-5>: A permutation-based non-parametric analysis of CRISPR screen data. Please cite this paper if you use this algorithm for your paper.
Following the method of Bailey et al., computes for a collection
    of candidate models the probability of backtest overfitting, the
    performance degradation and probability of loss, and the stochastic
    dominance.
Advanced statistical library offering a method to encapsulate and query the probability space of a
    dataset effortlessly using Probability Boxes (p-boxes). Its distinctive feature lies in the ease with
    which users can navigate and analyze marginal, joint, and conditional probabilities while taking into account 
    the underlying correlation structure inherent in the data using copula theory and models. A comprehensive explanation is available in the paper
    "pbox: Exploring Multivariate Spaces with Probability Boxes" to be published in the Journal of Statistical Software.
In short, this package is a locator for cool, refreshing beverages. 
  It will find and return the nearest location where you can get a cold one.
Adds different kinds of brackets to a plot, including braces, chevrons, parentheses or square brackets.
A collection of software provides R support for 'ADMB'
             (Automatic Differentiation Model Builder) and a 'GUI'
             interface facilitates the conversion of 'ADMB' template
             code to 'C code' followed by compilation to a binary executable.
             Stand-alone functions can also be run by users
             not interested in clicking a 'GUI'.
Functions for solving systems of delay differential equations by
   interfacing with numerical routines written by Simon N. Wood, including
   contributions from Benjamin J. Cairns. These numerical routines first
   appeared in Simon Wood's 'solv95' program. This package includes a vignette
   and a complete user's guide. 'PBSddesolve' originally appeared on CRAN under
   the name 'ddesolve'. That version is no longer supported. The current name
   emphasizes a close association with other 'PBS' packages, particularly
   'PBSmodelling'.
This software has evolved from fisheries research conducted at the
   Pacific Biological Station (PBS) in 'Nanaimo', British Columbia, Canada. It
   extends the R language to include two-dimensional plotting features similar
   to those commonly available in a Geographic Information System (GIS).
   Embedded C code speeds algorithms from computational geometry, such as
   finding polygons that contain specified point events or converting between
   longitude-latitude and Universal Transverse Mercator (UTM) coordinates.
   Additionally, we include 'C++' code developed by Angus Johnson for the
   'Clipper' library, data for a global shoreline, and other data sets in the
   public domain. Under the user's R library directory '.libPaths()',
   specifically in './PBSmapping/doc', a complete user's guide is offered and
   should be consulted to use package functions effectively.
Provides software to facilitate the design, testing, and operation
   of computer models. It focuses particularly on tools that make it easy to
   construct and edit a customized graphical user interface ('GUI'). Although our
   simplified 'GUI' language depends heavily on the R interface to the 'Tcl/Tk'
   package, a user does not need to know 'Tcl/Tk'. Examples illustrate models
   built with other R packages, including 'PBSmapping', 'PBSddesolve', and 'BRugs'. 
   A complete user's guide 'PBSmodelling-UG.pdf' shows how to use this package
   effectively.
Computes probabilities of the bivariate normal distribution
    in a vectorized R function (Drezner & Wesolowsky, 1990, 
    <doi:10.1080/00949659008811236>).
Methods to detect genetic markers involved in biological
    adaptation. 'pcadapt' provides statistical tools for outlier detection based on
    Principal Component Analysis. Implements the method described in (Luu, 2016)
    <DOI:10.1111/1755-0998.12592> and later revised in (Privé, 2020) 
    <DOI:10.1093/molbev/msaa053>.
A suite of non-parametric, visual tools for assessing differences in data structures
    for two datasets that contain different observations of the same variables. These tools are all 
    based on Principal Component Analysis (PCA) and thus effectively address differences in the structures
    of the covariance matrices of the two datasets. The PCASDC tools consist of easy-to-use, 
    intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue
    (CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The
    angle plot presents the information loss when moving from the PCA decomposition of one dataset to the 
    PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby
    presenting the relative weighting and importance of the variables from the original dataset.
Calibrate p-values under a robust perspective using the methods developed
    by Sellke, Bayarri, and Berger (2001) <doi:10.1198/000313001300339950> and obtain 
    measures of the evidence provided by the data in favor of point null hypotheses 
    which are safer and more straightforward to interpret.
Implementations of several methods for principal component analysis 
        using the L1 norm.  The package depends on COIN-OR Clp version >= 
        1.17.4.  The methods implemented are 
        PCA-L1 (Kwak 2008) <DOI:10.1109/TPAMI.2008.114>, 
        L1-PCA (Ke and Kanade 2003, 2005) <DOI:10.1109/CVPR.2005.309>, 
        L1-PCA* (Brooks, Dula, and Boone 2013) <DOI:10.1016/j.csda.2012.11.007>, 
        L1-PCAhp (Visentin, Prestwich and Armagan 2016) 
                 <DOI:10.1007/978-3-319-46227-1_37>, 
        wPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>,
        awPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>,
        PCA-Lp (Kwak 2014) <DOI:10.1109/TCYB.2013.2262936>, and
        SharpEl1-PCA (Brooks and Dula, submitted).
Functions for causal structure
  learning and causal inference using graphical models. The main algorithms
  for causal structure learning are PC (for observational data without hidden
  variables), FCI and RFCI (for observational data with hidden variables),
  and GIES (for a mix of data from observational studies
  (i.e. observational data) and data from experiments
  involving interventions (i.e. interventional data) without hidden
  variables). For causal inference the IDA algorithm, the Generalized
  Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC)
  and some related functions are implemented. Functions for incorporating
  background knowledge are provided.
Implements transformations of p-values to the smallest possible Bayes factor within the specified class of alternative hypotheses, as described in Held & Ott (2018, <doi:10.1146/annurev-statistics-031017-100307>). Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test.
Compute the price of different types of call using different methods. The types available are Vanilla European Calls, Vanilla American Calls and American Digital Calls. Available methods are Montecarlo Simulation, Montecarlo Simulation with Antithetic Variates, Black-Scholes and the Binary Tree.
Matches cases to controls based on genotype principal components (PC). 
      In order to produce better results, matches are based on the weighted 
      distance of PCs where the weights are equal to the % variance explained 
      by that PC. A weighted Mahalanobis distance metric (Kidd et al. (1987)
      <DOI:10.1016/0031-3203(87)90066-5>) is used to determine matches.
Implements principal component analysis, orthogonal rotation and multiple
    factor analysis for a mixture of quantitative and qualitative variables.
Fast and Accurate Randomized Singular Value Decomposition (RSVD) methods proposed in the 'PCAone' paper by Li (2023) <https://genome.cshlp.org/content/33/9/1599>.
Accurate classification of breast cancer tumors based on gene expression data is not a trivial task, and it lacks standard practices.The 'PAM50' classifier, which uses 50 gene centroid correlation distances to classify tumors, faces challenges with balancing estrogen receptor (ER) status and gene centering. The 'PCAPAM50' package leverages principal component analysis and iterative 'PAM50' calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping.
Provides functions for robust PCA by projection pursuit.
    The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>,
    Croux et al. (2013) <doi:10.1080/00401706.2012.727746>,
    Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.
Provides an R interface to the 'PCATS' API
    <https://pcats.research.cchmc.org/api/__docs__/>,
    allowing R users to submit tasks and retrieve results.
A system for fast, accurate, and flexible whole genome bisulfite sequencing (WGBS) data analysis of two-condition comparisons. Principal Component BiSulfite, 'PCBS', assigns methylated loci eigenvector values from the treatment-delineating principal component in lieu of running millions of pairwise statistical tests, which dramatically increases analysis flexibility and reduces computational requirements. Methods: <https://katlande.github.io/PCBS/articles/Differential_Methylation.html>.
An implementation of the pediatric complex chronic conditions (CCC)
    classification system using R and C++.
Implements methods to automate the Auer-Gervini graphical
  Bayesian approach for determining the number of significant
  principal components. Automation uses clustering, change points, or
  simple statistical models to distinguish "long" from "short" steps
  in a graph showing the posterior number of components as a function
  of a prior parameter. See <doi:10.1101/237883>.
Method extends multivariate and functional dynamic principal components
    to periodically correlated multivariate time series. This package allows you to
    compute true dynamic principal components in the presence of periodicity. 
    We follow implementation guidelines as described in Kidzinski, Kokoszka and
    Jouzdani (2017), in Principal component analysis of periodically correlated
    functional time series <arXiv:1612.00040>.
Contains the functions for construction and visualization of various families 
    of the proximity catch digraphs (PCDs), see (Ceyhan (2005) ISBN:978-3-639-19063-2),
    for computing the graph invariants for testing the patterns of segregation and association against complete spatial randomness (CSR)
    or uniformity in one, two and three dimensional cases.
    The package also has tools for generating points from these spatial patterns.
    The graph invariants used in testing spatial point data are the domination number (Ceyhan (2011)
    <doi:10.1080/03610921003597211>) and arc density (Ceyhan et al. (2006) <doi:10.1016/j.csda.2005.03.002>;
    Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>). The PCD families considered are Arc-Slice PCDs,
    Proportional-Edge PCDs, and Central Similarity PCDs.
Contains the functions for construction and visualization of underlying and reflexivity graphs of 
            the three families of the proximity catch digraphs (PCDs), see (Ceyhan (2005) ISBN:978-3-639-19063-2),
            and for computing the edge density of these PCD-based graphs which are then
            used for testing the patterns of segregation and association against complete spatial randomness (CSR))
            or uniformity in one and two dimensional cases. 
            The PCD families considered are Arc-Slice PCDs, Proportional-Edge (PE) PCDs (Ceyhan et al. (2006) <doi:10.1016/j.csda.2005.03.002>) 
            and Central Similarity PCDs (Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>). 
            See also (Ceyhan (2016) <doi:10.1016/j.stamet.2016.07.003>) for edge density of the underlying and 
            reflexivity graphs of PE-PCDs.
            The package also has tools for visualization of PCD-based graphs for one, two, and three dimensional data.
Functions for generating progressively Type-II censored data in
    a mixture structure and fitting models using a constrained EM algorithm. It can also
    create a progressive Type-II censored version of a given real dataset to be considered for
    model fitting.
Principal component of explained variance (PCEV) is a statistical
    tool for the analysis of a multivariate response vector. It is a dimension-
    reduction technique, similar to Principal component analysis (PCA), that seeks
    to maximize the proportion of variance (in the response vector) being explained
    by a set of covariates.
Provides convenience functions and pre-programmed
    Stan models related to the paired comparison factor model. Its purpose
    is to make fitting paired comparison data using Stan easy. This
    package is described in Pritikin (2020) <doi:10.1016/j.heliyon.2020.e04821>.
We provide several algorithms to compute the genotype ancestry scores (such as eigenvector projections) in the case where highly correlated individuals are involved.
The package solves linear system of equations Ax=b by using Preconditioned Conjugate Gradient Algorithm where A is real symmetric positive definite matrix. A suitable preconditioner matrix may be provided by user. This can also be used to minimize quadratic function (x'Ax)/2-bx for unknown x.
Implements the pcgen algorithm, which is a modified version of the standard pc-algorithm,
             with specific conditional independence tests and modified orientation rules. pcgen extends 
			 the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of 
			 direct genetic effects.
Large-scale gene expression studies allow gene network construction to uncover associations among genes. This package is developed for estimating and testing partial correlation graphs with prior information incorporated.
Contains logic for computing the statistical association of variable groups, i.e., gene sets, with respect to the principal components of genomic data.
Piecewise constant hazard models for survival data. 
    The package allows for right-censored, left-truncated, and interval-censored data.
Bayesian network learning using the PCHC, FEDHC, MMHC and variants of these algorithms. PCHC stands for PC Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of the BN and then 
			 applies the Hill-Climbing greedy search. More algorithms and variants have been added, such as MMHC, FEDHC, and the Tabu search variants, PCTABU, MMTABU and FEDTABU. 
			 The relevant papers are:
			 a) Tsagris M. (2021). "A new scalable Bayesian network learning algorithm with applications to economics". Computational Economics, 57(1): 341-367. <doi:10.1007/s10614-020-10065-7>. 
			 b) Tsagris M. (2022). "The FEDHC Bayesian Network Learning Algorithm". Mathematics 2022, 10(15): 2604. <doi:10.3390/math10152604>.
A collection of process capability index functions, such as
  C_p(), C_pk(), C_pm(), and others, along with metadata about each, like
  'LaTeX' equations and 'R' expressions. Its primary purpose is to form a
  foundation for other quality control packages to build on top of, by providing
  basic resources and functions. The indices belong to the field of statistical
  quality control, and quantify the degree to which a manufacturing process is
  able to create items that adhere to a certain standard of quality. For details
  see Montgomery, D. C. (2019, ISBN:978-1-119-39930-8).
Provides a work-alike to R's POSIXct class which implements
  360- and 365-day calendars in addition to the gregorian calendar.
Estimates the multidimensional polytomous Rasch model (Rasch, 1961) with conditional maximum likelihood estimation.
We fit causal models using proxies. We implement two stage proximal least squares estimator. E.J. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao. (2020). An Introduction to Proximal Causal Learning. arXiv e-prints, arXiv-2009 <arXiv:2009.10982>.
A method for fitting the entire regularization path 
    of the principal components lasso for linear and
    logistic regression models. The algorithm uses cyclic coordinate descent
    in a path-wise fashion. See URL below for more information on the algorithm.
    See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression'
    <arXiv:1810.04651>.
Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. PCLasso is a prognostic model that identifies risk protein complexes associated with survival. PCLasso2 is a classification model that identifies risk protein complexes associated with classes. For more information, see Wang and Liu (2021) <doi:10.1093/bib/bbab212>.
Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio' (2019) <doi:10.5506/APhysPolBSupp.12.25>. The suggested geiger package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/geiger/>, suggested to take latest version. Otherwise its required code is present in the pcmabc package. The suggested distory package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/distory/>, suggested to take latest version.
Phylogenetic comparative methods represent models of continuous trait 
  data associated with the tips of a phylogenetic tree. Examples of such models 
  are Gaussian continuous time branching stochastic processes such as Brownian 
  motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the 
  tips of the tree as an observed (final) state of a Markov process starting from 
  an initial state at the root and evolving along the branches of the tree. The 
  PCMBase R package provides a general framework for manipulating such models. 
  This framework consists of an application programming interface for specifying 
  data and model parameters, and efficient algorithms for simulating trait evolution 
  under a model and calculating the likelihood of model parameters for an assumed
  model and trait data. The package implements a growing collection of models, 
  which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed 
  Gaussian models, in which different types of the above models can be associated 
  with different branches of the tree. The PCMBase package is limited to 
  trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic 
  models. The PCMFit package provides functionality for inference of 
  these models to tree and trait data. The package web-site 
  <https://venelin.github.io/PCMBase/>
  provides access to the documentation and other resources.
Provides a C++ backend for multivariate phylogenetic comparative
  models implemented in the R-package 'PCMBase'. Can be used in combination
  with 'PCMBase' to enable fast and parallel likelihood calculation. Implements
  the pruning likelihood calculation algorithm described in Mitov et al. (2018) 
  <arXiv:1809.09014>. Uses the 'SPLITT' C++ library for parallel tree traversal 
  described in Mitov and Stadler (2018) <doi:10.1111/2041-210X.13136>.
Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2018) <doi:10.1177/0146621617748322> .  PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models.
Performs Bayesian arm-based network meta-analysis for datasets with binary, continuous, and count outcomes
 (Zhang et al., 2014 <doi:10.1177/1740774513498322>;
  Lin et al., 2017 <doi:10.18637/jss.v080.i05>).
Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.) 
             criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V., (2019)
             <https://hal.archives-ouvertes.fr/hal-02002275>. 
             It can be used with univariate and multivariate data.
An implementation of the parameter cascade method in Ramsay, J. O., Hooker,G., Campbell, D., and Cao, J. (2007) for estimating ordinary differential equation models with missing or complete observations. It combines smoothing method and profile estimation to estimate any non-linear dynamic system. The package also offers variance estimates for parameters of interest based on either bootstrap or Delta method.
Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components (de Jong S. & Kiers H. A. L. (1992) <doi:10.1016/0169-7439(92)80100-I>). Several rotation and model selection options are provided.
Set of functions for analysis of Principal Coordinates of Phylogenetic Structure (PCPS).
Calculates the amplification efficiency and curves from real-time 
  quantitative PCR (Polymerase Chain Reaction) data. Estimates the relative 
  expression from PCR data using the double delta CT and the standard curve 
  methods Livak & Schmittgen (2001) <doi:10.1006/meth.2001.1262>. Tests for 
  statistical significance using two-group tests and linear regression 
  Yuan et al. (2006) <doi: 10.1186/1471-2105-7-85>.
A collection of functions and data sets that support teaching
  a quantitative finance MS level course on Portfolio Construction and Risk
  Analysis, and the writing of a textbook for such a course.  The package is
  unique in providing several real-world data sets that may be used for problem
  assignments and student projects.  The data sets include cross-sections of
  stock data from the Center for Research on Security Prices, LLC (CRSP),
  corresponding factor exposures data from S&P Global, and several SP500 data
  sets.
Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations?  This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.
A function to estimate
        panel-corrected standard errors. Data may contain balanced or
        unbalanced panels.
Parallel Constraint Satisfaction (PCS) models are an increasingly
    common class of models in Psychology, with applications to reading and word
    recognition (McClelland & Rumelhart, 1981), judgment and decision making
    (Glöckner & Betsch, 2008; Glöckner, Hilbig, & Jekel, 2014), and several
    other fields (e.g. Read, Vanman, & Miller, 1997). In each of these fields,
    they provide a quantitative model of psychological phenomena, with precise
    predictions regarding choice probabilities, decision times, and often the degree
    of confidence. This package provides the necessary functions to create and
    simulate basic Parallel Constraint Satisfaction networks within R.
Defines functions to describe regression models using only
    pre-computed summary statistics (i.e. means, variances, and covariances)
    in place of individual participant data.
    Possible models include linear models for linear combinations, products, 
    and logical combinations of phenotypes.
    Implements methods presented in 
    Wolf et al. (2021) <doi:10.3389/fgene.2021.745901>
    Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and 
    Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>.
The Prize-Collecting Steiner Tree problem asks to find a subgraph
    connecting a given set of vertices with the most expensive nodes and least
    expensive edges. Since it is proven to be NP-hard, exact and efficient algorithm
    does not exist. This package provides convenient functionality for obtaining an
    approximate solution to this problem using loopy belief propagation algorithm.
Provides a comprehensive suite of tools for analyzing omics data. It includes functionalities for alpha diversity analysis, beta
                diversity analysis, differential abundance analysis, community assembly analysis, visualization of phylogenetic tree, and
                functional enrichment analysis. With a progressive approach, the package offers a range of analysis methods to explore and
                understand the complex communities. It is designed to support researchers and practitioners in conducting in-depth and
                professional omics data analysis.
Classes and methods for modelling and simulation of
    periodically correlated (PC) and periodically integrated time
    series.  Compute theoretical periodic autocovariances and related
    properties of PC autoregressive moving average models. Some original
    methods including Boshnakov & Iqelan (2009)
    <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996)
    <doi:10.1111/j.1467-9892.1996.tb00281.x>.
Offers a range of utilities and functions for everyday programming tasks. 1.Data Manipulation. Such as grouping
              and merging, column splitting, and character expansion. 2.File Handling. Read and convert files in popular
              formats. 3.Plotting Assistance. Helpful utilities for generating color palettes, validating color formats, and
              adding transparency. 4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing
              corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides efficient tools for
              creating doughnut plot and multi-layered doughnut plot; Venn diagrams, including traditional Venn diagrams,
              upset plots, and flower plots; Simplified functions for creating stacked bar plots, or a box plot with
              alphabets group for multiple comparison group.
Implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2023) <doi:10.1016/j.aca.2023.341096>.
Analyse common types of plant phenotyping data, provide a simplified interface
	to longitudinal growth modeling and select Bayesian statistics,
	and streamline use of 'PlantCV' output.
	Several Bayesian methods and reporting guidelines for Bayesian methods are described in
	Kruschke (2018) <doi:10.1177/2515245918771304>,
	Kruschke (2013) <doi:10.1037/a0029146>, and Kruschke (2021) <doi:10.1038/s41562-021-01177-7>.
A collection of privacy-preserving distributed algorithms for conducting multi-site data analyses. The regression analyses can be linear regression for continuous outcome, logistic regression for binary outcome, Cox proportional hazard regression for time-to event outcome, Poisson regression for count outcome, or multi-categorical regression for nominal or ordinal outcome. The PDA algorithm runs on a lead site and only requires summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online system (<https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.
Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.
The PDE (Pdf Data Extractor) allows the extraction of 
    information and tables optionally based on search words from 
    PDF (Portable Document Format) files and enables the visualization 
    of the results, both by providing a convenient user-interface.
Data sets for the Panel Data Econometrics with R <doi:10.1002/9781119504641> book.
Cluster analysis via nonparametric density 
   estimation is performed. Operationally, the kernel method is used throughout to estimate
   the density. Diagnostics methods for evaluating the quality of the clustering 
   are available. The package includes also a routine to estimate the 
   probability density function obtained by the kernel method, given a set of
   data with arbitrary dimensions.
Farmer, J., D. Jacobs (2108) <DOI:10.1371/journal.pone.0196937>. A multivariate nonparametric density estimator based on the maximum-entropy method.  Accurately predicts a probability density function (PDF) for random data using a novel iterative scoring function to determine the best fit without overfitting to the sample.
Download economic and financial time series from public sources, 
  including the St Louis Fed's FRED system, Yahoo Finance, the US Bureau of Labor Statistics, 
  the US Energy Information Administration, the World Bank, Eurostat, the European Central Bank,
  the Bank of England, the UK's Office of National Statistics, Deutsche Bundesbank, and INSEE.
Provides an interface to 'PDFMiner' <https://github.com/pdfminer/pdfminer.six> 
    a 'Python' package for extracting information from 'PDF'-files. 
    'PDFMiner' has the goal to get all information available in a 'PDF'-file, 
    position of the characters, font type, font size and informations about lines. 
    Which makes it the perfect starting point for extracting tables from 'PDF'-files.
    More information can be found in the package 'README'-file.
Includes functions for keyword search of pdf files. There is
    also a wrapper that includes searching of all files within a single
    directory.
Allows the user to convert PDF tables to formats more amenable to
  analysis ('.csv', '.xml', or '.xlsx') by wrapping the PDFTables API.
  In order to use the package, the user needs to sign up for an API account
  on the PDFTables website (<https://pdftables.com/pdf-to-excel-api>).
  The package works by taking a PDF file as input, uploading it to PDFTables,
  and returning a file with the extracted data.
Utilities based on 'libpoppler' for extracting text, fonts, attachments and 
    metadata from a PDF file. Also supports high quality rendering of PDF documents into
    PNG, JPEG, TIFF format, or into raw bitmap vectors for further processing in R.
Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2121) <doi:10.1016/j.foreco.2021.118948>.
Computes the euclidean distance between rows of a matrix X
        and rows of another matrix Y.  Previously, this could be done
        by binding the two matrices together and calling 'dist', but
        this creates unnecessary computation by computing the distances
        between a row of X and another row of X, and likewise for Y.
        pdist strictly computes distances across the two matrices, not
        within the same matrix, making computations significantly
        faster for certain use cases.
Measures real distances in pictures. With PDM() function, you can choose one '*.jpg' file, select the measure in mm of scale, starting and and finishing point in the graphical scale, the name of the measure, and starting and and finishing point of the measures. After, ask the user for a new measure.
Fits heterogeneous panel data models with interactive effects for linear regression, logistic, count, probit, quantile, and clustering. Based on Ando, T. and Bai, J. (2015) "A simple new test for slope homogeneity in panel data models with interactive effects" <doi: 10.1016/j.econlet.2015.09.019>, Ando, T. and Bai, J. (2015) "Asset Pricing with a General Multifactor Structure" <doi: 10.1093/jjfinex/nbu026> , Ando, T. and Bai, J. (2016) "Panel data models with grouped factor structure under unknown group membership" <doi: 10.1002/jae.2467>, Ando, T. and Bai, J. (2017) "Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures" <doi: 10.1080/01621459.2016.1195743>, Ando, T. and Bai, J. (2020) "Quantile co-movement in financial markets" <doi: 10.1080/01621459.2018.1543598>, Ando, T., Bai, J. and Li, K. (2021) "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity" <doi: 10.1016/j.jeconom.2020.11.013.>.
Fits a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.
Building patient level networks for prediction of 
        medical outcomes and draw the cluster of network.        
        This package is based on paper Personalized disease 
        networks for understanding and predicting cardiovascular 
        diseases and other complex processes 
        (See Cabrera et al. <http://circ.ahajournals.org/content/134/Suppl_1/A14957>).
A general framework for constructing partial dependence (i.e.,
        marginal effect) plots from various types machine learning models
        in R.
Looks for amino acid and/or nucleotide patterns and/or small
    ligands coordinated to a given prosthetic centre. Files have to be in the local
    file system and contain proper extension.
Create, transform, and summarize custom random
    variables with distribution functions (analogues of 'p*()', 'd*()',
    'q*()', and 'r*()' functions from base R). Two types of distributions
    are supported: "discrete" (random variable has finite number of output
    values) and "continuous" (infinite number of values in the form of
    continuous random variable). Functions for distribution
    transformations and summaries are available. Implemented approaches
    often emphasize approximate and numerical solutions: all distributions
    assume finite support and finite values of density function; some
    methods implemented with simulation techniques.
A collection of tools for approximating the 'PDQ' functions
    (respectively, the cumulative distribution, density, and quantile) of
    probability distributions via classical expansions involving moments and
    cumulants.
Threshold model, panel version of Hylleberg et al. (1990) <DOI:10.1016/0304-4076(90)90080-D> seasonal unit root tests, and panel unit root test of Chang (2002) <DOI:10.1016/S0304-4076(02)00095-7>.
Compute and tune some positive definite and sparse covariance
  estimators.
Interactive shiny application for working with Probability Distributions. Calculations and  Graphs are provided.
An implementation of data analysis tools for samples of symmetric or 
  Hermitian positive definite matrices, such as collections of covariance matrices 
  or spectral density matrices. The tools in this package can be used to perform: (i) 
  intrinsic wavelet transforms for curves (1D) or surfaces (2D) of Hermitian positive 
  definite matrices with applications to dimension reduction, denoising and clustering in the 
  space of Hermitian positive definite matrices; and (ii) exploratory data analysis and inference 
  for samples of positive definite matrices by means of intrinsic data depth functions and 
  rank-based hypothesis tests in the space of Hermitian positive definite matrices.
Permutation (randomisation) test for single-case phase design data with 
    two phases (e.g., pre- and post-treatment). Correction for dependency of observations 
    is done through stepwise resampling the time series while varying 
    the distance between observations. The required distance 0,1,2,3.. is determined 
    based on repeated dependency testing while stepwise increasing the distance.
    In preparation: Vroegindeweij et al. "A Permutation distancing test 
    for single-case observational AB phase design data: A Monte Carlo simulation study".
The goal of this package is to cover the most common steps in probability of default (PD) rating model development and validation. 
	     The main procedures available are those that refer to univariate, bivariate, multivariate analysis, calibration and validation. 
	     Along with accompanied 'monobin' and 'monobinShiny' packages, 'PDtoolkit' provides functions which are suitable for different 
	     data transformation and modeling tasks such as: 
	     imputations, monotonic binning of numeric risk factors, binning of categorical risk factors, weights of evidence (WoE) and 
	     information value (IV) calculations, WoE coding (replacement of risk factors modalities with WoE values), risk factor clustering, 
	     area under curve (AUC) calculation and others. Additionally, package provides set of validation functions for testing homogeneity, 
	     heterogeneity, discriminatory and predictive power of the model.
Conduct simulation-based customized power calculation 
             for clustered time to event data in a mixed crossed/nested design,
             where a number of cell lines and a number of mice within each cell line are considered to achieve a desired statistical power,
             motivated by Eckel-Passow and colleagues (2021) <doi:10.1093/neuonc/noab137> and Li and colleagues (2024) <doi:10.48550/arXiv.2404.08927>.
             This package provides two commonly used models for powering a design, linear mixed effects and Cox frailty model. Both models account for
             within-subject (cell line) correlation while holding different distributional assumptions about the outcome. 
             Alternatively, the counterparts of fixed effects model are also available, which produces similar estimates of statistical power.
An engaging collection of datasets from Portland Parks and Recreation. The city of Portland inventoried every tree in over 170 parks and along the streets in 96 neighborhoods.
Linear dynamic panel data modeling based on linear and
    nonlinear moment conditions as proposed by
    Holtz-Eakin, Newey, and Rosen (1988) <doi:10.2307/1913103>,
    Ahn and Schmidt (1995) <doi:10.1016/0304-4076(94)01641-C>,
    and Arellano and Bover (1995) <doi:10.1016/0304-4076(94)01642-D>.
    Estimation of the model parameters relies on the Generalized
    Method of Moments (GMM) and instrumental variables (IV) estimation,
    numerical optimization (when nonlinear moment conditions are
    employed) and the computation of closed form solutions (when
    estimation is based on linear moment conditions). One-step,
    two-step and iterated estimation is available. For inference
    and specification
    testing, Windmeijer (2005) <doi:10.1016/j.jeconom.2004.02.005>
    and doubly corrected standard errors
    (Hwang, Kang, Lee, 2021 <doi:10.1016/j.jeconom.2020.09.010>)
    are available. Additionally, serial correlation tests, tests for
    overidentification, and Wald tests are provided. Functions for
    visualizing panel data structures and modeling results obtained
    from GMM estimation are also available. The plot methods include
    functions to plot unbalanced panel structure, coefficient ranges
    and coefficient paths across GMM iterations (the latter is
    implemented according to the plot shown in
    Hansen and Lee, 2021 <doi:10.3982/ECTA16274>).
    For a more detailed description of the GMM-based functionality,
    please see Fritsch, Pua, Schnurbus (2021) <doi:10.32614/RJ-2021-035>.
    For more detail on the IV-based estimation routines,
    see Fritsch, Pua, and Schnurbus (WP, 2024) and
    Han and Phillips (2010) <doi:10.1017/S026646660909063X>.
These are useful tools and data sets for the study of quantitative 
    peace science. The goal for this package is to include tools and data sets
    for doing original research that mimics well what a user would have to previously
    get from a software package that may not be well-sourced or well-supported.
    Those software bundles were useful the extent to which they encourage replications 
    of long-standing analyses by starting the data-generating process from scratch. However, 
    a lot of the functionality can be done relatively quickly and more transparently
    in the R programming language.
A meta gene set analysis tool developed based on principles of Pareto dominance (William B T Mock (2011) <doi:10.1007/978-1-4020-9160-5_341>). It is designed to combine gene set analysis p-values from multiple transcriptome datasets (e.g., microarray and RNA-Seq). The novel Pareto method for p-value combination allows PEACH to properly model heterogeneity and correlation in Omics datasets.
The original definition of the two and three dimensional Kolmogorov-Smirnov two-sample
             test statistics given by Peacock (1983) is implemented. Two R-functions: peacock2 and peacock3, 
             are provided to compute the test statistics in two and three dimensional spaces, respectively. 
             Note the Peacock test is different from the Fasano and Franceschini test (1987). The latter is 
             a variant of the Peacock test.
Calculates the periodogram of a time series, maximum-likelihood fits an Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical significance of periodogram peaks against the OUSS null hypothesis. The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series. Citation: Louca, Stilianos and Doebeli, Michael (2015) <doi:10.1890/14-0126.1>.
Chromatin immunoprecipitation DNA sequencing results in genomic
    tracks that show enriched regions or peaks where proteins are bound.
    This package implements fast C code that computes the true and false
    positives with respect to a database of annotated region labels.
When working with big data sets, RAM conservation is critically
    important. However, it is not always enough to just monitor the
    size of the objects created. So-called "copy-on-modify" behavior,
    characteristic of R, means that some expressions or functions may
    require an unexpectedly large amount of RAM overhead. For example,
    replacing a single value in a matrix duplicates that matrix in the
    back-end, making this task require twice as much RAM as that used
    by the matrix itself. This package makes it easy to monitor the total
    and peak RAM used so that developers can quickly identify and
    eliminate RAM hungry code.
Disk-based implementation of
 Functional Pruning Optimal Partitioning with up-down constraints
 <doi:10.18637/jss.v101.i10> for single-sample peak calling
 (independently for each sample and genomic problem),
 can handle huge data sets (10^7 or more).
A quadratic time dynamic programming algorithm
 can be used to compute an approximate solution to the problem of
 finding the most likely changepoints
 with respect to the Poisson likelihood, subject
 to a constraint on the number of segments, and the changes which must
 alternate: up, down, up, down, etc. For more info read
 <http://proceedings.mlr.press/v37/hocking15.html>
 "PeakSeg: constrained optimal segmentation and supervised penalty learning
 for peak detection in count data" by TD Hocking et al,
 proceedings of ICML2015.
Jointly segment several ChIP-seq samples to find the peaks 
 which are the same and different across samples. The fast approximate
 maximum Poisson likelihood algorithm is described in
 "PeakSegJoint: fast supervised peak detection via joint segmentation
 of multiple count data samples"
 <doi:10.48550/arXiv.1506.01286>
 by TD Hocking and G Bourque.
Computes optimal changepoint models using the
 Poisson likelihood for non-negative count data,
 subject to the PeakSeg constraint:
 the first change must be up, second change down, third change up, etc.
 For more info about the models and algorithms,
 read "Constrained Dynamic Programming and Supervised Penalty Learning 
 Algorithms for Peak Detection"
 <https://jmlr.org/papers/v21/18-843.html> by TD Hocking et al.
Supports maximum likelihood inference for the Pearson VII
    distribution with shape parameter 3/2 and free location and scale
    parameters. This distribution is relevant when estimating the velocity of
    processive motor proteins with random detachment.
Implementation of the Pearson distribution system, including full
  support for the (d,p,q,r)-family of functions for probability distributions 
  and fitting via method of moments and maximum likelihood method.
The Pearson-ICA algorithm is a mutual information-based
        method for blind separation of statistically independent source
        signals. It has been shown that the minimization of mutual
        information leads to iterative use of score functions, i.e.
        derivatives of log densities. The Pearson system allows
        adaptive modeling of score functions. The flexibility of the
        Pearson system makes it possible to model a wide range of
        source distributions including asymmetric distributions. The
        algorithm is designed especially for problems with asymmetric
        sources but it works for symmetric sources as well.
Validation of risk predictions obtained from survival models and
    competing risk models based on censored data using inverse weighting and
    cross-validation. Most of the 'pec' functionality has been moved to 'riskRegression'.
It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework.
Data files and documentation for PEDiatric vALidation oF vAriableS
    in TBI (PEDALFAST).  The data was used in "Functional Status Scale in
    Children With Traumatic Brain Injury: A Prospective Cohort Study" by Bennett,
    Dixon, et al  (2016) <doi:10.1097/PCC.0000000000000934>.
Data and utilities for estimating pediatric blood pressure
    percentiles by sex, age, and optionally height (stature) as described in
    Martin et.al. (2022) <doi:10.1001/jamanetworkopen.2022.36918>.
    Blood pressure percentiles for children under one year of age come from Gemelli
    et.al. (1990) <doi:10.1007/BF02171556>.  Estimates of blood pressure
    percentiles for children at least one year of age are informed by
    data from the National Heart, Lung, and Blood Institute (NHLBI) and the
    Centers for Disease Control and Prevention (CDC)
    <doi:10.1542/peds.2009-2107C> or from Lo et.al. (2013)
    <doi:10.1542/peds.2012-1292>.  The flowchart for selecting the informing
    data source comes from Martin et.al. (2022)
    <doi:10.1542/hpeds.2021-005998>.
Reconstruct pedigrees from genotype data, by optimising the
    likelihood over all possible pedigrees subject to given restrictions.
    Tailor-made plots facilitate evaluation of the output. This package is
    part of the 'pedsuite' ecosystem for pedigree analysis. In
    particular, it imports 'pedprobr' for calculating pedigree likelihoods
    and 'forrel' for estimating pairwise relatedness.
Tools for exchanging pedigree data between the 'pedsuite'
    packages and the 'Familias' software for forensic kinship computations
    (Egeland et al. (2000) <doi:10.1016/s0379-0738(00)00147-x>). These
    functions were split out from the 'forrel' package to streamline
    maintenance and provide a lightweight alternative for packages otherwise
    independent of 'forrel'.
Gene-level variant association tests with disease status for pedigree data: kernel and burden association statistics.
Tools to sort, edit and prune pedigrees and to extract the inbreeding coefficients
    and the relationship matrix (includes code for pedigrees from self-pollinated species). 
    The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict 
    breeding values. The relationship matrix between the individuals can be derived from pedigree structure 
    ('Vazquez et al., 2010') <doi:10.2527/jas.2009-1952>.
Generate Mermaid syntax for a pedigree flowchart from a pedigree data frame.
    Mermaid syntax is commonly used to generate plots, charts, diagrams, and flowcharts. It is a textual syntax for creating reproducible illustrations. This package generates Mermaid syntax from a pedigree data frame to visualize a pedigree flowchart. The Mermaid syntax can be embedded in a Markdown or R Markdown file, or viewed on Mermaid editors and renderers. Links' shape, style, and orientation can be customized via function arguments, and nodes' shapes and styles can be customized via optional columns in the pedigree data frame.
A collection of functions for modelling mutations in
    pedigrees with marker data, as used e.g. in likelihood computations
    with microsatellite data. Implemented models include equal,
    proportional and stepwise models, as well as random models for
    experimental work, and custom models allowing the user to apply any
    valid mutation matrix. Allele lumping is done following the
    lumpability criteria of Kemeny and Snell (1976), ISBN:0387901922.
An R implementation of methods employed in the field of pedometrics, soil science
    discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil
    using statistical and computational methods. The methods found here include the calibration of
    linear regression models using covariate selection strategies, computation of summary validation
    statistics for predictions, generation of summary plots, evaluation of the local quality of a
    geostatistical model of uncertainty, and so on. Other functions simply extend the
    functionalities of or facilitate the usage of functions from other packages that are commonly
    used for the analysis of soil data. Formerly available versions of suggested packages no longer
    available from CRAN can be obtained from the CRAN archive
    <https://cran.r-project.org/src/contrib/Archive/>.
An implementation of the Elston-Stewart algorithm for
    calculating pedigree likelihoods given genetic marker data (Elston and
    Stewart (1971) <doi:10.1159/000152448>). The standard algorithm is
    extended to allow inbred founders. 'pedprobr' is part of the 'ped
    suite', a collection of packages for pedigree analysis in R. In
    particular, 'pedprobr' depends on 'pedtools' for pedigree
    manipulations and 'pedmut' for mutation modelling. For more
    information, see 'Pedigree Analysis in R' (Vigeland, 2021,
    ISBN:9780128244302).
Provides an interface to access public economic and financial data for 
    economic research and quantitative analysis. The data sources including 
    NBS, FRED, Sina, Eastmoney and etc. It also provides quantitative 
    functions for trading strategies based on the 'data.table', 'TTR', 
    'PerformanceAnalytics' and etc packages.
Simulate pedigree, genetic merits and phenotypes with random/non-random matings followed by random/non-random selection with different intensities and patterns in males and females. Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes.
   Mrode, R. A. (2005) <ISBN:9780851989969, 0851989969>;
   Nilforooshan, M.A. (2022) <doi:10.37496/rbz5120210131>.
The 'pedsuite' is a collection of packages for pedigree
    analysis, covering applications in forensic genetics, medical genetics
    and more. A detailed presentation of the 'pedsuite' is given in the
    book 'Pedigree Analysis in R' (Vigeland, 2021, ISBN: 9780128244302).
A comprehensive collection of tools for creating,
    manipulating and visualising pedigrees and genetic marker data.
    Pedigrees can be read from text files or created on the fly with
    built-in functions. A range of utilities enable modifications like
    adding or removing individuals, breaking loops, and merging pedigrees.
    An online tool for creating pedigrees interactively, based on
    'pedtools', is available at <https://magnusdv.shinyapps.io/quickped>.
    'pedtools' is the hub of the 'pedsuite', a collection of packages for
    pedigree analysis. A detailed presentation of the 'pedsuite' is given
    in the book 'Pedigree Analysis in R' (Vigeland, 2021,
    ISBN:9780128244302).
Sensitivity and power analysis,
  for calculating statistics describing pedigrees from wild populations,
  and for visualizing pedigrees.
Enables direct cloud access to health care decision models hosted on the PRISM server of the Peer Models Network.
Provides functions to perform the peer performance
    analysis of funds' returns as described in Ardia and Boudt (2018) <doi:10.1016/j.jbankfin.2017.10.014>.
Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks.
The population proportion using group testing can be estimated by different methods. Four functions including p.mle(), p.gart(), p.burrow() and p.order() are provided to implement four estimating methods including the maximum likelihood estimate, Gart's estimate, Burrow's estimate, and order statistic estimate.
Functions and mined database from 'UniProt' focusing on post-translational modifications to do single enrichment analysis (SEA) and protein set enrichment analysis (PSEA). Payman Nickchi, Mehdi Mirzaie, Marc Baumann, Amir Ata Saei, Mohieddin Jafari (2022) <bioRxiv:10.1101/2022.11.09.515610>.
Several functions introduced in Aster et al.'s book on inverse theory. The functions are often translations of MATLAB code developed by the authors to illustrate concepts of inverse theory as applied to geophysics. Generalized inversion, tomographic inversion algorithms (conjugate gradients, 'ART' and 'SIRT'), non-linear least squares, first and second order Tikhonov regularization, roughness constraints, and procedures for estimating smoothing parameters are included.
Bayesian supervised predictive classifiers, hypothesis testing, and parametric estimation under Partition Exchangeability are implemented. The two classifiers presented are the marginal classifier (that assumes test data is i.i.d.) next to a more computationally costly but accurate simultaneous classifier (that finds a labelling for the entire test dataset at once based on simultanous use of all the test data to predict each label). We also provide the Maximum Likelihood Estimation (MLE) of the only underlying parameter of the partition exchangeability generative model as well as hypothesis testing statistics for equality of this parameter with a single value, alternative, or multiple samples. We present functions to simulate the sequences from Ewens Sampling Formula as the realisation of the Poisson-Dirichlet distribution and their respective probabilities.
Historic Pell grant data as provided by the US Department of Education. 
    This package contains data about how much pell grant was awarded by which institution in 
    which year. 
    This data comes from the US Department of Education. Raw data can be downloaded from here: <https://www2.ed.gov/finaid/prof/resources/data/pell-institution.html>.
An R-Shiny application implementing a method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits <doi:10.1002/ajpa.23855>.
Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper
    (2023) <doi:10.31234/osf.io/6phs5>. In meta-analysis, there are
    often between-study differences. These can be coded as moderator variables,
    and controlled for using meta-regression. However, if the number of
    moderators is large relative to the number of studies, such an analysis may
    be overfit. Penalized meta-regression is useful in these cases, because
    it shrinks the regression slopes of irrelevant moderators towards zero.
A system contains easy-to-use tools for the conditional estimation of the prevalence of an emerging or rare infectious diseases using the methods proposed in Guerrier et al. (2023) <arXiv:2012.10745>.
Utility functions for the handling, analysis and visualisation of data
         from portable emissions measurement systems ('PEMS') and other similar
         mobile activity monitoring devices. The package includes a dedicated 'pems'
         data class that manages many of the quality control, unit handling and data
         archiving issues that can hinder efforts to standardise 'PEMS' research.
We aim for fitting a multinomial regression model with Lasso penalty and doing statistical inference (calculating confidence intervals of coefficients and p-values for individual variables). It implements 1) the coordinate descent algorithm to fit an l1-penalized multinomial regression model (parameterized with a reference level); 2) the debiasing approach to obtain the inference results, which is described in Tian et al. (2023) <arXiv:2302.02310>.
The semiparametric accelerated failure time (AFT) model is an attractive alternative to the Cox proportional hazards model. This package provides a suite of functions for fitting one popular estimator of the semiparametric AFT model, the regularized Gehan estimator. Specifically, we provide functions for cross-validation, prediction, coefficient extraction, and visualizing both trace plots and cross-validation curves. For further details, please see Suder, P. M. and Molstad, A. J., (2022+) Scalable algorithms for semiparametric accelerated failure time models in high dimensions, to appear in Statistics in Medicine <doi:10.1002/sim.9264>.
Fitting possibly high dimensional penalized
        regression models. The penalty structure can be any combination
        of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a
        positivity constraint on the regression coefficients. The
        supported regression models are linear, logistic and Poisson
        regression and the Cox Proportional Hazards model.
        Cross-validation routines allow optimization of the tuning
        parameters.
Utilize the CDF penalty function to estimate a penalized linear model. 
  It enables you to display some graphical representations and determine whether the Karush-Kuhn-Tucker conditions are met. 
  For more details about the theory, please refer to Cuntrera, D., Augugliaro, L., & Muggeo, V. M. (2022) <arXiv:2212.08582>.
Implements L1 and L2 penalized conditional logistic regression with penalty 
    factors allowing for integration of multiple data sources. Implements
    stability selection for variable selection.
Support Vector Machine (SVM) classification with simultaneous feature selection using penalty
        functions is implemented. The smoothly clipped absolute deviation (SCAD),
        'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
        SCAD' (SCAD and 'L2-norm') penalties are available. The tuning
        parameters can be found using either a fixed grid or a interval
        search.
Implementations of algorithms from 
 Learning Sparse Penalties for Change-point Detection
 using Max Margin Interval Regression, by
 Hocking, Rigaill, Vert, Bach
 <http://proceedings.mlr.press/v28/hocking13.html>
 published in proceedings of ICML2013.
Computes penalized regression calibration (PRC), a
    statistical method for the dynamic prediction of survival when many
    longitudinal predictors are available. PRC is described in Signorelli
    (2024) <doi:10.48550/arXiv.2309.15600> and in Signorelli et al. (2021)
    <doi:10.1002/sim.9178>.
Estimating Non-Simplified Vine Copulas Using Penalized Splines.
Different regularization approaches for Cox Frailty Models by penalization methods are provided.
Estimation of univariate (conditional) densities using penalized B-splines with automatic selection of optimal smoothing parameter.
Fits single- and multiple-group penalized factor analysis models 
    via a trust-region algorithm with integrated automatic multiple tuning 
    parameter selection (Geminiani et al., 2021 <doi:10.1007/s11336-021-09751-8>). 
    Available penalties include lasso, adaptive lasso, scad, mcp, and ridge.
Currently incorporate the generalized odds-rate model (a type of linear 
    transformation model) for interval-censored data based on penalized monotonic B-Spline. 
    More methods under other semiparametric models such as cure model or additive model will
    be included in future versions. For more details see Lu, M., Liu, Y., Li, C. and Sun, J. 
    (2019) <arXiv:1912.11703>.
Structured fusion Lasso penalized estimation of multi-state models with the penalty applied to absolute effects and absolute effect differences (i.e., effects on transition-type specific hazard rates).
Implementation of the semi-parametric proportional-hazards (PH) of Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x> extended to time-varying covariates. Estimation and variable selection are based on the methodology described in Beretta and Heuchenne (2019) <doi:10.1080/02664763.2018.1554627>; confidence intervals of the parameter estimates may be computed using a bootstrap approach. Moreover, data following the PH cure model may be simulated using a method similar to Hendry (2014) <doi:10.1002/sim.5945>, where the event-times are generated on a continuous scale from a piecewise exponential distribution conditional on time-varying covariates.
A set of tools that enables efficient estimation of penalized 
    Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models 
    that feature one or more sets of high-dimensional fixed effects. The methodology is based on 
    Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) <http://hdl.handle.net/10986/35451> 
    and takes advantage of the method of alternating projections of Gaure (2013) 
    <doi:10.1016/j.csda.2013.03.024> for dealing with HDFE, as well as 
    the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) 
    <doi:10.18637/jss.v033.i01> for fitting lasso regressions. The package is also able to carry out 
    cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) 
    <doi:10.1080/07350015.2015.1102733>.
Robust penalized (adaptive) elastic net S and M estimators for
    linear regression. The methods are proposed in
    Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E.
    (2019) <https://projecteuclid.org/euclid.aoas/1574910036>.
    The package implements the extensions and algorithms described in
    Kepplinger, D. (2020) <doi:10.14288/1.0392915>.
Simulation of continuous, correlated high-dimensional data with 
    time to event or binary response, and parallelized functions for Lasso, 
    Ridge, and Elastic Net penalized regression with repeated starts and 
    two-dimensional tuning of the Elastic Net.
Estimate penalized synthetic control models
    and perform hold-out validation to determine their 
    penalty parameter. This method is based on the work by
    Abadie & L'Hour (2021) <doi:10.1080/01621459.2021.1971535>.
    Penalized synthetic controls smoothly interpolate between 
    one-to-one matching and the synthetic control method.
Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R".
Data sets for statistical inference modeling related to People Analytics.  
  Contains various data sets from the book 'Handbook of Regression Modeling in People Analytics' 
  by Keith McNulty (2020).
Allows to perform the tests of equal predictive accuracy for panels of forecasts. Main references: Qu et al. (2024) <doi:10.1016/j.ijforecast.2023.08.001> and Akgun et al. (2024) <doi:10.1016/j.ijforecast.2023.02.001>.
Performs Bayesian variable selection under normal linear
          models for the data with the model parameters following as prior distributions either 
          the power-expected-posterior (PEP) or the intrinsic (a special case of the former)
          (Fouskakis and Ntzoufras (2022) <doi: 10.1214/21-BA1288>,
          Fouskakis and Ntzoufras (2020) <doi: 10.3390/econometrics8020017>).          
          The prior distribution on model space is the uniform over all models
          or the uniform on model dimension (a special case of the beta-binomial prior). 
          The selection is performed by either implementing a full enumeration 
          and evaluation of all possible models or using the Markov Chain 
          Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) <doi: 10.2307/1403615>). 
          Complementary functions for hypothesis testing, estimation and 
          predictions under Bayesian model averaging, as well as, plotting and 
          printing the results are also provided. The results can be compared to the
          ones obtained under other well-known priors on model parameters and model spaces.
Is designed to make easier printing summary statistics (for continues and factor level) tables in Latex, and plotting by factor.
Designed for prediction error estimation
        through resampling techniques, possibly accelerated by parallel
        execution on a compute cluster. Newly developed model fitting
        routines can be easily incorporated. Methods used in the package are detailed in
        Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062>
        and were used, for instance, in
        Porzelius Ch., Schumacher M.and  Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.
A versatile R visualization package that empowers 
    researchers with comprehensive visualization tools for seamlessly mapping peptides 
    to protein sequences, identifying distinct domains and regions of interest, 
    accentuating mutations, and highlighting post-translational modifications, 
    all while enabling comparisons across diverse experimental conditions. 
    Potential applications of 'PepMapViz' include the visualization of cross-software 
    mass spectrometry results at the peptide level for specific protein and domain 
    details in a linearized format and post-translational modification coverage 
    across different experimental conditions; unraveling insights into disease 
    mechanisms. It also enables visualization of major histocompatibility complex-presented peptides in 
    different antibody regions predicting immunogenicity in antibody drug development.
Fits the Piecewise Exponential distribution with random time grids using the clustering structure of the Product Partition Models. Details of the implemented model can be found in Demarqui et al. (2008) <doi:10.1007/s10985-008-9086-0>.
A PEP, or Portable Encapsulated Project, is a dataset that 
    subscribes to the PEP structure for organizing metadata. It is written using
    a simple YAML + CSV format, it is your one-stop solution to metadata 
    management across data analysis environments. This package reads this 
    standardized project configuration structure into R. 
    Described in Sheffield et al. (2021) <doi:10.1093/gigascience/giab077>.
An implementation of the data processing and data analysis portion
    of a pipeline named the PepSAVI-MS which is currently under development by
    the Hicks laboratory at the University of North Carolina.  The statistical
    analysis package presented herein provides a collection of software tools
    used to facilitate the prioritization of putative bioactive peptides from a
    complex biological matrix.  Tools are provided to deconvolute mass
    spectrometry features into a single representation for each peptide charge
    state, filter compounds to include only those possibly contributing to the
    observed bioactivity, and prioritize these remaining compounds for those
    most likely contributing to each bioactivity data set.
Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot 'XVG' output files from the 'GROMACS' molecular dynamics package.
This toolkit is designed for manipulation and analysis of peptides. It provides functionalities to assist researchers in peptide engineering and proteomics. Users can manipulate peptides by adding amino acids at every position, count occurrences of each amino acid at each position, and transform amino acid counts based on probabilities. The package offers functionalities to select the best versus the worst peptides and analyze these peptides, which includes counting specific residues, reducing peptide sequences, extracting features through One Hot Encoding (OHE), and utilizing Quantitative Structure-Activity Relationship (QSAR) properties (based in the package 'Peptides' by Osorio et al. (2015) <doi:10.32614/RJ-2015-001>). This package is intended for both researchers and bioinformatics enthusiasts working on peptide-based projects, especially for their use with machine learning.
Perform permutation-based hypothesis testing for randomized
    experiments as suggested in Ludbrook & Dudley (1998) <doi:10.2307/2685470>
    and Ernst (2004) <doi:10.1214/088342304000000396>, introduced in Pham et al.
    (2022) <doi:10.1016/j.chemosphere.2022.136736>.
To find the certainty of dominance interactions with indirect
    interactions being considered.
An implementation of two functions that estimate values for percentiles from an ordered categorical variable as described by Reardon (2011, isbn:978-0-87154-372-1). One function estimates percentile differences from two percentiles while the other returns the values for every percentile from 1 to 100.
Several person-fit statistics 
  (PFSs; Meijer and Sijtsma, 2001, <doi:10.1177/01466210122031957>) 
  are offered. These statistics allow assessing whether
  individual response patterns to tests or questionnaires are (im)plausible given 
  the other respondents in the sample or given a specified item response theory model. Some PFSs apply to 
  dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs 
  (personal biserial correlation, caution index, (normed) number of Guttman errors, 
  agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include
  extensions of lz, U3, and (normed) number of Guttman errors.
Implementation of different performance measures for classification and ranking tasks  including  Area Under the Receiving Characteristic Curve (AUROC) and Area Under the Precision Recall Curve (AUPRC), precision at a given recall, F-score for single and multiple classes.
Utilities for computing measures to assess model quality,
    which are not directly provided by R's 'base' or 'stats' packages.
    These include e.g. measures like r-squared, intraclass correlation
    coefficient (Nakagawa, Johnson & Schielzeth (2017)
    <doi:10.1098/rsif.2017.0213>), root mean squared error or functions to
    check models for overdispersion, singularity or zero-inflation and
    more. Functions apply to a large variety of regression models,
    including generalized linear models, mixed effects models and Bayesian
    models. References: Lüdecke et al. (2021) <doi:10.21105/joss.03139>.
Collection of econometric functions for performance and risk 
    analysis. In addition to standard risk and performance metrics, this 
    package aims to aid practitioners and researchers in utilizing the latest
    research in analysis of non-normal return streams.  In general, it is most 
    tested on return (rather than price) data on a regular scale, but most 
    functions will work with irregular return data as well, and increasing
    numbers of functions will work with P&L or price data where possible.
An infra-structure for estimating the predictive performance of
    predictive models. In this context, it can also be used to compare and/or select
    among different alternative ways of solving one or more predictive tasks. The
    main goal of the package is to provide a generic infra-structure to estimate
    the values of different metrics of predictive performance using different
    estimation procedures. These estimation tasks can be applied to any solutions
    (workflows) to the predictive tasks. The package provides easy to use standard
    workflows that allow the usage of any available R modeling algorithm together
    with some pre-defined data pre-processing steps and also prediction post-
    processing methods. It also provides means for addressing issues related with
    the statistical significance of the observed differences.
Functions for generating variants of curves:
    restricted cubic spline, periodic restricted cubic spline,
    periodic cubic spline. Periodic splines can be used to model data
    that has periodic nature / seasonality.
Provides a dataset containing properties for chemical elements.
    Helper functions are also provided to access some atomic properties.
An enterprise-targeted scalable and UI-standardized 'shiny' framework 
    including a variety of developer convenience functions with the goal of both 
    streamlining robust application development while assisting with creating a 
    consistent user experience regardless of application or developer.
A framework for building enterprise, scalable and UI-standardized 'shiny' applications.  
    It brings enhanced features such as 'bootstrap' v4 <https://getbootstrap.com/docs/4.0/getting-started/introduction/>,
    additional and enhanced 'shiny' modules, customizable UI features, as well as an enhanced application file
    organization paradigm. This update allows developers to harness the ability to build powerful applications and 
    enriches the 'shiny' developers' experience when building and  maintaining applications.
A Shiny Web Application to predict and visualize concentrations of pharmaceuticals in the aqueous environment. 
    Jagadeesan K., Barden R. and Kasprzyk-Hordern B. (2022) <https://www.ssrn.com/abstract=4306129>.
Perform Exact or Asymptotic permutation tests [see Fay and Shaw <doi:10.18637/jss.v036.i02>].
This version of the permutational algorithm generates a
        dataset in which event and censoring times are conditional on
        an user-specified list of covariates, some or all of which are
        time-dependent.
Includes functions to work with the Mallows and Generalized Mallows
    Models. The considered distances are Kendall's-tau, Cayley, Hamming and Ulam
    and it includes functions for making inference, sampling and learning such
    distributions, some of which are novel in the literature. As a by-product,
    PerMallows also includes operations for permutations, paying special attention
    to those related with the Kendall's-tau, Cayley, Ulam and Hamming distances. It
    is also possible to generate random permutations at a given distance, or with
    a given number of inversions, or cycles, or fixed points or even with a given
    length on LIS (longest increasing subsequence).
Calculates multivariate analysis of variance based on permutations and some associated pictorial representations. The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon.
Performance metric provides different performance measures like mean squared error, root mean square error, mean absolute deviation, mean absolute percentage error etc. of a fitted model. These can provide a way for forecasters to quantitatively compare the performance of competing models. For method details see (i) Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.
Implements an extension of the Chacko chi-square test for
    ordered vectors (Chacko, 1966, <https://www.jstor.org/stable/25051572>).
    Our extension brings the Chacko test to the computer age by implementing
    a permutation test to offer a numeric estimate of the p-value, which is
    particularly useful when the analytic solution is not available.
Provides tools for statistical testing of correlation coefficients through robust 
    permutation method and large sample approximation method. Tailored to different types 
    of correlation coefficients including Pearson correlation coefficient, weighted 
    Pearson correlation coefficient, Spearman correlation coefficient, and Lin's 
    concordance correlation coefficient.The robust permutation test controls type I error
    under general scenarios when sample size is small and two variables are dependent but 
    uncorrelated. The large sample approximation test generally controls type I error when
    the sample size is large (>200).
Permutational group-sequential tests for time-to-event data based on the log-rank test statistic. Supports exact permutation test when the censoring distributions are equal in the treatment and the control group and approximate imputation-permutation methods when the censoring distributions are different.
An add-on to the 'party' package, with a faster implementation 
   of the partial-conditional permutation importance for random forests. The 
   standard permutation importance is implemented exactly the same as in 
   the 'party' package. The conditional permutation importance can be 
   computed faster, with an option to be backward compatible to the 'party' 
   implementation. The package is compatible with random forests fit using the 
   'party' and the 'randomForest' package. The methods are described in
   Strobl et al. (2007) <doi:10.1186/1471-2105-8-25> and 
   Debeer and Strobl (2020) <doi:10.1186/s12859-020-03622-2>.
Can be used to carry out permutation based gene expression pathway analysis. This work was supported by a National Institute of Allergy and Infectious Disease/National Institutes of Health contract (No. HHSN272200900059C).
Implements the algorithm of Christensen (2024) <doi:10.1214/22-BA1353> for estimating marginal likelihoods via permutation counting.
The permubiome R package was created to perform a permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in a pairwise manner, after a random shuffling of data into the different groups of study with a prior selection of the microbiome features with the largest variation among groups. Previous to the permutation test itself, data can be normalized according to different methods proposed to handle microbiome data ('proportions' or 'Anders'). The median-based differences between groups resulting from the multiple simulations are fitted to a normal distribution with the aim to calculate their significance. A multiple testing correction based on Benjamini-Hochberg method (fdr) is finally applied to extract the differentially presented features between groups of your dataset. LATEST UPDATES: v1.1 and olders incorporates function to parse COLUMN format; v1.2 and olders incorporates -optimize- function to maximize evaluation of features with largest inter-class variation; v1.3 and olders includes the -size.effect- function to perform estimation statistics using the bootstrap-coupled approach implemented in the 'dabestr' (>=0.3.0) R package. Current v1.3.2 fixed bug with "Class" recognition and updated 'dabestr' functions.
Functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014) <doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <doi:10.1016/j.neuroimage.2014.01.060>). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) <doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).
Conduct permutation One-Way or Two-Way Analysis of Variance in R. Use different permutation types for two-way designs.
Manipulates invertible functions from a finite set to
             itself.  Can transform from word form to cycle form and
             back.  To cite the package in publications please use
             Hankin (2020) "Introducing the permutations R package",
             SoftwareX, volume 11 <doi:10.1016/j.softx.2020.100453>.
A set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak.
Helps you determine the analysis window to use when analyzing densely-sampled
    time-series data, such as EEG data, using permutation testing (Maris & Oostenveld, 2007)
    <doi:10.1016/j.jneumeth.2007.03.024>. These permutation tests can help identify the timepoints
    where significance of an effect begins and ends, and the results can be plotted in various
    types of heatmap for reporting. Mixed-effects models are supported using an implementation of
    the approach by Lee & Braun (2012) <doi:10.1111/j.1541-0420.2011.01675.x>.
Implements permutation tests for any test statistic and randomization scheme and constructs associated confidence intervals as described in Glazer and Stark (2024) <doi:10.48550/arXiv.2405.05238>.
A coding assistant using Perplexity's Large Language Models
    <https://www.perplexity.ai/> API. A set of functions and 'RStudio' add-ins
    that aim to help R developers.
Provides tools for fitting periodic coefficients regression models to data where periodicity plays a crucial role. It allows users to model and analyze relationships between variables that exhibit cyclical or seasonal patterns, offering functions for estimating parameters and testing the periodicity of coefficients in linear regression models. For simple periodic coefficient regression model see Regui et al. (2024) <doi:10.1080/03610918.2024.2314662>.
Tools that allow developers to write functions for prediction
    error estimation with minimal programming effort and assist users with
    model selection in regression problems.
Examples for integrating package 'perry' for prediction error estimation into regression models.
Tailoring the optimal biomarker(s) for disease screening or diagnosis based on subjects' individual characteristics.
Enables the interoperability between the Perseus platform for omics data analysis (Tyanova et al. 2016) <doi:10.1038/nmeth.3901> and R. It provides the foundation for developing and running Perseus plugins implemented in R by providing all required input and output handling, including data and parameter parsing as described in Rudolph and Cox 2018 <doi:10.1101/447268>.
Allows users to stem Persian texts for text analysis.
Automated identification of printed array positions from high content 
    microscopy images and the export of those positions as individual images 
    written to output as multi-layered tiff files.
Provides functions for fitting and validation of models for subgroup
    identification and personalized medicine / precision medicine under the general subgroup
    identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>.
    This package is intended for use for both randomized controlled trials and
    observational studies and is described in detail in Huling and Yu (2021) 
    <doi:10.18637/jss.v098.i05>.
Implements the methodology of Huling, Smith, and 
    Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification 
    for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part 
    modeling framework to handle semi-continuous data by separately modeling the positive part 
    of the outcome and an indicator of whether each outcome is positive, but still results in a 
    single treatment rule. High dimensional data is handled with a cooperative lasso penalty, 
    which encourages the coefficients in the two models to have the same sign.
Functions to setup a personal R package that attaches given
    libraries and exports personal helper functions.
An R-package-version of an open online science-based personality
    test from <https://openpsychometrics.org/tests/IPIP-BFFM/>,
    providing a better-designed interface and a more detailed report.
    The core command launch_test() opens a personality test in your browser,
    and generates a report after you click "Submit". In this report, your results
    are compared with other people's, to show what these results mean.
    Other people's data is from <https://openpsychometrics.org/_rawdata/BIG5.zip>.
Provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. In addition to its primary test, it also provides functions for simulation-based power analysis, reading in common data set formats, and visualizing results. Temporarily contains an edited version of function hr.mcp() from package 'wild1', written by Glen Sargeant. For tutorial see: http://evolve.zoo.ox.ac.uk/Evolve/Perspectev.html.
Compute personal values scores from various questionnaires based on the theoretical constructs proposed by professor Shalom H. Schwartz. Designed for researchers and practitioners in psychology, sociology, and related fields, the package facilitates the quantification and visualization of different dimensions related to personal values from survey data. It incorporates the recommended statistical adjustment to enhance the accuracy and interpretation of the results.   
    Note: The package 'persval' is independently developed based on the personal values theoretical framework, and is not directly endorsed by professor Schwartz.
The perturbR() function incrementally perturbs network edges (using the   rewireR function)and compares the resulting community detection solutions 
    from the rewired networks with the solution found for the original network. 
    These comparisons aid in understanding the stability of the 
    original solution. The package requires symmetric, weighted (specifically, count) matrices/networks.
Provide users with a convenient way to access and analyze information on endangered plant species in Peru based on `Decreto Supremo N 043-2006-AG - Aprueban categorizacion de especies amenazadas de flora silvestre`<https://sinia.minam.gob.pe/normas/aprueban-categorizacion-especies-amenazadas-flora-silvestre>.
A friendly API for sequence iteration and set comprehension.
Access the data of the 'Catalogue of the Timber Forest Species of the Peruvian Amazon' Vásquez Martínez, R., & Rojas Gonzáles, R.D.P.(2022)<doi:10.21704/rfp.v37i3.1956>.
Automatic estimation of number of principal components in PCA
    with PEnalized SEmi-integrated Likelihood (PESEL). See Piotr Sobczyk, Malgorzata Bogdan, Julie Josse
    "Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood"
    (2017) <doi:10.1080/10618600.2017.1340302>.
Computes the Danish Pesticide Load Indicator as described in Kudsk et al. (2018) <doi:10.1016/j.landusepol.2017.11.010> and Moehring et al. (2019) <doi:10.1016/j.scitotenv.2018.07.287> for pesticide use data. Additionally offers the possibility to directly link pesticide use data to pesticide properties given access to the Pesticide properties database (Lewis et al., 2016) <doi:10.1080/10807039.2015.1133242>.
Set of tools to automatize extraction of data on pests from 'EPPO
    Data Services' and 'EPPO Global Database' and to put them into tables with
    human readable format. Those function use 'EPPO database API', thus you 
    first need to register on <https://data.eppo.int> (free of charge).
    Additional helpers allow to download, check and connect to
    'SQLite EPPO database'.
A comprehensive implementation of Petersen-type estimators
    and its many variants for two-sample capture-recapture studies.
    A conditional likelihood approach is used that allows
    for tag loss; non reporting of tags; reward tags; categorical, geographical and temporal stratification;
    partial stratification; reverse capture-recapture;
    and continuous variables in modeling the probability of capture.
    Many examples from fisheries management are presented.
A collection of R functions that are widely used by the Petersen
    Lab. Included are functions for various purposes, including evaluating the
    accuracy of judgments and predictions, performing scoring of assessments,
    generating correlation matrices, conversion of data between various types,
    data management, psychometric evaluation, extensions related to latent
    variable modeling, various plotting capabilities, and other miscellaneous
    useful functions. By making the package available, we hope to make our
    methods reproducible and replicable by others and to help others perform
    their data processing and analysis methods more easily and efficiently. The
    codebase is provided in Petersen (2024) <doi:10.5281/zenodo.7602890> and on
    CRAN: <doi: 10.32614/CRAN.package.petersenlab>. The package is described in
    "Principles of Psychological Assessment: With Applied Examples in R"
    (Petersen, 2024, 2025) <doi:10.1201/9781003357421>,
    <doi:10.25820/work.007199>, <doi:10.5281/zenodo.6466589>.
Two-sample power-enhanced mean tests, covariance tests, and simultaneous tests on mean vectors and covariance matrices for high-dimensional data. Methods of these PE tests are presented in 
    Yu, Li, and Xue (2022) <doi:10.1080/01621459.2022.2126781>; 
    Yu, Li, Xue, and Li (2022) <doi:10.1080/01621459.2022.2061354>.
Wrapper of the 'Petfinder API' <https://www.petfinder.com/developers/v2/docs/> that implements 
  methods for interacting with and extracting data from the 'Petfinder' database. The 'Petfinder 
  REST API' allows access to the 'Petfinder' database, one of the largest online databases of 
  adoptable animals and animal welfare organizations across North America.
Calculates the relative growth rate (RGR) of a series of individuals by building a life table and solving the Lotka-Birch equation. (See Birch, L. C. 1948. The intrinsic rate of natural increase of an insect population. - Journal of Animal Ecology 17: 15-26) <doi:10.2307/1605>.
Functions for the construction of Petri Nets. Petri Nets can be replayed by firing enabled transitions.
     Silent transitions will be hidden by the execution handler. Also includes functionalities for the visualization of Petri Nets and
     export of Petri Nets to PNML (Petri Net Markup Language) files.
Reproducible, programmatic retrieval of survey datasets from the
    Pew Research Center.
Load the Just Another Gibbs Sampling (JAGS) module 'pexm'. The module provides the tools to work with the Piecewise Exponential (PE) distribution in a Bayesian model with the corresponding Markov Chain Monte Carlo algorithm (Gibbs Sampling) implemented via JAGS. Details about the module implementation can be found in Mayrink et al. (2021) <doi:10.18637/jss.v100.i08>.
Eco-phylogenetic and community phylogenetic analyses.
    Keeps community ecological and phylogenetic data matched up and
    comparable using 'comparative.comm' objects. Wrappers for common
    community phylogenetic indices ('pez.shape', 'pez.evenness',
    'pez.dispersion', and 'pez.dissimilarity' metrics). Implementation
    of Cavender-Bares (2004) correlation of phylogenetic and
    ecological matrices ('fingerprint.regression'). Phylogenetic
    Generalised Linear Mixed Models (PGLMMs; 'pglmm') following Ives &
    Helmus (2011) and Rafferty & Ives (2013). Simulation of null
    assemblages, traits, and phylogenies ('scape', 'sim.meta.comm').
Performs smoothed (and non-smoothed) principal/independent components analysis
  of functional data. Various functional pre-whitening approaches are implemented as 
  discussed in Vidal and Aguilera (2022) “Novel whitening approaches in functional 
  settings", <doi:10.1002/sta4.516>. Further whitening representations of functional 
  data can be derived in terms of a few principal components, providing an avenue 
  to explore hidden structures in low dimensional settings: see Vidal,
  Rosso and Aguilera (2021) “Bi-smoothed functional independent component 
  analysis for EEG artifact removal”, <doi:10.3390/math9111243>.
Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to 
    Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>,
    Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>,
    Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>,
    Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>,
    Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and
    Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.
Implementation of commonly used penalized functional linear regression models, including the Smooth and Locally Sparse (SLoS) method by Lin et al. (2016) <doi:10.1080/10618600.2016.1195273>, Nested Group bridge Regression (NGR) method by Guan et al. (2020) <doi:10.1080/10618600.2020.1713797>, Functional Linear Regression That's interpretable (FLIRTI) by James et al. (2009) <doi:10.1214/08-AOS641>, and the Penalized B-spline regression method.
Builds and runs 'c++' code for classes that encapsulate state space model, particle filtering algorithm pairs.
    Algorithms include the Bootstrap Filter from Gordon et al. (1993) <doi:10.1049/ip-f-2.1993.0015>, the generic SISR filter, 
    the Auxiliary Particle Filter from Pitt et al (1999) <doi:10.2307/2670179>, and a variety of Rao-Blackwellized 
    particle filters inspired by Andrieu et al. (2002) <doi:10.1111/1467-9868.00363>. For more details on the 'c++' library 
    'pf', see Brown (2020) <doi:10.21105/joss.02599>.
Provides access to a high performant random distribution
    sampler for the Polya Gamma Distribution using either 'C++' headers for 
    'Rcpp' or 'RcppArmadillo' and 'R'.
Density, distribution function, quantile function, 
    and random generation function based on Kittipong Klinjan,Tipat Sottiwan and Sirinapa Aryuyuen (2024)<DOI:10.28919/cmbn/8833>.
This work is an extension of the state space model for Poisson count data, Poisson-Gamma model, towards a semiparametric specification. Just like the generalized additive models (GAM), cubic splines are used for covariate smoothing. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.
Generates random samples from the Polya-Gamma distribution using an implementation of the algorithm described in J. Windle's PhD thesis (2013) <https://repositories.lib.utexas.edu/bitstream/handle/2152/21842/WINDLE-DISSERTATION-2013.pdf>. The underlying implementation is in C.
Fits penalized generalized estimating equations to longitudinal data with high-dimensional covariates.
Perform simultaneous estimation and variable selection for correlated bivariate
    mixed outcomes (one continuous outcome and one binary outcome per cluster) using
    penalized generalized estimating equations. In addition, clustered Gaussian and binary
    outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported.
    Cross-validation can be performed to find the optimal regularization parameter(s).
A thin wrapper over PLINK 2's core libraries which provides an R
    interface for reading .pgen files.  A minimal .pvar loader is also
    included.  Chang et al. (2015) \doi{10.1186/s13742-015-0047-8}.
Set of tools for reading, writing and transforming spatial and seasonal data, model selection and specific statistical tests for ecologists. It includes functions to interpolate regular positions of points between landmarks, to discretize polylines into regular point positions, link distant observations to points and convert a bounding box in a spatial object. It also provides miscellaneous functions for field ecologists such as spatial statistics and inference on diversity indexes, writing data.frame with Chinese characters.
Nonparametric density estimation for (hyper)spherical data by means of a parametrically guided kernel estimator (adaptation of the method of Hjort and Glad (1995) <doi:10.1214/aos/1176324627> to the spherical setting). The package also allows the data-driven selection of the smoothing parameter and the representation of the estimated density for circular and spherical data. Estimators of the density without guide can also be obtained.
Estimation of panel models for glm-like models:
             this includes binomial models (logit and probit), count models (poisson and negbin)
	     and ordered models (logit and probit), as described in:
             Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7,
	     Hsiao (2014) Analysis of Panel Data  <doi:10.1017/CBO9781139839327> and
	     Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4.
Construction method of nested resolvable designs from 
    a projective geometry defined on Galois field of order 2. The obtained
    Resolvable designs are used to build uniform design. The presented results
    are based on <https://eudml.org/doc/219563> and A. Boudraa et al. (See references).
Carries out model-based clustering or classification using parsimonious Gaussian mixture models. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>, McNicholas et al. (2010) <doi:10.1016/j.csda.2009.02.011>.
Evaluation of the pdf and the cdf of the univariate,
        noncentral, p-generalized normal distribution. Sampling from
        the univariate, noncentral, p-generalized normal distribution
        using either the p-generalized polar method, the p-generalized
        rejecting polar method, the Monty Python method, the Ziggurat
        method or the method of Nardon and Pianca. The package also
        includes routines for the simulation of the bivariate,
        p-generalized uniform distribution and the simulation of the
        corresponding angular distribution.
Computes pseudo-realizations from the posterior distribution of a Gaussian Process (GP) with the method described in Azzimonti et al. (2016) <doi:10.1137/141000749>. The realizations are obtained from simulations of the field at few well chosen points that minimize the expected distance in measure between the true excursion set of the field and the approximate one. Also implements a R interface for (the main function of) Distance Transform of sampled Functions (<https://cs.brown.edu/people/pfelzens/dt/index.html>).
Implements a general framework for creating dependency graphs using projection as introduced in Fan, Feng and Xia (2019)<arXiv:1501.01617>. Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance options are available to generate the graph.
Provides functions to aid the identification of probable/possible
    duplicates in Plant Genetic Resources (PGR) collections using
    'passport databases' comprising of information records of each constituent
    sample. These include methods for cleaning the data, creation of a
    searchable Key Word in Context (KWIC) index of keywords associated with
    sample records and the identification of nearly identical records with
    similar information by fuzzy, phonetic and semantic matching of keywords.
Inference and visualize gene regulatory network based on single-cell RNA sequencing pseudo-time information.
Computes the generalized synthetic control estimator described in
    Powell (2017) <doi:10.7249/WR1142>.  Provides both point estimates, and hypothesis testing.
Create PostgreSQL statements/scripts from R, optionally executing the SQL statements.
    Common SQL operations are included, although not every configurable option is available at this time. 
    SQL output is intended to be compliant with PostgreSQL syntax specifications. PostgreSQL documentation is available here
    <https://www.postgresql.org/docs/current/index.html>.
The purpose of 'PH1XBAR' is to build a Phase I Shewhart control chart for the basic Shewhart, the variance components and the ARMA models in R for subgrouped and individual data. More details can be found: Yao and Chakraborti (2020) <doi: 10.1002/qre.2793>, Yao and Chakraborti (2021) <doi: 10.1080/08982112.2021.1878220>, and Yao et al. (2023) <doi: 10.1080/00224065.2022.2139783>.
An implementation of Bayesian single-arm phase II design methods for binary outcome based on posterior
  probability (Thall and Simon (1994) <doi:10.2307/2533377>) and predictive probability (Lee and Liu (2008) <doi:10.1177/1740774508089279>).
Calculate the Bayesian posterior/predictive probability and
    determine the sample size and stopping boundaries for single-arm Phase II design.
Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level).
Fits right-truncated meta-analysis (RTMA), a bias correction for
  the joint effects of p-hacking (i.e., manipulation of results within studies
  to obtain significant, positive estimates) and traditional publication bias
  (i.e., the selective publication of studies with significant, positive
  results) in meta-analyses [see Mathur MB (2022). "Sensitivity analysis for
  p-hacking in meta-analyses." <doi:10.31219/osf.io/ezjsx>.]. Unlike publication
  bias alone, p-hacking that favors significant, positive results (termed
  "affirmative") can distort the distribution of affirmative results. To
  bias-correct results from affirmative studies would require strong assumptions
  on the exact nature of p-hacking. In contrast, joint p-hacking and publication
  bias do not distort the distribution of published nonaffirmative results when
  there is stringent p-hacking (e.g., investigators who hack always eventually
  obtain an affirmative result) or when there is stringent publication bias
  (e.g., nonaffirmative results from hacked studies are never published). This
  means that any published nonaffirmative results are from unhacked studies.
  Under these assumptions, RTMA involves analyzing only the published
  nonaffirmative results to essentially impute the full underlying distribution
  of all results prior to selection due to p-hacking and/or publication bias.
  The package also provides diagnostic plots described in Mathur (2022).
Allows for estimation of phylogenetic trees and networks
    using Maximum Likelihood, Maximum Parsimony, distance methods and
    Hadamard conjugation (Schliep 2011). Offers methods for tree comparison, 
    model selection and visualization of phylogenetic networks as described in
    Schliep et al. (2017).
Create phantom variables, which are variables that were not observed, for the purpose of sensitivity analyses for structural equation models. The package makes it easier for a user to test different combinations of covariances between the phantom variable(s) and observed variables. The package may be used to assess a model's or effect's sensitivity to temporal bias (e.g., if cross-sectional data were collected) or confounding bias.
Enhanced RTF wrapper written in R for use with existing R tables
    packages such as 'Huxtable' or 'GT'. This package fills a gap where tables in
    certain packages can be written out to RTF, but cannot add certain metadata
    or features to the document that are required/expected in a report for a
    regulatory submission, such as multiple levels of titles and footnotes,
    making the document landscape, and controlling properties such as margins.
The 'pharmaverse' is a set of packages that compose multiple pathways 
    through clinical data generation and reporting in the pharmaceutical industry.
    This package is designed to guide users to our work-spaces on 'GitHub', 'Slack' 
    and 'LinkedIn' as well as our website and examples.
    Learn more about the 'pharmaverse' at <https://pharmaverse.org>.
A set of Analysis Data Model (ADaM) datasets constructed using the 
    Study Data Tabulation Model (SDTM) datasets contained in the 'pharmaversesdtm' package and 
    the template scripts from the 'admiral' family of packages. ADaM dataset specifications 
    are described in the CDISC ADaM implementation guide, accessible by creating a free account on <https://www.cdisc.org/>.
A set of Study Data Tabulation Model (SDTM) datasets from the Clinical
    Data Interchange Standards Consortium (CDISC) pilot project used for testing
    and developing Analysis Data Model (ADaM) datasets inside the pharmaverse 
    family of packages. SDTM dataset specifications are described in the CDISC SDTM implementation guide,
    accessible by creating a free account on <https://www.cdisc.org/>.
Interface to the 'Pharmpy' 'pharmacometrics' library. The 'Reticulate' package is used to interface Python from R.
Compiles functions to trim, bin, visualise, and analyse activity/sleep time-series data collected from the Drosophila Activity Monitor (DAM) system (Trikinetics, USA). The following methods were used to compute periodograms - Chi-square periodogram: Sokolove and Bushell (1978) <doi:10.1016/0022-5193(78)90022-X>, Lomb-Scargle periodogram: Lomb (1976) <doi:10.1007/BF00648343>, Scargle (1982) <doi:10.1086/160554> and Ruf (1999) <doi:10.1076/brhm.30.2.178.1422>, and Autocorrelation: Eijzenbach et al. (1986) <doi:10.1111/j.1440-1681.1986.tb00943.x>. Identification of activity peaks is done after using a Savitzky-Golay filter (Savitzky and Golay (1964) <doi:10.1021/ac60214a047>) to smooth raw activity data. Three methods to estimate anticipation of activity are used based on the following papers - Slope method: Fernandez et al. (2020) <doi:10.1016/j.cub.2020.04.025>, Harrisingh method: Harrisingh et al. (2007) <doi:10.1523/JNEUROSCI.3680-07.2007>, and Stoleru method: Stoleru et al. (2004) <doi:10.1038/nature02926>. Rose plots and circular analysis are based on methods from - Batschelet (1981) <ISBN:0120810506> and Zar (2010) <ISBN:0321656865>.
Contains three simulation functions for implementing the entire Phase 123 trial and the separate Eff-Tox and Phase 3 portions of the trial, which may be beneficial for use on clusters. The functions AssignEffTox() and RandomizeEffTox() assign doses to patient cohorts during phase 12 and Reoptimize() determines the optimal dose to continue with during Phase 3. The functions ReturnMeansAgent() and ReturnMeanControl() gives the true mean survival for the agent doses and control and ReturnOCS() gives the operating characteristics of the design.
Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the 'Efftox' (efficacy and toxicity tradeoff, <https://biostatistics.mdanderson.org/SoftwareDownload/SingleSoftware/Index/2>) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding.
Implements Bayesian phase I repeated measurement design that 
    accounts for multidimensional toxicity endpoints and longitudinal efficacy 
    measure from multiple treatment cycles. The package provides flags to 
    fit a variety of model-based phase I design, including 1 stage models with
    or without individualized dose modification, 3-stage models with or without
    individualized dose modification, etc. Functions are provided to recommend 
    dosage selection based on the data collected in the available patient cohorts 
    and to simulate trial characteristics given design parameters. 
    Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.
Implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.
Performs a qualitative analysis of one- and two-dimensional
   autonomous ordinary differential equation systems, using phase plane methods.
   Programs are available to identify and classify equilibrium points, plot the
   direction field, and plot trajectories for multiple initial conditions. In
   the one-dimensional case, a program is also available to plot the phase
   portrait. Whilst in the two-dimensional case, programs are additionally
   available to plot nullclines and stable/unstable manifolds of saddle points.
   Many example systems are provided for the user. For further details can be
   found in Grayling (2014) <doi:10.32614/RJ-2014-023>.
Functions to perform Bayesian inference on absorption time data for
             Phase-type distributions. The methods of Bladt et al (2003)
             <doi:10.1080/03461230110106435> and Aslett (2012)
             <https://www.louisaslett.com/PhD_Thesis.pdf> are provided.
General implementation of core function from phase-type
    theory. 'PhaseTypeR' can be used to model continuous and discrete
    phase-type distributions, both univariate and multivariate. The
    package includes functions for outputting the mean and (co)variance of
    phase-type distributions; their density, probability and quantile
    functions; functions for random draws; functions for
    reward-transformation; and functions for plotting the distributions as
    networks. For more information on these functions please refer to
    Bladt and Nielsen (2017, ISBN: 978-1-4939-8377-3) and Campillo Navarro
    (2019)
    <https://orbit.dtu.dk/en/publications/order-statistics-and-multivariate-discrete-phase-type-distributio>.
PHATE is a tool for visualizing high dimensional single-cell data
  with natural progressions or trajectories. PHATE uses a novel conceptual framework
  for learning and visualizing the manifold inherent to biological systems in which
  smooth transitions mark the progressions of cells from one state to another.
  To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic
  stem cells, human embryonic stem cells, and bone marrow samples, check out our publication in Nature Biotechnology
  at <doi:10.1038/s41587-019-0336-3>.
Clustering analysis for sparse microbiome data, based on a Poisson hurdle model.
Provides permutation methods for testing in high-dimensional linear models.
    The tests are often robust against heteroscedasticity and non-normality 
    and usually perform well under anti-sparsity.
    See Hemerik, Thoresen and Finos (2021) <doi:10.1080/00949655.2020.1836183>.
A toolkit of functions to help: i) effortlessly transform collected data into a 
  publication ready format, ii) generate insightful visualizations from clinical data, iii) report
  summary statistics in a publication-ready format, iv) efficiently export, save and reload R objects
  within the framework of R projects.
Implementation of heatmaps that offers more control
    over dimensions and appearance.
A system for binary and multi-class classification of
    high-dimensional phenotypic data using ensemble learning. By combining
    predictions from different classification models, this package attempts
    to improve performance over individual learners. The pre-processing,
    training, validation, and testing are performed end-to-end to minimize
    user input and simplify the process of classification.
Implement surrogate-assisted feature extraction (SAFE) and
             common machine learning approaches to train and validate phenotyping models.
             Background and details about the methods can be found at 
             Zhang et al. (2019) <doi:10.1038/s41596-019-0227-6>,
             Yu et al. (2017) <doi:10.1093/jamia/ocw135>, and 
             Liao et al. (2015) <doi:10.1136/bmj.h1885>.
To build a shiny app for visualization of the hierarchy of PheCode Mapping with International Classification of Diseases (ICD). The same PheCode hierarchy is displayed in two ways: as a sunburst plot and as a tree.
Functions to calculate commonly used public health statistics and 
    their confidence intervals using methods approved for use in the production  
    of Public Health England indicators such as those presented via Fingertips 
    (<https://fingertips.phe.org.uk/>). It provides functions for the generation 
    of proportions, crude rates, means, directly standardised rates, indirectly 
    standardised rates, standardised mortality ratios, slope and relative index
    of inequality and life expectancy. 
    Statistical methods are referenced in the following publications. 
    Breslow NE, Day NE (1987) <doi:10.1002/sim.4780080614>.
    Dobson et al (1991) <doi:10.1002/sim.4780100317>. 
    Armitage P, Berry G (2002) <doi:10.1002/9780470773666>.
    Wilson EB. (1927) <doi:10.1080/01621459.1927.10502953>.
    Altman DG et al (2000, ISBN: 978-0-727-91375-3).
    Chiang CL. (1968, ISBN: 978-0-882-75200-6).
    Newell C. (1994, ISBN: 978-0-898-62451-9).
    Eayres DP, Williams ES (2004) <doi:10.1136/jech.2003.009654>.
    Silcocks PBS et al (2001) <doi:10.1136/jech.55.1.38>.
    Low and Low (2004) <doi:10.1093/pubmed/fdh175>.
    Fingertips Public Health Technical Guide: <https://fingertips.phe.org.uk/static-reports/public-health-technical-guidance/>.
Generates Weibull-parameterized estimates of phenology for any percentile of 
    a distribution using the framework established in Cooke (1979)
    <doi:10.1093/biomet/66.2.367>. Extensive testing against other 
    estimators suggest the weib_percentile() function is especially useful in 
    generating more accurate and less biased estimates of onset and offset 
    (Belitz et al. 2020 <doi.org:10.1111/2041-210X.13448>. Non-parametric 
    bootstrapping can be used to generate confidence intervals around those 
    estimates, although this is computationally expensive. Additionally, this 
    package offers an easy way to perform non-parametric bootstrapping to 
    generate confidence intervals for quantile estimates, mean estimates, 
    or any statistical function of interest.
Provides some easy-to-use functions for 
	spatial analyses of (plant-) phenological data 
	sets and satellite observations of vegetation.
Provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984), bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices.
Provides functions for phenological data preprocessing, modelling and result handling. For more information, please refer to Lange et al. (2016) <doi:10.1007/s00484-016-1161-8>.
Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) <http://mural.maynoothuniversity.ie/view/ethesisauthor/Pollard=3ACiaran_P=2E=3A=3A.html>. A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour.
Provides some easy-to-use functions for time series
        analyses of (plant-) phenological data sets. These functions
        mainly deal with the estimation of combined phenological time
        series and are usually wrappers for functions that are already
        implemented in other R packages adapted to the special
        structure of phenological data and the needs of phenologists.
        Some date conversion functions to handle Julian dates are also
        provided.
Programmatic interface to the 'PhenoCam' web services (<https://phenocam.nau.edu/webcam>).
    Allows for easy downloading of 'PhenoCam' data directly to your R workspace
    or your computer and provides post-processing routines for consistent and easy
    timeseries outlier detection, smoothing and estimation of phenological transition dates.
    Methods for this package are described in detail in Hufkens et. al (2018) <doi:10.1111/2041-210X.12970>.
Using the Bayesian state-space approach, we developed a continuous development model to quantify dynamic incremental changes in the response variable. While the model was originally developed for daily changes in forest green-up, the model can be used to predict any similar process. The CDM can capture both timing and rate of nonlinear processes. Unlike statics methods, which aggregate variations into a single metric, our dynamic model tracks the changing impacts over time. The CDM accommodates nonlinear responses to variation in predictors, which changes throughout development.
The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and 
    growing season dividing method and a practical snow elimination method 
    based on Whittaker were proposed. 7 curve fitting methods and 4 phenology 
    extraction methods were provided. Parameters boundary are considered for 
    every curve fitting methods according to their ecological meaning. 
    And 'optimx' is used to select best optimization method for different 
    curve fitting methods.
    Reference:
    Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction 
    package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>;
    Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains 
    the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness 
    Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. 
    <doi:10.1029/2020JG005636>;
    Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing 
    global MODIS EVI time series on the Google Earth Engine. 
    ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13–24;
    Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology 
    on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). 
    Agric. For. Meteorol. 248, 408–417. <doi:10.1016/j.agrformet.2017.10.026>.
Fit a time-series model 
    to a crop phenology data, such as time-series rice canopy height.
    This package returns the model parameters as the summary statistics of crop phenology,
    and these parameters will be useful to characterize the growth pattern of each cultivar and 
    predict manually-measured traits, such as days to heading and biomass.
    Please see Taniguchi et al. (2022) <doi:10.3389/fpls.2022.998803> for detail.
    This package has been designed for scientific use.  
    Use for commercial purposes shall not be allowed.
Functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) <doi:10.3354/esr00292> for the phenology function, Girondot, M. (2017) <doi:10.1016/j.ecolind.2017.05.063> for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) <doi:10.2993/etbi-35-02-337-353.1> for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) <doi:10.1007/s00227-019-3545-x> for tag-loss estimate, Hancock J, ..., Girondot M (2019) <doi:10.1016/j.ecolmodel.2019.04.013> for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) <doi:10.1007/s00227-020-03686-x> for aggregating several seasons.
This takes in a series of multi-layer raster files and returns a phenology projection raster, following methodologies described in John (2016) <https://etda.libraries.psu.edu/catalog/13521clj5135>.
A collection of functions to process digital images, depict greenness index trajectories and extract relevant phenological stages.
The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) <doi:10.1093/jamia/ocx111>.
Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) <doi:10.1007/s10681-007-9449-8>.
Phenotype study cohorts in data mapped to the 
    Observational Medical Outcomes Partnership Common Data Model. Diagnostics 
    are run at the database, code list, cohort, and population level to assess 
    whether study cohorts are ready for research.
Simulation is a critical part of method development and assessment
    in quantitative genetics. 'PhenotypeSimulator' allows for the flexible 
    simulation of phenotypes under different models, including genetic variant 
    and  infinitesimal genetic effects (reflecting population structure) as well 
    as non-genetic covariate effects, observational noise and additional 
    correlation effects. The different phenotype components are combined into a 
    final phenotype while controlling for the proportion of variance explained 
    by each of the components. For each effect component, the number of 
    variables, their distribution and the design of their effect across traits 
    can be customised. For the simulation of the genetic effects, external 
    genotype data from a number of standard software ('plink', 'hapgen2'/
    'impute2', 'genome', 'bimbam', simple text files) can be imported. The final 
    simulated phenotypes and its components can be automatically saved into .rds 
    or .csv files. In addition, they can be saved in formats compatible with 
    commonly used genetic association software ('gemma', 'bimbam', 'plink', 
    'snptest', 'LiMMBo').
Methods to calculate and present 'PHENTHAUproc', an early warning and decision support system for hazard assessment and control of oak processionary moth (OPM) using local and spatial temperature data. It was created by Halbig et al. 2024 (<doi:10.1016/j.foreco.2023.121525>) at FVA (<https://www.fva-bw.de/en/homepage/>) Forest Research Institute Baden-Wuerttemberg, Germany and at BOKU - University of Natural Ressources and Life Sciences, Vienna, Austria.
Use phenotype risk scores based on linked clinical and genetic data
  to study Mendelian disease and rare genetic variants. See Bastarache et al.
  2018 <doi:10.1126/science.aal4043>.
Provides tools for the test for the comparison of survival curves, the evaluation of the goodness-of-fit and the predictive capacity of the proportional hazards model.
Using Electronic Health Record (EHR) is difficult because most of the time the true characteristic of the patient is not available. Instead we can retrieve the International Classification of Disease code related to the disease of interest or we can count the occurrence of the Unified Medical Language System. None of them is the true phenotype which needs chart review to identify. However chart review is time consuming and costly. 'PheVis' is an algorithm which is phenotyping (i.e identify a characteristic) at the visit level in an unsupervised fashion. It can be used for chronic or acute diseases. An example of how to use 'PheVis' is available in the vignette. Basically there are two functions that are to be used: `train_phevis()` which trains the algorithm and `test_phevis()` which get the predicted probabilities. The detailed method is described in preprint by Ferté et al. (2020) <doi:10.1101/2020.06.15.20131458>.
Computes the Owen's T function or the bivariate
             normal integral using one of the following:
             modified Euler's arctangent series, tetrachoric
             series, or Vasicek's series.  For the methods,
             see Komelj, J. (2023) <doi:10.4236/ajcm.2023.134026>
             (or reprint <arXiv:2312.00011> with better typography)
             and Vasicek, O. A. (1998) <doi:10.21314/JCF.1998.015>.
Analysis of terms in linear, generalized and mixed linear models, 
	on the basis of multiple comparisons of factor contrasts.  Specially suited 
	for the analysis of interaction terms.
Analysis of features by phi delta diagrams. In particular, functions for reading data and calculating phi and delta as well as the functionality to plot it. Moreover it is possible to do further analysis on the data by generating rankings. For more information on phi delta diagrams, see also Giuliano Armano (2015) <doi:10.1016/j.ins.2015.07.028>.
Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.
Control Philips Hue smart lighting. Use this package to
    connect to a Hue bridge on your local network (remote authentication
    not yet supported) and control your smart lights through the Philips
    Hue API. All API V1 endpoints are supported. See API documentation at
    <https://developers.meethue.com/>.
Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist.  These difficulties are related to those arising from logistic and multinomial regression.  References for methods are given in the separate function documents.  Supported by grant NSF DMS 1712839.
A bioinformatics method developed for analyzing the heterogeneity of single-cell populations. Phitest provides an objective and automatic method to evaluate the performance of clustering and quality of cell clusters.
Functions to extract and handle commonly occurring principal phrases
    obtained from collections of texts.
Perform a differential analysis at pathway level based on metabolite quantifications and information on pathway metabolite composition.
  The method is based on a Principal Component Analysis step and on a linear mixed model.
  Automatic query of metabolic pathways is also implemented.
Implementation of the Phoenix and Phoenix-8 Sepsis Criteria as
    described in "Development and Validation of the Phoenix Criteria for
    Pediatric Sepsis and Septic Shock" by Sanchez-Pinto, Bennett, DeWitt,
    Russell et al. (2024) <doi:10.1001/jama.2024.0196> (Drs. Sanchez-Pinto
    and Bennett contributed equally to this manuscript; Dr. DeWitt and Mr.
    Russell contributed equally to the manuscript), "International Consensus
    Criteria for Pediatric Sepsis and Septic Shock" by Schlapbach, Watson,
    Sorce, Argent, et al. (2024) <doi:10.1001/jama.2024.0179> (Drs Schlapbach,
    Watson, Sorce, and Argent contributed equally) and the application note
    "phoenix: an R package and Python module for calculating the Phoenix
    pediatric sepsis score and criteria" by DeWitt, Russell, Rebull,
    Sanchez-Pinto, and Bennett (2024) <doi:10.1093/jamiaopen/ooae066>.
Convert English letters to numbers or numbers to English
    letters as on a telephone keypad. When converting letters to numbers,
    a character vector is returned with "A," "B," or "C" becoming 2, "D,"
    "E", or "F" becoming 3, etc. When converting numbers to letters, a
    character vector is returned with multiple elements (i.e., "2" becomes
    a vector of "A," "B," and "C").
There are a lot of different typical tasks that have to be solved during phonetic research and experiments. This includes creating a presentation that will contain all stimuli, renaming and concatenating multiple sound files recorded during a session, automatic annotation in 'Praat' TextGrids (this is one of the sound annotation standards provided by 'Praat' software, see Boersma & Weenink 2020 <https://www.fon.hum.uva.nl/praat/>), creating an html table with annotations and spectrograms, and converting multiple formats ('Praat' TextGrid, 'ELAN', 'EXMARaLDA', 'Audacity', subtitles '.srt', and 'FLEx' flextext). All of these tasks can be solved by a mixture of different tools (any programming language has programs for automatic renaming, and Praat contains scripts for concatenating and renaming files, etc.). 'phonfieldwork' provides a functionality that will make it easier to solve those tasks independently of any additional tools. You can also compare the functionality with other packages: 'rPraat' <https://CRAN.R-project.org/package=rPraat>, 'textgRid' <https://CRAN.R-project.org/package=textgRid>.
Provides a collection of phonetic algorithms including
    Soundex, Metaphone, NYSIIS, Caverphone, and others.  The package is
    documented in <doi:10.18637/jss.v095.i08>.
Tools for phoneticians and phonologists, including functions for normalization and plotting of vowels.
Contains tools for the organization, display, and analysis of the sorts of data frequently encountered in phonetics research and experimentation, including the easy creation of IPA vowel plots, and the creation and manipulation of WAVE audio files.
Use 'Phosphor' icons in 'shiny' applications or 'rmarkdown' documents. Icons are available in
    5 different weights and can be customized by setting color, size, orientation and more.
Definitions of classes, methods, operators and functions for use 
    in photobiology and radiation meteorology and climatology. Calculation of
    effective (weighted) and not-weighted irradiances/doses, fluence rates,
    transmittance, reflectance, absorptance, absorbance and diverse ratios and 
    other derived quantities from spectral data. Local maxima and minima: peaks,
    valleys and spikes. Conversion between energy-and photon-based units. 
    Wavelength interpolation. Astronomical calculations related solar angles and 
    day length. Colours and vision. This package is part of the 'r4photobiology' 
    suite, Aphalo, P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral 'transmittance' data for frequently used filters and 
    similar materials. Plastic sheets and films; photography filters; 
    theatrical gels; machine-vision filters; various types of window glass;
    optical glass and some laboratory plastics and glassware. Spectral 
    reflectance data for frequently encountered materials. Part of the
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Functions for reading, and in some cases writing, foreign files 
    containing spectral data from spectrometers and their associated software, 
    output from daylight simulation models in common use, and some spectral 
    data repositories. As well as functions for exchange of spectral data with 
    other R packages. Part of the 'r4photobiology' suite, 
    Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral emission data for some frequently used lamps including 
    bulbs and flashlights based on led emitting diodes (LEDs) but excluding
    LEDs available as electronic components. Original spectral irradiance data 
    for incandescent-, LED- and discharge lamps are included. They are 
    complemented by data on the effect of temperature on the emission by 
    fluorescent tubes. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) 
    <doi:10.19232/uv4pb.2015.1.14>.
Spectral emission data for some frequently used light emitting
    diodes available as electronic components. Part of the 'r4photobiology' 
    suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Provides functions for quantifying visible (VIS) and ultraviolet
    (UV) radiation in relation to the photoreceptors Phytochromes,
    Cryptochromes, and UVR8 which are present in plants. It also includes data 
    sets on the optical properties of plants. Part of the 'r4photobiology' 
    suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral response data for broadband ultraviolet and visible
    radiation sensors. Angular response data for broadband ultraviolet and visible
    radiation sensors and diffusers used as entrance optics. Data obtained from
    multiple sources were used: author-supplied data from scientific research 
    papers, sensor-manufacturer supplied data, and published sensor 
    specifications. Part of the 'r4photobiology' suite Aphalo P. J. (2015) 
    <doi:10.19232/uv4pb.2015.1.14>.
Data for the extraterrestrial solar spectral irradiance and ground 
    level solar spectral irradiance and irradiance. In addition data for 
    shade light under vegetation and irradiance time series from different
    broadband sensors.  Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Constructors of waveband objects for commonly used biological
    spectral weighting functions (BSWFs) and for different wavebands describing
    named ranges of wavelengths in the ultraviolet (UV), visible (VIS)
    and infrared (IR) regions of the electromagnetic spectrum.  Part of the
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Features unstructured, structured and reverse geocoding using the
    'photon' geocoding API <https://photon.komoot.io/>.
    Facilitates the setup of local 'photon' instances to enable offline
    geocoding.
Connect R to the PhotosynQ platform (<https://photosynq.org>). It allows to login and logout,
    as well as receive project information and project data. Further it transforms the received JSON objects
    into a data frame, which can be used for the final data analysis.
Contains all phrasal verbs listed in <https://www.englishclub.com/ref/Phrasal_Verbs/>
            as data frame. Useful for educational purpose as well as for text mining.
A geochemical modeling program developed by the US Geological
    Survey that is designed to perform a wide variety of aqueous geochemical
    calculations, including speciation, batch-reaction, one-dimensional 
    reactive-transport, and inverse geochemical calculations.
A collection of methods for commonly undertaken analytical
    tasks, primarily developed for Public Health Scotland (PHS) analysts,
    but the package is also generally useful to others working in the
    healthcare space, particularly since it has functions for working with
    Community Health Index (CHI) numbers. The package can help to make
    data manipulation and analysis more efficient and reproducible.
Provides tools for penalised maximum likelihood estimation of hidden semi-Markov models (HSMMs) with flexible state dwell-time distributions. These include functions for model fitting, model checking and state-decoding. The package considers HSMMs for univariate time series with state-dependent gamma, normal, Poisson or Bernoulli distributions. For details, see Pohle, J., Adam, T. and Beumer, L.T. (2021): Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. <arXiv:2101.09197>.
Assessment of habitat selection by means of the permutation-based combination of sign tests (Fattorini et al., 2014 <DOI:10.1007/s10651-013-0250-7>). To exemplify the application of this procedure, habitat selection is assessed for a population of European Brown Hares settled in central Italy.
Phylogenetic clustering (phyloclustering) is an evolutionary
        Continuous Time Markov Chain model-based approach to identify
        population structure from molecular data without assuming
        linkage equilibrium. The package phyclust (Chen 2011) provides a
        convenient implementation of phyloclustering for DNA and SNP data,
        capable of clustering individuals into subpopulations and identifying
        molecular sequences representative of those subpopulations. It is
        designed in C for performance, interfaced with R for visualization,
        and incorporates other popular open source programs including
        ms (Hudson 2002) <doi:10.1093/bioinformatics/18.2.337>,
        seq-gen (Rambaut and Grassly 1997)
        <doi:10.1093/bioinformatics/13.3.235>,
        Hap-Clustering (Tzeng 2005) <doi:10.1002/gepi.20063> and
        PAML baseml (Yang 1997, 2007) <doi:10.1093/bioinformatics/13.5.555>,
        <doi:10.1093/molbev/msm088>,
        for simulating data, additional analyses, and searching the best tree.
        See the phyclust website for more information, documentations and
        examples.
Based on (but not identical to) the no-longer-maintained package 'phyext', provides enhancements to 'phylobase' classes, specifically for use by package 'SigTree'; provides classes and methods which help users manipulate branch-annotated trees (as in 'SigTree'); also provides support for a few other extra features.
A collection of utilities and 'ggplot2' extensions to assist with
  visualisations in genomic epidemiology. This includes the 'phylepic' chart,
  a visual combination of a phylogenetic tree and a matched epidemic curve.
  The included 'ggplot2' extensions such as date axes binned by week are
  relevant for other applications in epidemiology and beyond. The approach is
  described in Suster et al. (2024) <doi:10.1101/2024.04.02.24305229>.
The spatial interpolation of genetic distances between
	     samples is based on a modified kriging method that
	     accepts a genetic distance matrix and generates a map of
	     probability of lineage presence. This package also offers
	     tools to generate a map of  potential contact zones
	     between groups with user-defined thresholds in the tree
	     to account for old and recent divergence. Additionally,
	     it has functions for IDW interpolation using genetic data
	     and midpoints.
Provides a base S4 class for comparative methods, incorporating
    one or more trees and trait data.
Create and customize interactive phylogenetic trees using the 'phylocanvas' JavaScript library and the 'htmlwidgets' package. These trees can be used directly from the R console, from 'RStudio', in Shiny apps, and in R Markdown documents.  See <http://phylocanvas.org/>  for more information on the 'phylocanvas' library.
Interface to 'Phylocom' (<https://phylodiversity.net/phylocom/>),
    a library for analysis of 'phylogenetic' community structure and
    character evolution. Includes low level methods for interacting with
    the three executables, as well as higher level interfaces for methods
    like 'aot', 'ecovolve', 'bladj', 'phylomatic', and more.
Implementation of the automatic shift detection method for
    Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on
    phylogenies. Some tools to handle equivalent shifts configurations are also
    available. See Bastide et al. (2017) <doi:10.1111/rssb.12206> and
    Bastide et al. (2018) <doi:10.1093/sysbio/syy005>.
Manipulation and analysis of phylogenetically simulated
        data sets and phylogenetically based analyses using GLS.
Contains functions for developing phylogenetic trees as
    deeply-nested lists ("dendrogram" objects).
    Enables bi-directional conversion between dendrogram and
    "phylo" objects
    (see Paradis et al (2004) <doi:10.1093/bioinformatics/btg412>),
    and features several tools for command-line tree
    manipulation and import/export via Newick parenthetic text.
Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. The package also provides functions for simulating continuous or binary traits along the tree. Other tools include functions to test the adequacy of a population tree.
Conducts hierarchical partitioning to calculate individual contributions of phylogenetic tree and  predictors (groups) towards total R2  for phylogenetic linear regression models.
Provides functions to estimate statistical errors of phylogenetic
    metrics particularly to detect binary trait influence on diversification, as
    well as a function to simulate trees with fixed number of sampled taxa and trait
    prevalence.
Facilitates the testing of causal relationships among lineage-pair traits in a phylogenetically informed context. Lineage-pair traits are characters that are defined for pairs of lineages instead of individual taxa. Examples include the strength of reproductive isolation, range overlap, competition coefficient, diet niche similarity, and relative hybrid fitness. Users supply a lineage-pair dataset and a phylogeny. 'phylopairs' calculates a covariance matrix for the pairwise-defined data and provides built-in models to test for relationships among variables while taking this covariance into account. Bayesian sampling is run through built-in 'Stan' programs via the 'rstan' package. The various models and methods that this package makes available are described in Anderson et al. (In Review), Coyne and Orr (1989) <doi:10.1111/j.1558-5646.1989.tb04233.x>, Fitzpatrick (2002) <doi:10.1111/j.0014-3820.2002.tb00860.x>, and Castillo (2007) <doi:10.1002/ece3.3093>.
A comprehensive and easy to use R implementation of confirmatory
    phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer
    (2012) <doi:10.1111/j.1558-5646.2012.01790.x>.
Phylogenetic Diversity (PD, Faith 1992), Evolutionary Distinctiveness (ED, Isaac et al. 2007), Phylogenetic Endemism (PE, Rosauer et al. 2009; Laffan et al. 2016), and Weighted Endemism (WE, Laffan et al. 2016) for presence-absence raster.
    Faith, D. P. (1992) <doi:10.1016/0006-3207(92)91201-3>
    Isaac, N. J. et al. (2007) <doi:10.1371/journal.pone.0000296>
    Laffan, S. W. et al. (2016)  <doi:10.1111/2041-210X.12513>
    Rosauer, D. et al. (2009) <doi:10.1111/j.1365-294X.2009.04311.x>.
Computational infrastructure for biogeography, community ecology,
    and biodiversity conservation (Daru et al. 2020) <doi:10.1111/2041-210X.13478>.
    It is based on the methods described in Daru et al. (2020) <doi:10.1038/s41467-020-15921-6>.
    The original conceptual work is described in Daru et al. (2017) <doi:10.1016/j.tree.2017.08.013>
    on patterns and processes of biogeographical regionalization. Additionally, the package
    contains fast and efficient functions to compute more standard conservation measures
    such as phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness
    and global endangerment, as well as compositional turnover (e.g., beta diversity).
Implements novel tools for estimating sample sizes needed for phylogenetic studies, including
    studies focused on estimating the probability of true pathogen transmission between two cases given phylogenetic linkage and studies focused on tracking pathogen variants at a population level. Methods described in Wohl, Giles, and Lessler (2021) and in Wohl, Lee, DiPrete, and Lessler (2023).
Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation using 
    structural equation models (SEM), extending methods from Thorson et al. (2023) <doi:10.1111/2041-210X.14076>.  
    This implementation includes a minimal set of features, to 
    allow users to easily read all of the documentation and source code.  PCM using SEM 
    includes phylogenetic linear models and structural equation models as nested submodels, 
    but also allows imputation of missing values.  Features and comparison with other packages
    are described in Thorson and van der Bijl (2023) <doi:10.1111/jeb.14234>.
Provides functions for graph-based multiple-sample
    testing and visualization of microbiome data, in particular data
    stored in 'phyloseq' objects. The tests are based on those
    described in Friedman and Rafsky (1979)
    <http://www.jstor.org/stable/2958919>, and the tests are described
    in more detail in Callahan et al. (2016)
    <doi:10.12688/f1000research.8986.1>.
A collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method.
A unified method, called M statistic, is provided for detecting phylogenetic signals in continuous traits, discrete traits, and multi-trait combinations. Blomberg and Garland (2002) <doi:10.1046/j.1420-9101.2002.00472.x> provided a widely accepted statistical definition of the phylogenetic signal, which is the "tendency for related species to resemble each other more than they resemble species drawn at random from the tree". The M statistic strictly adheres to the definition of phylogenetic signal, formulating an index and developing a method of testing in strict accordance with the definition, instead of relying on correlation analysis or evolutionary models. The novel method equivalently expressed the textual definition of the phylogenetic signal as an inequality equation of the phylogenetic and trait distances and constructed the M statistic. Also, there are more distance-based methods under development.
Conduct various analyses on spatial phylogenetics. Use your data
    on an evolutionary tree and geographic distributions of the terminal taxa
    to compute diversity and endemism metrics, test significance with null 
    model randomization, analyze community turnover and biotic regionalization,
    and perform spatial conservation prioritizations. All functions support
    quantitative community data in addition to binary data.
Functions to read and write APE-compatible phylogenetic
  trees in NEXUS and Newick formats, while preserving annotations.
A collection of tools for building RAxML supermatrix using
             PHYLIP or aligned FASTA files. These functions will be
             useful for building large phylogenies using multiple markers.
Tools for calculating and viewing topological properties of phylogenetic trees.
Classification based analysis of DNA sequences to taxonomic groupings. This package primarily implements Naive Bayesian Classifier from the Ribosomal Database Project. This approach has traditionally been used to classify 16S rRNA gene sequences to bacterial taxonomic outlines; however, it can be used for any type of gene sequence. The method was originally described by Wang, Garrity, Tiedje, and Cole in Applied and Environmental Microbiology 73(16):5261-7 <doi:10.1128/AEM.00062-07>. The package also provides functions to read in 'FASTA'-formatted sequence data.
Analyzis and filtering of phylogenomics datasets. 
	It takes an input either a collection of gene trees (then transformed to matrices) or directly a collection of gene matrices and performs an iterative process to identify
	what species in what genes are outliers, and whose elimination significantly improves the concordance between the input matrices. The methods builds upon the Distatis approach (Abdi et al. (2005) <doi:10.1101/2021.09.08.459421>), a generalization of classical multidimensional scaling to multiple distance matrices.
A collection of functions to do model-based phylogenetic analysis. 
    It includes functions to calculate community phylogenetic diversity,
    to estimate correlations among functional traits while accounting for 
    phylogenetic relationships, and to fit phylogenetic generalized linear
    mixed models. The Bayesian phylogenetic generalized linear mixed models
    are fitted with the 'INLA' package (<https://www.r-inla.org>).
Provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The method is, however, based on the nowadays rather uncommon situation in which uncertainty about a phylogeny is well represented by adopting a single polytomous tree. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true  sequence of binary splits).
Contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active.  The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification 'br' or 'a' denoting bedrest/sleep and activity, respectively.  The package is designed to be used after wear/nonwear marking function in the 'PhysicalActivity' package.  Version 1.1 adds preschool thresholds and corrects for possible errors in algorithm implementation.
It provides a function "wearingMarking" for classification of monitor
    wear and nonwear time intervals in accelerometer data collected to assess
    physical activity. The package also contains functions for making plot for 
    accelerometer data and obtaining the summary of various information including 
    daily monitor wear time and the mean monitor wear time during valid days.      
    "deliveryPred" and "markDelivery" can classify days for ActiGraph delivery by mail;
    "deliveryPreprocess" can process accelerometry data for analysis by zeropadding incomplete 
    days and removing low activity days; "markPAI" can categorize physical activity
    intensity level based on user-defined cut-points of accelerometer counts. It also
    supports importing ActiGraph AGD files with "readActigraph" and "queryActigraph" functions.
Screens and sorts phylogenetic trees in both traditional and
    extended Newick format. Allows for the fast and flexible screening (within
    a tree) of Exclusive clades that comprise only the target taxa and/or Non-
    Exclusive clades that includes a defined portion of non-target taxa.
Determine the chlorophyll a (Chl a) concentrations of different 
    phytoplankton groups based on their pigment biomarkers. The method uses
    non-negative matrix factorisation and simulated annealing to minimise error
    between the observed and estimated values of pigment concentrations
    (Hayward et al. (2023) <doi:10.1002/lom3.10541>).
    The approach is similar to the widely used 'CHEMTAX' program
    (Mackey et al. 1996) <doi:10.3354/meps144265>, but is more straightforward,
    accurate, and not reliant on initial guesses for the pigment to Chl a
    ratios for phytoplankton groups.
A wide range of methods for phylogenetic analysis - concentrated in phylogenetic comparative biology, but also including numerous techniques for visualizing, analyzing, manipulating, reading or writing, and even inferring phylogenetic trees. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, and simulation of phylogenies and trait data. A broad range of plotting methods for phylogenies and comparative data include (but are not restricted to) methods for mapping trait evolution on trees, for projecting trees into phenotype space or a onto a geographic map, and for visualizing correlated speciation between trees. Lastly, numerous functions are designed for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. For instance, there are functions for computing consensus phylogenies from a set, for simulating phylogenetic trees and data under a range of models, for randomly or non-randomly attaching species or clades to a tree, as well as for a wide range of other manipulations and analyses that phylogenetic biologists might find useful in their research.
A 'Shiny' application for calculating phytosanitary inspection plans based on risks. It generates a diagram of pallets in a lot, highlights the units to be sampled, and documents them based on the selected sampling method (simple random or systematic sampling).
Most price indexes are made with a two-step procedure, where
    period-over-period elemental indexes are first calculated for a collection
    of elemental aggregates at each point in time, and then aggregated according
    to a price index aggregation structure. These indexes can then be chained
    together to form a time series that gives the evolution of prices with
    respect to a fixed base period. This package contains a collection of
    functions that revolve around this work flow, making it easy to build
    standard price indexes, and implement the methods described by
    Balk (2008, <doi:10.1017/CBO9780511720758>), von der Lippe (2007,
    <doi:10.3726/978-3-653-01120-3>), and the CPI manual (2020,
    <doi:10.5089/9781484354841.069>) for bilateral price indexes.
Functions for phylocom integration, community analyses, null-models, traits and evolution. Implements numerous ecophylogenetic approaches including measures of community phylogenetic and trait diversity, phylogenetic signal, estimation of trait values for unobserved taxa, null models for community and phylogeny randomizations, and utility functions for data input/output and phylogeny plotting. A full description of package functionality and methods are provided by Kembel et al. (2010) <doi:10.1093/bioinformatics/btq166>.
Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.
Provides a 'Shiny' input widget, pasteBoxInput, 
    that allows users to paste images directly into a 'Shiny' 
    application. The pasted images are captured as Base64 
    encoded strings and can be used within the application 
    for various purposes, such as display or further processing. 
    This package is particularly useful for applications that 
    require easy and quick image uploads without the need for 
    traditional file selection dialog boxes.
Performant interactive scatterplot for ~ 1 million points. Zoom, pan,
    and pick points. Includes tooltips, labels, a grid overlay, legend, and
    coupled interactions across multiple plots.
High Dynamic Range (HDR) images support a large range in luminosity
    between the lightest and darkest regions of an image.  To capture this range,
    data in HDR images is often stored as floating point numbers and in formats
    that capture more data and channels than standard image types.  This package supports
    reading and writing two types of HDR images; PFM (Portable Float Map) 
    and OpenEXR images. HDR images can be converted to lower
    dynamic ranges (for viewing) using tone-mapping.  A number of
    tone-mapping algorithms are included which are based on 
    Reinhard (2002) "Photographic tone reproduction for digital images" 
    <doi:10.1145/566654.566575>.
Computation of predictive information criteria (PIC) from select model object classes 
    for model selection in predictive contexts. In contrast to the more widely used Akaike 
    Information Criterion (AIC), which are derived under the assumption that target(s) of prediction 
    (i.e. validation data) are independently and identically distributed to the fitting data, the PIC
    are derived under less restrictive assumptions and thus generalize AIC to the more practically 
    relevant case of training/validation data heterogeneity. The methodology featured in this package is based on Flores (2021) <https://iro.uiowa.edu/esploro/outputs/doctoral/A-new-class-of-information-criteria/9984097169902771?institution=01IOWA_INST> "A new class of information criteria for improved prediction in the presence of training/validation data heterogeneity".
A collection of scripts and data files for the statistics text: 
 "Process Improvement using Data" <https://learnche.org/pid> and the online 
 course "Experimentation for Improvement" found on Coursera. The package 
 contains code for designed experiments, data sets and other convenience 
 functions used in the book.
Implements a novel predictive model, Partially Interpretable Estimators (PIE), which jointly trains an interpretable model and a black-box model to achieve high predictive performance as well as partial model. See the paper, Wang, Yang, Li, and Wang (2021) <doi:10.48550/arXiv.2105.02410>.
This function fits a reversible jump Bayesian piecewise exponential model that also includes the intensity of each event considered along with the rate of events.
Tokenizers break text into pieces that are more usable by
    machine learning models. Many tokenizers share some preparation steps.
    This package provides those shared steps, along with a simple
    tokenizer.
Provides an implementation of piecewise normalisation techniques useful when dealing with the communication of skewed and highly skewed data. It also provides utilities that recommends a normalisation technique based on the distribution of the data.
Functions to make board game graphics with the 'ggplot2', 'grid', 'rayrender', 'rayvertex', and 'rgl' packages.  Specializes in game diagrams, animations, and "Print & Play" layouts for the 'piecepack' <https://www.ludism.org/ppwiki> but can make graphics for other board game systems.  Includes configurations for several public domain game systems such as checkers, (double-18) dominoes, go, 'piecepack', playing cards, etc.
Implements piecewise structural equation modeling from a single
    list of structural equations, with new methods for non-linear, latent, and
    composite variables, standardized coefficients, query-based prediction and
    indirect effects. See <http://jslefche.github.io/piecewiseSEM/> for more.
Extends 'ggplot2' to help replace points in a scatter plot with pie-chart glyphs showing the relative proportions of different categories. The pie glyphs are independent of the axes and plot dimensions, to prevent distortions when the plot dimensions are changed.
Because larger (> 50 MB) data files cannot easily be committed to git,
  a different approach is required to manage data associated with an analysis in a 
  GitHub repository.  This package provides a simple work-around by allowing larger
  (up to 2 GB) data files to piggyback on a repository as assets attached to individual
  GitHub releases.  These files are not handled by git in any way, but instead are
  uploaded, downloaded, or edited directly by calls through the GitHub API. These
  data files can be versioned manually by creating different releases.  This approach
  works equally well with public or private repositories.  Data can be uploaded
  and downloaded programmatically from scripts. No authentication is required to
  download data from public repositories.
Check a data frame for personal information, including names, location, disability status, and geo-coordinates.
Global univariate minimization of Lipschitz functions is performed by using Pijavski method, which was published in Pijavski (1972) <DOI:10.1016/0041-5553(72)90115-2>.
An 'R' interface to 'pikchr' (<https://pikchr.org>, pronounced “picture”), a 'PIC'-like markup language for creating diagrams within technical documentation. Originally developed by Brian Kernighan, 'PIC' has been adapted into 'pikchr' by D. Richard Hipp, the creator of 'SQLite'. 'pikchr' is designed to be embedded in fenced code blocks of Markdown or other documentation markup languages, making it ideal for generating diagrams in text-based formats. This package allows R users to seamlessly integrate the descriptive syntax of 'pikchr' for diagram creation directly within the 'R' environment.
Provides 'pillar' and 'colonnade' generics designed
    for formatting columns of data using the full range of colours
    provided by modern terminals.
Fit a probabilistic index model as described in 
    Thas et al, 2012: <doi:10.1111/j.1467-9868.2011.01020.x>. The interface to the 
    modeling function has changed in this new version. The old version is
    still available at R-Forge.
An implementation of prediction intervals for random-effects meta-analysis:
  Higgins et al. (2009) <doi:10.1111/j.1467-985X.2008.00552.x>, Partlett and Riley (2017)
  <doi:10.1002/sim.7140>, and Nagashima et al. (2019) <doi:10.1177/0962280218773520>,
  <arXiv:1804.01054>.
Genomic data for the plant pathogen "Phytophthora infestans." It
    includes a variant file ('VCF'), a sequence file ('FASTA') and an annotation file
    ('GFF'). This package is intended to be used as example data for packages that
    work with genomic data.
To assist you with troubleshooting internet connection issues and
    assist in isolating packet loss on your network. It does this by allowing you to 
    retrieve the top trace route destinations your internet provider uses, and recursively 
    ping each server in series while capturing the results and writing them to a log file. 
    Each iteration it queries the destinations again, before shuffling the sequence of 
    destinations to ensure the analysis is unbiased and consistent across each trace route.
Check if a remote computer is up. It can either just call the
    system ping command, or check a specified TCP port.
Improved methods to construct prediction intervals for network meta-analysis. The parametric bootstrap and Kenward-Roger-type adjustment by Noma et al. (2022) <forthcoming> are implementable.
Market odds from from Pinnacle, an online sports betting bookmaker (see <https://www.pinnacle.com> for more information). Included are datasets for the Major League Baseball (MLB) 2016 season and the USA election 2016. These datasets can be used to build models and compare statistical information with the information from prediction markets.The Major League Baseball (MLB) 2016 dataset can be used for sabermetrics analysis and also can be used in conjunction with other popular Major League Baseball (MLB) datasets such as Retrosheets or the Lahman package by merging by GameID.
Packages data about the victims of the Pinochet regime as compiled by the Chilean National Commission for Truth and Reconciliation Report (1991, ISBN:9780268016463).
A 'PNAS'-alike style for 'rmarkdown', derived from the 'Proceedings of the
 National Academy of Sciences of the United States of America' ('PNAS') 'LaTeX' style,
 and adapted for use with 'markdown' and 'pandoc'.
Publish data sets, models, and other R objects, making it
    easy to share them across projects and with your colleagues. You can
    pin objects to a variety of "boards", including local folders (to
    share on a networked drive or with 'DropBox'), 'Posit Connect',
    'AWS S3', and more.
Provides a robust approach for omics data integration and disease subtyping. PINSPlus is fast and supports the analysis of large datasets with hundreds of thousands of samples and features. The software automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust against noise and data perturbation (Nguyen et al. (2019) <DOI: 10.1093/bioinformatics/bty1049>, Nguyen et al. (2017)<DOI: 10.1101/gr.215129.116>, Nguyen et al. (2021)<DOI: 10.3389/fonc.2021.725133>).
A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.
Collect  marketing data from Pinterest Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
    Use four spaces when indenting paragraphs within the Description.
Convert Chinese characters into Pinyin (the official romanization system for Standard Chinese in mainland China, Malaysia, Singapore, and Taiwan. See <https://en.wikipedia.org/wiki/Pinyin> for details), Sijiao (four or five numerical digits per character. See <https://en.wikipedia.org/wiki/Four-Corner_Method>.), Wubi (an input method with five strokes. See <https://en.wikipedia.org/wiki/Wubi_method>) or user-defined codes.
Measure productivity and efficiency using Data Envelopment Analysis 
    (DEA). Available methods include DEA under different technology assumptions, 
    bootstrapping of efficiency scores and calculation of the Malmquist 
    productivity index. Analyses can be performed either in the console or with 
    the provided 'shiny' app. See Banker, R.; Charnes, A.; Cooper, W.W. (1984)
    <doi:10.1287/mnsc.30.9.1078>, Färe, R.; Grosskopf, S. (1996) <doi:10.1007/978-94-009-1816-0>.
Implements the Product of Independent beta Probabilities dose Escalation (PIPE) design for dual-agent Phase I trials as described in Mander AP, Sweeting MJ (2015) <DOI:10.1002/sim.6434>.
Provides a simple function to bind a piped object to a placeholder
    symbol to enable complex function evaluation with the base R |> pipe.
To take nested function calls and convert them to a more readable form using pipes from package 'magrittr'.
A lightweight yet powerful framework for building robust data
    analysis pipelines. With 'pipeflow', you initialize a pipeline with your
    dataset and construct workflows step by step by adding R functions.
    You can modify, remove, or insert steps and parameters at any stage,
    while 'pipeflow' ensures the pipeline's integrity.
    Overall, this package offers a beginner-friendly framework that simplifies
    and streamlines the development of data analysis pipelines by making
    them modular, intuitive, and adaptable.
A framework for defining 'pipelines' of functions for applying data transformations, 
  model estimation and inverse-transformations, resulting in predicted value generation (or 
  model-scoring) functions that automatically apply the entire pipeline of functions required to go
  from input to predicted output.
Functions representing some useful empirical and data-driven
    models of heat loss, corrosion diagnostics, reliability and predictive
    maintenance of pipeline systems. The package is an option for technical
    engineering departments of heat generating and heat transfer companies
    that use or plan to use regulatory calculations in their activities.
    Methods are described in
    Timashev et al. (2016) <doi:10.1007/978-3-319-25307-7>,
    A.C.Reddy (2017) <doi:10.1016/j.matpr.2017.07.081>,
    Minenergo (2008) <https://docs.cntd.ru/document/902148459>,
    Minenergo (2005) <https://docs.cntd.ru/document/1200035568>,
    Xing LU. (2014) <doi:10.1080/23744731.2016.1258371>.
Provides various styles of function chaining methods: Pipe
    operator, Pipe object, and pipeline function, each representing a distinct
    pipeline model yet sharing almost a common set of features: A value can be
    piped to the first unnamed argument of a function and to dot symbol in an
    enclosed expression. The syntax is designed to make the pipeline more
    readable and friendly to a wide range of operations.
An embedded proximal interior point quadratic programming solver, which can solve dense and sparse quadratic programs, described in Schwan, Jiang, Kuhn, and Jones (2023) <doi:10.48550/arXiv.2304.00290>. Combining an infeasible interior point method with the proximal method of multipliers, the algorithm can handle ill-conditioned convex quadratic programming problems without the need for linear independence of the constraints. The solver is written in header only 'C++ 14' leveraging the 'Eigen' library for vectorized linear algebra. For small dense problems, vectorized instructions and cache locality can be exploited more efficiently. Allocation free problem updates and re-solves are also provided.
Calculate and optimize dynamic performance ratings of association football 
  teams competing in matches, in accordance with the method used in 
  the research paper "Determining the level of ability of football teams by 
  dynamic ratings based on the relative discrepancies in scores between adversaries", 
  by Constantinou and Fenton (2013) 
  <doi:10.1515/jqas-2012-0036>    
  This dynamic rating system has proven to provide superior 
  results for predicting association football outcomes.
Scored responses and responses times from the Canadian subsample of the PISA 2018 assessment, accessible as the "Cognitive items total time/visits data file" by OECD (2020) <https://www.oecd.org/pisa/data/2018database/>.
A wrapper around the 'Parsing Expression Grammar Template Library', a C++11 library for generating
    Parsing Expression Grammars, that makes it accessible within Rcpp. With this, developers can implement
    their own grammars and easily expose them in R packages.
Collection of pivotal algorithms 
             for: relabelling the MCMC chains in order to undo the label 
             switching problem in Bayesian mixture models;
             fitting sparse finite mixtures;
             initializing the centers of the classical k-means algorithm 
             in order to obtain a better clustering solution. 
             For further details see
             Egidi, Pappadà, Pauli and Torelli (2018b)<ISBN:9788891910233>.
Functions to get prediction intervals and prediction points of future observations from mixture distributions like gamma, beta, Weibull and normal.
'Pivotal Tracker' <https://www.pivotaltracker.com> is a project
    management software-as-a-service that provides a REST API. This package
    provides an R interface to that API, allowing you to query it and work with
    its responses.
Pivot easily by specifying rows, columns, values and split.
Create regular pivot tables with just a few lines of R.  
    More complex pivot tables can also be created, e.g. pivot tables
    with irregular layouts, multiple calculations and/or derived 
    calculations based on multiple data frames.  Pivot tables are
    constructed using R only and can be written to a range of
    output formats (plain text, 'HTML', 'Latex' and 'Excel'), 
    including with styling/formatting.
Run Queries against the API of 'Piwik Pro' <https://developers.piwik.pro/en/latest/custom_reports/http_api/http_api.html>. The result is a tibble.
Data about Disney Pixar films provided by Wikipedia. This
    package contains data about the films, the people involved, and their
    awards.
Contains functions to classify the pixels of an image file by its colour. It implements a simple form of the techniques known as Support Vector Machine adapted to this particular problem.
Puzzle game that can be played in the R console.
  Restore the pixel art by shifting rows.
Provides tools to show and draw image pixels using 'HTML' widgets 
  and 'Shiny' applications.  It can be used to visualize the 'MNIST' dataset
  for handwritten digit recognition or to create new image recognition datasets.
The introduction of the 'broom' package has made converting model
    objects into data frames as simple as a single function. While the 'broom'
    package focuses on providing tidy data frames that can be used in advanced
    analysis, it deliberately stops short of providing functionality for reporting
    models in publication-ready tables. 'pixiedust' provides this functionality with
    a programming interface intended to be similar to 'ggplot2's system of layers
    with fine tuned control over each cell of the table. Options for output include
    printing to the console and to the common markdown formats (markdown, HTML, and
    LaTeX). With a little 'pixiedust' (and happy thoughts) tables can really fly.
Functions for import, export, visualization and other manipulations of bitmapped images.
Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events
    and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. 
    Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. 
    Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.
Estimation of pharmacokinetic parameters using non-compartmental theory.
Achieve internal conversions of mass units, molar units,
	and volume units commonly used in pharmacokinetics, 
	as well as conversions between mass units and molar units.
Calculate and compare the prediction probability (PK) values for Anesthetic Depth Indicators.
    The PK values are widely used for measuring the performance of anesthetic depth and were first 
    proposed by the group of Dr. Warren D. Smith in the paper 
    Warren D. Smith; Robert C. Dutton; Ty N. Smith (1996) <doi:10.1097/00000542-199601000-00005> and
    Warren D. Smith; Robert C. Dutton; Ty N. Smith (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1199::AID-SIM218%3E3.0.CO;2-Y>.
    The authors provided two 'Microsoft Excel' files in xls format for calculating and comparing PK values.
    This package provides an easy-to-use API for calculating and comparing PK values in R.
Automate pharmacokinetic/pharmacodynamic bioanalytical procedures based on best practices and regulatory recommendations.
    The package impose regulatory constrains and sanity checking for common bioanalytical procedures. 
    Additionally, 'PKbioanalysis' provides a relational infrastructure for plate management and injection sequence.
Pharmacokinetics is the study of drug absorption, distribution, 
   metabolism, and excretion. The pharmacokinetics model explains that how the 
   drug concentration change as the drug moves through the different compartments
   of the body. For pharmacokinetic modeling and analysis, it is essential
   to understand the basic pharmacokinetic parameters. All parameters are 
   considered, but only some of parameters are used in the model. Therefore, 
   we need to convert the estimated parameters to the other parameters after 
   fitting the specific pharmacokinetic model. This package is developed to help
   this converting work. For more detailed explanation of pharmacokinetic 
   parameters, see "Gabrielsson and Weiner" (2007), "ISBN-10: 9197651001";
   "Benet and Zia-Amirhosseini" (1995) <DOI: 10.1177/019262339502300203>; 
   "Mould and Upton" (2012) <DOI: 10.1038/psp.2012.4>;
   "Mould and Upton" (2013) <DOI: 10.1038/psp.2013.14>.
Prepare pharmacokinetic/pharmacodynamic (PK/PD) data for PK/PD analyses. 
    This package provides functions to standardize infusion and bolus dose data while
    linking it to drug level or concentration data.
Provides functions used to build R packages. Locates
    compilers needed to build R packages on various platforms and ensures
    the PATH is configured appropriately so R can use them.
Metadata and package cache for CRAN-like repositories.  This
    is a utility package to be used by package management tools that want
    to take advantage of caching.
This provides utilities for creating classed error and warning
  conditions based on where the error originated.
Set configuration options on a per-package basis.
    Options set by a given package only apply to that package,
    other packages are unaffected.
Find recursive dependencies of 'R' packages from various
    sources. Solve the dependencies to obtain a consistent set of packages
    to install. Download packages, and install them. It supports packages
    on 'CRAN', 'Bioconductor' and other 'CRAN-like' repositories,
    'GitHub', package 'URLs', and local package trees and files. It caches
    metadata and package files via the 'pkgcache' package, and performs
    all 'HTTP' requests, downloads, builds and installations in parallel.
    'pkgdepends' is the workhorse of the 'pak' package.
Statically determine and visualize the function dependencies
    within and across packages. This may be useful for managing function 
    dependencies across a code base of multiple R packages.
Generate an attractive and useful website from a source
    package.  'pkgdown' converts your documentation, vignettes, 'README',
    and more to 'HTML' making it easy to share information about your
    package online.
Manage optional data for your package. The data can be hosted anywhere, and you have to give a Uniform Resource Locator (URL) for each file. File integrity checks are supported. This is useful for package authors who need to ship more than the 5 Megabyte of data currently allowed by the the Comprehensive R Archive Network (CRAN).
Interactively explore various dependencies of a package(s) (on the Comprehensive R Archive Network Like repositories) and perform analysis using tidy philosophy. Most of the functions return a 'tibble' object (enhancement of 'dataframe') which can be used for further analysis. The package offers functions to produce 'network' and 'igraph' dependency graphs. The 'plot' method produces a static plot based on 'ggnetwork' and 'plotd3' function produces an interactive D3 plot based on 'networkD3'.
It is often useful when developing an R package to track the relationship between functions in order to appropriately test and track changes. This package generates a graph of the relationship between all R functions in a package. It can also be used on any directory containing .R files which can be very useful for 'shiny' apps or other non-package workflows.
Provides a function kitten() which creates cute little 
 packages which pass R package checks. This sets it apart from 
 package.skeleton() which it calls, and which leaves imperfect files 
 behind. As this is not exactly helpful for beginners, kitten() offers 
 an alternative. Unit test support can be added via the 'tinytest'
 package (if present), and documentation-creation support can be
 added via 'roxygen2' (if present).
A tool, grammar, and standard to represent and exchange
    R package source code as text files. Converts one or more source
    packages to a text file and restores the package structures from the file.
Simulates the process of installing a package and then
    attaching it. This is a key part of the 'devtools' package as it
    allows you to rapidly iterate while developing a package.
A new metric named 'dependency heaviness' is proposed that measures the number 
    of additional dependency packages that a parent package brings to its child package 
    and are unique to the dependency packages imported by all other parents.  
    The dependency heaviness analysis is visualized by a customized heatmap. 
    The package is described in <doi:10.1093/bioinformatics/btac449>. 
    We have also performed the dependency heaviness analysis on the CRAN/Bioconductor 
    package ecosystem and the results are implemented as a web-based database 
    which provides comprehensive tools for querying dependencies of individual R packages. 
    The systematic analysis on the CRAN/Bioconductor ecosystem is described in 
    <doi:10.1016/j.jss.2023.111610>. From 'pkgndep' version 2.0.0, the heaviness 
    database includes snapshots of the CRAN/Bioconductor ecosystems for many old R versions.
Tools from the domain of graph theory can be used to quantify the complexity
             and vulnerability to failure of a software package. That is the guiding philosophy
             of this package. 'pkgnet' provides tools to analyze the dependencies between functions
             in an R package and between its imported packages.  See the pkgnet website for vignettes 
             and other supplementary information.
Read R package news files, regardless of whether or not the package
  is installed.
Search CRAN metadata about packages by keyword, popularity,
    recent activity, package name and more. Uses the 'R-hub' search
    server, see <https://r-pkg.org> and the CRAN metadata database, that
    contains information about CRAN packages. Note that this is _not_ a
    CRAN project.
Build your own universe of packages similar to the 'tidyverse'
    package <https://tidyverse.org/> with this meta-package creator. Create a
    package-verse, or meta package, by supplying a custom name for the
    collection of packages and the vector of desired package names to include–
    and optionally supply a destination directory, an indicator of whether to
    keep the created package directory, and/or a vector of verbs implement via
    the 'usethis' <http://usethis.r-lib.org/> package.
Public Key Infrastucture functions such as verifying certificates, RSA encription and signing which can be used to build PKI infrastructure and perform cryptographic tasks.
Implementation of a KL-based (Kullback-Leibler) test for MCAR (Missing Completely At Random) in the context of missing data as introduced in Michel et al. (2021)  <arXiv:2109.10150>.
We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) <DOI:10.1111/j.1467-9469.2007.00588.x>. The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) <DOI:10.1239/jap/1378401239>, and from mixtures of splines.
Compute standard Non-Compartmental Analysis (NCA) parameters for
    typical pharmacokinetic analyses and summarize them.
Simulate dose regimens for pharmacokinetic-pharmacodynamic (PK-PD)
        models described by differential equation (DE) systems. Simulation using 
        ADVAN-style analytical equations is also supported (Abuhelwa et al. (2015) 
        <doi:10.1016/j.vascn.2015.03.004>).
Conduct a noncompartmental analysis as closely as possible to the most widely used commercial software.
             Some features are
             1) CDISC SDTM terms
             2) Automatic slope selection with the same criterion of WinNonlin(R)
             3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
             4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
             * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
Fitting and testing probabilistic knowledge structures,
  especially the basic local independence model (BLIM, Doignon & Flamagne,
  1999) and the simple learning model (SLM), using the minimum discrepancy
  maximum likelihood (MDML) method (Heller & Wickelmaier, 2013
  <doi:10.1016/j.endm.2013.05.145>).
A tool for inferring kinase activity changes from 
    phosphoproteomics data. 'pKSEA' uses kinase-substrate 
    prediction scores to weight observed changes in 
    phosphopeptide abundance to calculate a phosphopeptide-level 
    contribution score, then sums up these contribution scores by 
    kinase to obtain a phosphoproteome-level kinase activity 
    change score (KAC score). 'pKSEA' then assesses the 
    significance of changes in predicted substrate abundances for 
    each kinase using permutation testing. This results in a 
    permutation score (pKSEA significance score) reflecting the 
    likelihood of a similarly high or low KAC from random chance, 
    which can then be interpreted in an analogous manner to an 
    empirically calculated p-value. 'pKSEA' contains default 
    databases of kinase-substrate predictions from 'NetworKIN' 
    (NetworKINPred_db) <http://networkin.info> 
    Horn, et. al (2014) <doi:10.1038/nmeth.2968>
    and of known kinase-substrate links from 'PhosphoSitePlus' 
    (KSEAdb) <https://www.phosphosite.org/>
    Hornbeck PV, et. al (2015) <doi:10.1093/nar/gku1267>.
Applying the global sensitivity analysis workflow to investigate 
    the parameter uncertainty and sensitivity in physiologically based 
    kinetic (PK) models, especially the physiologically based 
    pharmacokinetic/toxicokinetic model with multivariate outputs. 
    The package also provides some functions to check the convergence 
    and sensitivity of model parameters. The workflow was first mentioned 
    in Hsieh et al., (2018) <doi:10.3389/fphar.2018.00588>, then further 
    refined (Hsieh et al., 2020 <doi:10.1016/j.softx.2020.100609>).
Tools to process legacy format summary redistricting data files
    produced by the United States Census Bureau pursuant to P.L. 94-171. These
    files are generally available earlier but are difficult to work with as-is.
A semi-parametric estimation method for the Cox model
    with left-truncated data using augmented information
    from the marginal of truncation times.
Assessment of the prevalence of plastic
    debris in bird nests based on bootstrap replicates. The package allows for 
    calculating bootstrapped 95% confidence intervals for the estimated prevalence 
    of debris. Combined with a Bayesian approach, the resampling simulations can 
    be also used to define appropriate sample sizes to detect prevalence of plastics. 
    The method has wide application, and can also be applied to estimate confidence 
    intervals and define sample sizes for the prevalence of plastics ingested by any 
    other organisms. The method is described in Tavares et al. (Submitted).
Functions to prepare rankings data and fit the Plackett-Luce model
    jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce
    (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized
    to accommodate ties of any order in the ranking. Partial rankings, in which
    only a subset of items are ranked in each ranking, are also accommodated in
    the implementation. Disconnected/weakly connected networks implied by the
    rankings may be handled by adding pseudo-rankings with a hypothetical item.
    Optionally, a multivariate normal prior may be set on the log-worth
    parameters and ranker reliabilities may be incorporated as proposed by
    Raman and Joachims (2014) <doi:10.1145/2623330.2623654>. Maximum a
    posteriori estimation is used when priors are set. Methods are provided to
    estimate standard errors or quasi-standard errors for inference as well as
    to fit Plackett-Luce trees. See the package website or vignette for further
    details.
Provides methods for plotting potentially large (raster) images
    interactively on a plain HTML canvas. In contrast to package 'mapview'
    data are plotted without background map, but data can be projected to
    any spatial coordinate reference system.
    Supports plotting of classes 'RasterLayer', 'RasterStack', 'RasterBrick'
    (from package 'raster') as well as 'png' files located on disk. 
    Interactivity includes zooming, panning, and mouse location information. 
    In case of multi-layer 'RasterStacks' or 'RasterBricks', RGB image plots 
    are created (similar to 'raster::plotRGB' - but interactive).
Supports the creation of 'burndown' charts and 'gantt' diagrams.
Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge  Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution.
It includes functions to download and process the 'Planet NICFI' (Norway's International Climate and Forest Initiative) Satellite Imagery utilizing the Planet Mosaics API <https://developers.planet.com/docs/basemaps/reference/#tag/Basemaps-and-Mosaics>. 'GDAL' (library for raster and vector geospatial data formats) and 'aria2c' (paralleled download utility) must be installed and configured in the user's Operating System.
The goal of 'planets' is to provide of very simple and accessible data containing basic information from all known planets.
Perform flexible and quick calculations for Demand and Supply Planning, such as projected inventories and coverages, as well as replenishment plan. For any time bucket, daily, weekly or monthly, and any granularity level, product or group of products.
Provides access to the 'PlanScore' Application Programming Interface 
    (<https://github.com/PlanScore/PlanScore/blob/main/API.md>) for scoring redistricting
    plans. Allows for upload of plans from block assignment files and shape files. 
    For shapes in memory, such as from 'sf' or 'redist', it processes them to save
    and upload. Includes tools for tidying responses and saving output from the 
    website.
Coupled leaf gas exchange model, A-Ci curve simulation and
    fitting, Ball-Berry stomatal conductance models, 
    leaf energy balance using Penman-Monteith, Cowan-Farquhar
    optimization, humidity unit conversions. 
    See Duursma (2015) <doi:10.1371/journal.pone.0143346>.
Functions tailored for scientific and student communities involved in plant science research. Functionalities encompass estimation chlorophyll content according to Arnon (1949) <doi:10.1104/pp.24.1.1>, determination water potential of Polyethylene glycol(PEG)6000 as in Michel and Kaufmann (1973) <doi:10.1104/pp.51.5.914>  and functions related to estimation of yield related indices like Abiotic tolerance index as given by Moosavi et al.(2008)<doi:10.22059/JDESERT.2008.27115>, Geometric mean productivity (GMP) by Fernandez (1992) <ISBN:92-9058-081-X>, Golden Mean by Moradi et al.(2012)<doi:10.14207/ejsd.2012.v1n3p543>, HAM by Schneider et al.(1997)<doi:10.2135/cropsci1997.0011183X003700010007x>,MPI and TOL by Hossain etal., (1990)<doi:10.2135/cropsci1990.0011183X003000030030x>, RDI by Fischer et al. (1979)<doi:10.1071/AR9791001>,SSI by Fisher et al.(1978)<doi:10.1071/AR9780897>, STI by Fernandez (1993)<doi:10.22001/wvc.72511>,YSI by Bouslama & Schapaugh (1984)<doi:10.2135/cropsci1984.0011183X002400050026x>, Yield index by Gavuzzi et al.(1997)<doi:10.4141/P96-130>.
Extracts growth, survival, and local neighborhood density 
  information from repeated, fine-scale maps of organism occurrence. Further 
  information about this package can be found in our journal article, 
  "plantTracker: An R package to translate maps of plant occurrence into 
  demographic data" published in 2022 in Methods in Ecology and Evolution 
  (Stears, et al., 2022) <doi:10.1111/2041-210X.13950>.
Estimation, prediction, thresholding, transformation, and plotting for partially linear additive quantile regression.  Intuitive functions for fitting and plotting partially linear additive quantile regression models.  Uses and works with functions from the 'quantreg' package.
Contains tools for supervised analyses of incomplete, overlapping
  multiomics datasets. Applies partial least squares in multiple steps to find
  models that predict survival outcomes. See Yamaguchi et al. (2023)
  <doi:10.1101/2023.03.10.532096>.
Aims at detecting single nucleotide variation
    (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used
    as a surrogate marker for tumor, at each base position of an Next Generation
    Sequencing (NGS) analysis. Mutations are assessed by comparing the minor-allele
    frequency at each position to the measured PER in control samples.
Contains functions developed to combine the results of querying a plasmid database using
    short-read sequence typing with the results of a blast analysis against the query results.
Built on top of the 'glmnet' library by Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01>, the 'plasso' package follows Knaus (2022) <doi:10.1093/ectj/utac015> and comes up with two functions that estimate least squares Lasso and Post-Lasso models.
  The plasso() function adds coefficient paths for a Post-Lasso model to the standard 'glmnet' output.
  On top of that cv.plasso() cross-validates the coefficient paths for both the Lasso and Post-Lasso model and provides optimal hyperparameter values for the penalty term lambda.
Tools for interacting with data from experiments done in microtiter
    plates. Easily read in plate-shaped data and convert it to tidy format, 
    combine plate-shaped data with tidy data, and view tidy data in plate shape.
Collection of functions for working with multi-well microtitre
    plates, mainly 96, 384 and 1536 well plates.
Design parameters of the optimal two-period multiarm platform design
    (controlling for either family-wise error rate or pair-wise error rate) can be calculated
    using this package, allowing pre-planned deferred arms to be added during the trial. More
    details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J.
    (2022) "An optimal two-period multiarm platform design with new experimental arms added
    during the trial". Manuscript submitted for publication. For additional references: Dunnett,
    C. W. (1955) <doi:10.2307/2281208>.
Data and analysis from an experiment with improving touch 
    typing speed, using the tDCS PlatoWork headset produced by PlatoScience.
It creates a lattice plot to visualize panel or longitudinal data. The observed values are plotted as dots and the fitted values as lines, both against time. The plot is customizable and easy to edit, even if you do not know how to construct a lattice plot from scratch.
We present 'Platypus', an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. 'Platypus' provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology. This R version of 'Platypus' is part of the 'ePlatypus' ecosystem for computational analysis of immunogenomics data: Yermanos et al. (2021) <doi:10.1093/nargab/lqab023>, Cotet et al. (2023) <doi:10.1093/bioinformatics/btad553>.
Provides functions to visualise sports data. Converts data into a 
    format suitable for plotting charts. Helps to ease the process of working with
    messy sports data to a more user friendly format. Football data is accessed 
    through 'worldfootballR' '<https://github.com/JaseZiv/worldfootballR>' which 
    gets data from 'FBref' <https://fbref.com/en>, 'Transfermarkt' <https://www.transfermarkt.com/>, 
    'Understat' <https://understat.com/>, and 'fotmob' <https://www.fotmob.com/>.
Create an interactive pizza chart visualizing a specific player's statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: 'data', a dataframe containing player data for any sports; 'player_stats_col', a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; 'name_col', specifying the name of the column in the dataframe that contains the player names; and 'player_name', representing the specific player whose statistics will be visualized in the chart, serving as the chart title.
Implements schemes for estimating player or 
  team skill based on dynamic updating. Implemented methods include 
  Elo, Glicko, Glicko-2 and Stephenson. Contains pdf documentation 
  of a reproducible analysis using approximately two million chess 
  matches. Also contains an Elo based method for multi-player games
  where the result is a placing or a score. This includes zero-sum
  games such as poker and mahjong.
Perform inference in the secondary analysis setting with linked data potentially containing mismatch errors. Only the linked data file may be accessible and information about the record linkage process may be limited or unavailable. Implements the 'General Framework for Regression with Mismatched Data' developed by Slawski et al. (2023) <doi:10.48550/arXiv.2306.00909>. The framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Inference is based on composite likelihood and the Expectation-Maximization (EM) algorithm. The package currently supports Cox Proportional Hazards Regression (right-censored data only) and Generalized Linear Regression Models (Gaussian, Gamma, Poisson, and Logistic (binary models only)). Information about the underlying record linkage process can be incorporated into the method if available (e.g., assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches).
We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC).
Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker.
It performs a fast multi-trait genome-wide association analysis based on seemingly unrelated regressions. It tests for pleiotropic effects based on a series of Intersection-Union Wald tests. The package can handle large and unbalanced data and plot results.
Log-multiplicative association models (LMA) are
    models for cross-classifications of categorical variables
    where interactions are represented by products of category
    scale values and an association parameter. Maximum
    likelihood estimation (MLE) fails for moderate to large
    numbers of categorical variables. The 'pleLMA' package
    overcomes this limitation of MLE by using pseudo-likelihood
    estimation to fit the models to small or large
    cross-classifications dichotomous or multi-category variables.
    Originally proposed by Besag (1974,
    <doi:10.1111/j.2517-6161.1974.tb00999.x>), pseudo-likelihood
    estimation takes large complex models and breaks it down
    into smaller ones. Rather than maximizing the likelihood
    of the joint distribution of all the variables, a
    pseudo-likelihood function, which is the product likelihoods
    from conditional distributions, is maximized. LMA models can
    be derived from a number of different frameworks including
    (but not limited to) graphical models and uni-dimensional
    and multi-dimensional item response theory models. More
    details about the models and estimation can be found in
    the vignette.
The portmanteau local feature discriminant approach first identifies the local 
    discriminant features and their differential structures, then constructs the 
    discriminant rule by pooling the identified local features together. This method 
    is applicable to high-dimensional matrix-variate data. 
    See the paper by Xu, Luo and Chen (2021, <doi:10.1007/s13171-021-00255-2>).
Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data.
Provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.
Sequential Monte Carlo (SMC) inference for fully Bayesian
  Gaussian process (GP) regression and classification models by
  particle learning (PL) following Gramacy & Polson (2011) <arXiv:0909.5262>.
  The sequential nature of inference
  and the active learning (AL) hooks provided facilitate thrifty 
  sequential design (by entropy) and optimization
  (by improvement) for classification and
  regression models, respectively.
  This package essentially provides a generic
  PL interface, and functions (arguments to the interface) which
  implement the GP models and AL heuristics.  Functions for 
  a special, linked, regression/classification GP model and 
  an integrated expected conditional improvement (IECI) statistic 
  provide for optimization in the presence of unknown constraints.
  Separable and isotropic Gaussian, and single-index correlation
  functions are supported.
  See the examples section of ?plgp and demo(package="plgp") 
  for an index of demos.
Plots with high flexibility and easy handling, including
  informative regression diagnostics for many models.
Tools for both single and batch image manipulation and
    analysis (Olivoto, 2022 <doi:10.1111/2041-210X.13803>) and
    phytopathometry (Olivoto et al., 2022 <doi:10.1007/S40858-021-00487-5>).
    The tools can be used for the quantification of leaf area, object
    counting, extraction of image indexes, shape measurement, object
    landmark identification, and Elliptical Fourier Analysis of object
    outlines (Claude (2008) <doi:10.1007/978-0-387-77789-4>). The package
    also provides a comprehensive pipeline for generating shapefiles with
    complex layouts and supports high-throughput phenotyping of RGB,
    multispectral, and hyperspectral orthomosaics. This functionality
    facilitates field phenotyping using UAV- or satellite-based imagery.
Receiver Operating Characteristic (ROC) analysis is performed assuming samples are from the Power Lindley distribution. Specificity, sensitivity, area under the curve and ROC curve are provided.
Item response theory based methods are used to compute
        linking constants and conduct chain linking of unidimensional
        or multidimensional tests for multiple groups under a common
        item design.  The unidimensional methods include the Mean/Mean,
        Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous
        (1PL, 2PL and 3PL) and/or polytomous (graded response, partial
        credit/generalized partial credit, nominal, and multiple-choice
        model) items.  The multidimensional methods include the least
        squares method and extensions of the Haebara and Stocking-Lord
        method using single or multiple dilation parameters for
        multidimensional extensions of all the unidimensional
        dichotomous and polytomous item response models.  The package
        also includes functions for importing item and/or ability
        parameters from common IRT software, conducting IRT true score
        and observed score equating, and plotting item response
        curves/surfaces, vector plots, information plots, and comparison 
        plots for examining parameter drift.
Reads/write binary genotype file compatible with 'PLINK' <https://www.cog-genomics.org/plink/1.9/input#bed> into/from a R matrix; traverse genotype data one windows of variants at a time, like apply() or a for loop; reads/writes genotype relatedness/kinship matrices created by 'PLINK' <https://www.cog-genomics.org/plink/1.9/distance#make_rel> or 'GCTA' <https://cnsgenomics.com/software/gcta/#MakingaGRM> into/from a R square matrix. It is best used for bringing data produced by 'PLINK' and 'GCTA' into R workflow.
Genotyping arrays enable the direct measurement of an individuals
    genotype at thousands of markers. 'plinkQC' facilitates genotype quality
    control for genetic association studies as described by Anderson and
    colleagues (2010) <doi:10.1038/nprot.2010.116>. It makes 'PLINK' basic
    statistics (e.g. missing genotyping rates per individual, allele frequencies
    per genetic marker) and relationship functions accessible from 'R' and
    generates a per-individual and per-marker quality control report.
    Individuals and markers that fail the quality control can subsequently be
    removed to generate a new, clean dataset. Removal of individuals based on
    relationship status is optimised to retain as many individuals as possible
    in the study.
A multiple testing procedure for testing several groups of 
        hypotheses is implemented. Linear dependency among the hypotheses 
        within the same group is modeled by using hidden Markov Models. 
        It is noted that a smaller p value does not necessarily imply 
        more significance due to the dependency. A typical application is 
        to analyze genome wide association studies datasets, where SNPs 
        from the same chromosome are treated as a group and exhibit 
        strong linear genomic dependency. See Wei Z, Sun W, Wang K, 
        Hakonarson H (2009) <doi:10.1093/bioinformatics/btp476> for more details.
A set of estimators for models and (robust) covariance matrices, and tests for panel data
             econometrics, including within/fixed effects, random effects, between, first-difference, 
             nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models,
             panel generalized method of moments (GMM) and general FGLS models,
             mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators
             with common factors, variable coefficients and limited dependent variables models.
             Test functions include model specification, serial correlation, cross-sectional dependence,
             panel unit root and panel Granger (non-)causality. Typical references are general econometrics 
             text books such as Baltagi (2021), Econometric Analysis of Panel Data (<doi:10.1007/978-3-030-53953-5>),
             Hsiao (2014), Analysis of Panel Data (<doi:10.1017/CBO9781139839327>), and Croissant and Millo (2018), 
             Panel Data Econometrics with R (<doi:10.1002/9781119504641>).
Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) <doi.org/10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi/10.1002/sim.6224>.
Utilizes the 'lme4' and 'optimx' packages (previously the optim()
    function from 'stats') to estimate (generalized) linear mixed models (GLMM)
    with factor structures using a profile likelihood approach, as outlined in
    Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and
    Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>.
    Factor analysis and item response models can be extended to allow
    for an arbitrary number of nested and crossed random effects,
    making it useful for multilevel and cross-classified models.
Fits penalized linear mixed models that correct for
    unobserved confounding factors. 'plmmr' infers and corrects for the
    presence of unobserved confounding effects such as population
    stratification and environmental heterogeneity. It then fits a linear
    model via penalized maximum likelihood. Originally designed for the
    multivariate analysis of single nucleotide polymorphisms (SNPs)
    measured in a genome-wide association study (GWAS), 'plmmr' eliminates
    the need for subpopulation-specific analyses and post-analysis p-value
    adjustments.  Functions for the appropriate processing of 'PLINK'
    files are also supplied. For examples, see the package homepage.
    <https://pbreheny.github.io/plmmr/>.
Performs bivariate composite likelihood and full
        information maximum likelihood estimation for polytomous
        logit-normit (graded logistic) item response theory (IRT)
        models.
The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 
    2021 <doi:10.3389/fevo.2021.588292>) can be used for 
    a variety of multivariate problems when count data are at play, including 
    principal component analysis for count data, discriminant analysis, model-based clustering and 
    network inference. Implements variational algorithms to fit such models accompanied with a set of 
    functions for visualization and diagnostic.
A system to plan analyses within the mental model
    where you have one (or more) datasets and want to run either A) the same 
    function multiple times with different arguments, or B) multiple functions. 
    This is appropriate when you have multiple strata (e.g. locations, age groups)
    that you want to apply the same function to, or you have multiple variables 
    (e.g. exposures) that you want to apply the same statistical method to, or
    when you are creating the output for a report and you need multiple different
    tables or graphs.
A simple header-only logging library for C++.
  Add 'LinkingTo: plogr' to 'DESCRIPTION', and '#include <plogr.h>' in your C++ modules to use it.
Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likelihood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function  is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.
A method for the quantitative prediction with much predictors. 
    This package provides functions to construct the quantitative prediction 
    model with less overfitting and robust to noise.
Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.
Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, etc. Includes data sets from oceanography.
The 'rgl' implementation of plot3D functions.
Explore the world of R graphics with fun and interesting plot functions! 
    Use make_LED() to create dynamic LED screens, draw interconnected rings with Olympic_rings(), 
    and make festive Chinese couplets with chunlian(). 
    Unleash your creativity and turn data into exciting visuals!
Functions to assist in diagnostics and plotting during the causal inference modeling process. 
  Supplements the 'bartCause' package.
Proof of concept for implementing grammar of graphics using base plot. The bbplot() function initializes a 'bbplot' object to store input data, aesthetic mapping, a list of layers and theme elements. The object will be rendered as a graphic using base plot command if it is printed.
Create the density contour plot for bivariate inverse Gaussian distribution for given non negative random variables.
The 'plotcli' package provides terminal-based plotting in R.
             It supports colored scatter plots, line plots, bar plots, and box plots. The package allows users 
             to customize plot appearance, add titles, labels, ticks, and legends, and output the plot as a text-based visualization.
This function plots a contour line with a user-defined probability and tightness of fit.
Easily visualize and animate 'tabledap' and 'griddap' objects obtained via the 'rerddap' package in a simple one-line command,  using either base graphics or 'ggplot2' graphics. 'plotdap' handles extracting and reshaping the data,  map projections and continental outlines.  Optionally the data can be animated through time using the 'gganmiate' package.
Provides a ggplot2 front end to plot summary statistics on danish provinces,
    regions, municipalities, and zipcodes. The needed geoms of each of the four levels are 
    inherent in the package, thus making these types of plots easy for the user. This is essentially 
    an updated port of the previously available 'mapDK' package by Sebastian Barfort.
The goal of 'PlotFTIR' is to easily and quickly kick-start the production of journal-quality Fourier Transform Infra-Red (FTIR) spectral plots in R using 'ggplot2'. The produced plots can be published directly or further modified by 'ggplot2' functions.
    L'objectif de 'PlotFTIR' est de démarrer facilement et rapidement la production des tracés spectraux de spectroscopie infrarouge à transformée de Fourier (IRTF) de qualité journal dans R à l'aide de 'ggplot2'. Les tracés produits peuvent être publiés directement ou modifiés davantage par les fonctions 'ggplot2'.
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), 
    including both densities and overlaying mixture weight component curves from the fit GMM. The package
    also include the function, plot_cut_point, which plots the cutpoint (mu) from the GMM over a histogram
    of the distribution with several color options. Finally, the package includes the function, plot_mix_comps, 
    which is used in the plot_GMM function, and can be used to create a custom plot for overlaying mixture 
    component curves from GMMs. For the plot_mix_comps function, usage most often will be specifying 
    the "fun" argument within "stat_function" in a ggplot2 object.
An extension to 'ggplot2' and 'magick'. It contains 
    three groups of functions: Functions in the first group draw 'ggplot2' - based plots: geom_shading_bar() draws barplot 
	with shading colors in each bar. geom_rect_cm(), geom_circle_cm() and geom_ellipse_cm() draw rectangles, circles 
	and ellipses with centimeter as their unit. Thus their sizes do not change when the coordinate system or the aspect ratio 
	changes. annotation_transparent_text() draws labels with transparent texts. annotation_shading_polygon() draws irregular 
	polygons with shading colors. Functions in the second group generate coordinates for regular shapes and make linear transformations. Functions in the third group are 'magick' - based functions facilitating image processing.
Hidden Markov Models are useful for modeling
 sequential data. This package provides several functions
 implemented in C++ for explaining the algorithms used for
 Hidden Markov Models (forward, backward, decoding,
 learning).
Examines the characteristics of a data frame and a formula to
    automatically choose the most suitable type of plot out of the following supported
    options: scatter, violin, box, bar, density, hexagon bin, spine plot, and
    heat map. The aim of the package is to let the user focus on what to plot,
    rather than on the "how" during exploratory data analysis. It also automates
    handling of observation weights, logarithmic axis scaling, reordering of
    factor levels, and overlaying smoothing curves and median lines. Plots are
    drawn using 'ggplot2'.
Create interactive web graphics from 'ggplot2' graphs and/or a custom interface to the (MIT-licensed) JavaScript library 'plotly.js' inspired by the grammar of graphics.
Includes 'JavaScript' files that allow 'plotly' maps to render without an internet connection.
Markov chain Monte Carlo diagnostic plots. The purpose of the
  package is to combine existing tools from the 'coda' and 'lattice' packages,
  and make it easy to adjust graphical details.
Plot marginal effects for interactions estimated
    from linear models.
The main function, plot_mm(), is used for (gg)plotting output from mixture models, 
    including both densities and overlaying mixture weight component curves from the fit models in line with the
    tidy principles. The package includes several additional functions for added plot customization. 
    Supported model objects include: 'mixtools', 'EMCluster', and 'flexmix', with more from each in active dev. 
    Supported mixture model specifications include mixtures of univariate Gaussians, multivariate Gaussians, Gammas, 
    logistic regressions, linear regressions, and Poisson regressions.
Plot model surfaces for a wide variety of models
        using partial dependence plots and other techniques.
        Also plot model residuals and other information on the model.
Produces an Odds Ratio (OR) Plot to visualise the result of a 
    logistic regression analysis.
    Provide it with a binomial regression model produced by 'glm()' and it will 
    convert the estimates to odds ratios with a 95% confidence interval and 
    plot the results using 'ggplot2'.
Plot principal component histograms around a bivariate
        scatter plot.
Useful set of tools for plotting network diagrams in any kind of project.
Lots of plots, various labeling, axis and color scaling functions. The author/maintainer died in September 2023.
Most ROC curve plots obscure the cutoff values and inhibit
    interpretation and comparison of multiple curves. This attempts to address
    those shortcomings by providing plotting and interactive tools. Functions
    are provided to generate an interactive ROC curve plot for web use, and
    print versions. A Shiny application implementing the functions is also
    included.
Figures rendered on graphics devices are usually 
 rescaled to fit pre-determined device dimensions. 'plotscale' 
 implements the reverse: desired plot dimensions are specified 
 and device dimensions are calculated to accommodate marginal
 material, giving consistent proportions for plot elements.
 Default methods support grid graphics such as lattice and ggplot.
 See "example('devsize')" and "vignette('plotscale')".
A framework for creating interactive figures for data exploration. 
    All plots are automatically linked and support several kinds of interactive features,
    including selection, zooming, panning, and parameter manipulation. The figures
    can be interacted with either manually, using a mouse and a keyboard, 
    or by running code from inside an active R session.
Contains a graphical user interface to generate the diagnostic
    plots proposed by Bauer (2005; <doi:10.1207/s15328007sem1204_1>), 
    Pek & Chalmers (2015; <doi:10.1080/10705511.2014.937790>), and
    Pek, Chalmers, R. Kok, & Losardo (2015; <doi:10.3102/1076998615589129>) to investigate
    nonlinear bivariate relationships in latent regression models using structural
    equation mixture models (SEMMs).
Provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly.
    It is built upon 'ggplot2' and other plotting packages, and is designed to be easy to use and to work seamlessly with 'ggplot2' objects.
    It is particularly useful for creating complex plots with multiple layers, facets, and annotations.
    It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees.
    The package is designed to be flexible and customizable, and to work well with the 'ggplot2' ecosystem.
    The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.
Annotate plots with legends for continuous variables and colour
  spectra using the base graphics plotting tools; and manipulate irregular
  polygons.
Small self-contained plots for use in larger plots or to
    delegate plotting in other functions. Also contains a number of
    alternative color palettes and HSL color space based tools to modify
    colors or palettes.
Prediction limits for the Poisson distribution
  are produced from both frequentist and Bayesian viewpoints. Limiting results
  are provided in a Bayesian setting with uniform, Jeffreys and gamma as prior
  distributions. More details on the methodology are discussed in Bejleri and
  Nandram (2018) <doi:10.1080/03610926.2017.1373814> and Bejleri, Sartore and
  Nandram (2021) <doi:10.1007/s42952-021-00157-x>.
Power logit regression models for bounded
  continuous data, in which the density generator may be normal, Student-t, 
  power exponential, slash, hyperbolic, sinh-normal, or type II logistic. 
  Diagnostic tools associated with the fitted model, such as the residuals, 
  local influence measures, leverage measures, and goodness-of-fit statistics,
  are implemented. The estimation process follows the maximum likelihood approach
  and, currently, the package supports two types of estimators: the usual maximum 
  likelihood estimator and the penalized maximum likelihood estimator. More details
  about power logit regression models are described in 
  Queiroz and Ferrari (2022) <arXiv:2202.01697>.
Contains statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.
Multivariate regression methods
	Partial Least Squares Regression (PLSR), Principal Component
	Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).
Different methods for PLS analysis of
        one or two data tables such as Tucker's Inter-Battery, NIPALS,
        SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis.
        The main reference for this software is the awesome book (in
        French) 'La Regression PLS: Theorie et Pratique' by Michel
        Tenenhaus.
The plsdof package provides Degrees of Freedom estimates
        for Partial Least Squares (PLS) Regression. Model selection for
        PLS is based on various information criteria (aic, bic, gmdl)
        or on cross-validation. Estimates for the mean and covariance
        of the PLS regression coefficients are available. They allow
        the construction of approximate confidence intervals and the
        application of test procedures (Kramer and Sugiyama 
        2012 <doi:10.1198/jasa.2011.tm10107>).
        Further, cross-validation procedures for Ridge Regression and 
        Principal Components Regression are available.
Routines for PLS-based genomic analyses,
        implementing PLS methods for classification with
        microarray data and prediction of transcription factor
        activities from combined ChIP-chip analysis. The >=1.2-1
        versions include two new classification methods for microarray
        data: GSIM and Ridge PLS. The >=1.3 versions includes a
        new classification method combining variable selection and
        compression in logistic regression context: logit-SPLS; and
        an adaptive version of the sparse PLS.
Estimation, hypothesis tests, and variable selection in partially linear single-index models. Please see H. (2010) at <doi:10.1214/10-AOS835> for more details.
Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a random intercept and applies a lasso penalty to both the fixed effects and the coefficients associated with the nonlinear function. 
    The model also accommodates interactions between the nonlinear function and a grouping variable, allowing for the capture of group-specific nonlinearities. Nonlinear functions are modeled using a set of bases functions. Estimation is conducted using a penalized Expectation-Maximization algorithm, and the package offers flexibility in choosing between various information criteria for model selection. 
    Post-selection inference is carried out using a debiasing method, while inference on the nonlinear functions employs a bootstrap approach.
Bindings for additional regression models for use with the
    'parsnip' package, including ordinary and spare partial least squares
    models for regression and classification (Rohart et al (2017)
    <doi:10.1371/journal.pcbi.1005752>).
Fit a model with potentially many linear and smooth predictors. Interaction
    effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties
    we use iterative steps alternating between using linear predictors (lasso) and smooth predictors
    (generalised additive model).
Partial Least Squares Path Modeling (PLS-PM), Tenenhaus, Esposito Vinzi, Chatelin, Lauro (2005) <doi:10.1016/j.csda.2004.03.005>,
    analysis for both metric and non-metric data, as well as REBUS analysis, Esposito Vinzi, Trinchera, Squillacciotti, and Tenenhaus (2008) <doi:10.1002/asmb.728>.
Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013,  <http://journal-sfds.fr/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.
Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <arXiv:1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Provides methods for testing the goodness-of-fit 
    of generalized linear models (GLMs) using random projections. It is 
    specifically designed for high-dimensional scenarios where the number 
    of predictors substantially exceeds the sample size. The statistical 
    methodologies implemented in this package are detailed in the paper by 
    Wen Chen and Falong Tan (2024, <doi:10.48550/arXiv.2412.10721>).
Interfaces and methods for variable selection in Partial Least
    Squares. The methods include filter methods, wrapper methods and embedded
    methods. Both regression and classification is supported.
Calculates and depicts probabilistic long-term effects
    in binary models with temporal dependence variables. The package performs
    two tasks. First, it calculates the change in the probability of the event
    occurring given a change in a theoretical variable. Second, it calculates
    the rolling difference in the future probability of the event for two
    scenarios: one where the event occurred at a given time and one where the
    event does not occur. The package is consistent with the recent movement to
    depict meaningful and easy-to-interpret quantities of interest with the
    requisite measures of uncertainty. It is the first to make it easy for
    researchers to interpret short- and long-term effects of explanatory
    variables in binary autoregressive models, which can have important
    implications for the correct interpretation of these models.
Converts English phrases to singular or plural form based on
    the length of an associated vector.  Contains helper functions to
    create natural language lists from vectors and to include the length
    of a vector in natural language.
Provides a secure and user-friendly interface to interact with the 'Plug' <https://plugbytpf.com.br> 'API'. It enables developers to store and manage tokens securely using the 'keyring' package, retrieve data from 'API' endpoints with the 'httr2' package, and handle large datasets with chunked data fetching. Designed for simplicity and security, the package facilitates seamless integration with 'Plug' ecosystem.
Kernel density estimation with global bandwidth selection
	     via "plug-in".
Gives the ability to automatically generate and serve an HTTP API
    from R functions using the annotations in the R documentation around your
    functions.
Gives the ability to automatically deploy a plumber API
    from R functions on 'DigitalOcean' and other cloud-based servers.
Build 'Plumber' APIs that can be used in 'Tableau' workbooks.
    Annotations in R comments allow APIs to conform to the 'Tableau Analytics
    Extension' specification, so that R code can be used to power 'Tableau'
    workbooks.
The base R data.frame, like any vector, is
    copied upon modification. This behavior is at odds with
    that of GUIs and interactive graphics. To rectify this,
    plumbr provides a mutable, dynamic tabular data model.
    Models may be chained together to form the complex
    plumbing necessary for sophisticated graphical
    interfaces. Also included is a
    general framework for linking datasets; an typical
    use case would be a linked brush.
Handles and formats author information in scientific writing
    in 'R Markdown' and 'Quarto'. 'plume' provides easy-to-use and
    flexible tools for injecting author metadata in 'YAML' headers as well
    as generating author and contribution lists (among others) as strings
    from tabular data.
Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'.
Generates 'Plus Code' of geometric objects or data frames that contain them, giving the possibility to specify the precision of the area. The main feature of the package comes from the open-source code developed by 'Google Inc.' present in the repository <https://github.com/google/open-location-code/blob/main/java/src/main/java/com/google/openlocationcode/OpenLocationCode.java>. For details about 'Plus Code', visit <https://maps.google.com/pluscodes/> or <https://github.com/google/open-location-code>.
In ancient Roman mythology, 'Pluto' was the ruler of the underworld
  and presides over the afterlife. 'Pluto' was frequently conflated with 
  'Plutus', the god of wealth, because mineral wealth was found underground. 
  When plotting with R, you try once, twice, practice again and again, and finally 
  you get a pretty figure you want. It's a 'plot tour', a tour about repetition 
  and reward. Hope 'plutor' helps you on the tour!
A set of tools that solves a common set of problems: you need
    to break a big problem down into manageable pieces, operate on each
    piece and then put all the pieces back together.  For example, you
    might want to fit a model to each spatial location or time point in
    your study, summarise data by panels or collapse high-dimensional
    arrays to simpler summary statistics. The development of 'plyr' has
    been generously supported by 'Becton Dickinson'.
You can use this program for 3 sets of categorical data for propensity score matching.
            Assume that the data has 3 different categorical variables. You can use it to perform propensity matching of baseline indicator groupings. 
            The matching will make the differences in the baseline data smaller.
            This method was described by Alvaro Fuentes (2022) <doi:10.1080/00273171.2021.1925521>.
Performs Penalized Multivariate Analysis: a penalized
        matrix decomposition, sparse principal components analysis,
        and sparse canonical correlation analysis, described in
        Witten, Tibshirani and Hastie (2009)
        <doi:10.1093/biostatistics/kxp008> and Witten and Tibshirani
        (2009) Extensions of sparse canonical correlation analysis,
        with applications to genomic data
        <doi:10.2202/1544-6115.1470>.
We innovatively defined a pathway mutation accumulate perturbation score (PMAPscore) to reflect the position and the cumulative effect of the genetic mutations at the pathway level. Based on the PMAPscore of pathways, identified prognosis-related pathways altered by somatic mutation and predict immunotherapy efficacy by constructing a multiple-pathway-based risk model (Tarca, Adi Laurentiu et al (2008) <doi:10.1093/bioinformatics/btn577>).
Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups.  Implements methods described in:  Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>.  Webb-Robertson et al. (2011) <doi:10.1002/pmic.201100078>.  Matzke et al. (2011) <doi:10.1093/bioinformatics/btr479>.  Matzke et al. (2013) <doi:10.1002/pmic.201200269>.  Polpitiya et al. (2008) <doi:10.1093/bioinformatics/btn217>.  Webb-Robertson et al. (2010) <doi:10.1021/pr1005247>.
Monte Carlo based model choice for applied phylogenetics of
    continuous traits. Method described in  Carl Boettiger, Graham Coop,
    Peter Ralph (2012) Is your phylogeny informative? Measuring
    the power of comparative methods, Evolution 66 (7)
    2240-51. <doi:10.1111/j.1558-5646.2011.01574.x>.
Fit calibrations curves for clinical prediction models and calculate several associated 
  metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model 
  should align with observed probabilities. Calibration curves relate predicted probabilities 
  (or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function. 
  'pmcalibration' allows users to choose between several smoothers (regression splines, generalized 
  additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported. 
  See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>; 
  Austin and Steyerberg (2019) <doi:10.1002/sim.8281>; 
  Austin et al. (2020) <doi:10.1002/sim.8570>.
Aims to utilize model-based clustering (unsupervised)
        for high dimensional and ultra large data, especially in a distributed
        manner. The code employs 'pbdMPI' to perform a
        expectation-gathering-maximization algorithm
        for finite mixture Gaussian
        models. The unstructured dispersion matrices are assumed in the
        Gaussian models. The implementation is default in the single program
        multiple data programming model. The code can be executed
        through 'pbdMPI' and MPI' implementations such as 'OpenMPI'
        and 'MPICH'.
        See the High Performance Statistical Computing website
	<https://snoweye.github.io/hpsc/>
	for more information, documents and examples.
Note, that the 'PMCMR' package is superset
	     by the novel 'PMCMRplus' package. The 'PMCMRplus'
	     package contains all functions from 
	     'PMCMR' and many more parametric and non-parametric
	     multiple comparison procedures, one-factorial trend
	     tests, as well as improved method functions, 
	     such as print, summary and plot. 
	     The 'PMCMR' package is no longer maintained,
	     but kept for compatibility of reverse depending
	     packages for some time.
For one-way layout experiments the one-way ANOVA can
	     be performed as an omnibus test. All-pairs multiple comparisons 
	     tests (Tukey-Kramer test, Scheffe test, LSD-test) 
	     and many-to-one tests (Dunnett test) for normally distributed 
	     residuals and equal within variance are available. Furthermore,
	     all-pairs tests (Games-Howell test, Tamhane's T2 test, 
	     Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one
	     (Tamhane-Dunnett Test) for normally distributed residuals 
	     and heterogeneous variances are provided. Van der Waerden's normal
	     scores test for omnibus, all-pairs and many-to-one tests is
	     provided for non-normally distributed residuals and homogeneous
	     variances. The Kruskal-Wallis, BWS and Anderson-Darling
	     omnibus test and all-pairs tests
	     (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow-
	     Fligner test) as well as many-to-one (Nemenyi test, Dunn test,
	     U-test) are given for the analysis of variance by ranks. 
             Non-parametric trend tests (Jonckheere test, Cuzick test,
	     Johnson-Mehrotra test, Spearman test) are included. 
	     In addition, a Friedman-test for one-way ANOVA with repeated 
	     measures on ranks (CRBD) and Skillings-Mack test for unbalanced 
	     CRBD is provided with consequent all-pairs tests (Nemenyi test, 
	     Siegel test, Miller test, Conover test, Exact test)
	     and many-to-one tests (Nemenyi test, Demsar test, Exact test). 
	     A trend can be tested with Pages's test. Durbin's test 
	     for a two-way balanced incomplete block design (BIBD) is given 
	     in this package as well as Gore's test for CRBD with multiple
	     observations per cell is given.  Outlier tests, Mandel's k- and
	     h statistic as well as functions for Type I error and Power 
	     analysis as well as generic summary, print and plot methods 
             are provided.
Paired mass distance (PMD) analysis proposed in Yu, Olkowicz and Pawliszyn (2018) <doi:10.1016/j.aca.2018.10.062> and PMD based reactomics analysis proposed in Yu and Petrick (2020) <doi:10.1038/s42004-020-00403-z> for gas/liquid chromatography–mass spectrometry (GC/LC-MS) based non-targeted analysis. PMD analysis including GlobalStd algorithm and structure/reaction directed analysis. GlobalStd algorithm could found independent peaks in m/z-retention time profiles based on retention time hierarchical cluster analysis and frequency analysis of paired mass distances within retention time groups. Structure directed analysis could be used to find potential relationship among those independent peaks in different retention time groups based on frequency of paired mass distances. Reactomics analysis could also be performed to build PMD network, assign sources and make biomarker reaction discovery. GUIs for PMD analysis is also included as 'shiny' applications.
Calculation of Predictive Moran's eigenvector maps (pMEM), as
  defined by Guénard and Legendre (In Press) "Spatially-explicit predictions
  using spatial eigenvector maps" <doi:10.5281/zenodo.13356457>. Methods in
  Ecology and Evolution. This method enables scientists to predict the values of
  spatially-structured environmental variables. Multiple types of pMEM are
  defined, each one implemented on the basis of spatial weighting function
  taking a range parameter, and sometimes also a shape parameter. The code's
  modular nature enables programers to implement new pMEM by defining new
  spatial weighting functions.
Allows to download current and historical METAR weather reports
  extract and parse basic parameters and present main weather information. 
  Current reports are downloaded from Aviation Weather Center 
  <https://aviationweather.gov/data/metar/> and historical reports from
  Iowa Environmental Mesonet web page of Iowa State University
  ASOS-AWOS-METAR <http://mesonet.agron.iastate.edu/AWOS/>.
Given a project schedule and associated costs, this package calculates the earned value to date. It is an implementation of Project Management Body of Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)).
The Food and Agriculture Organization-56 Penman-Monteith is one of the important method for estimating evapotranspiration from vegetated land areas. This package helps to calculate reference evapotranspiration using the weather variables collected from weather station. Evapotranspiration is the process of water transfer from the land surface to the atmosphere through evaporation from soil and other surfaces and transpiration from plants. The package aims to support agricultural, hydrological, and environmental research by offering accurate and accessible reference evapotranspiration calculation. This package has been developed using concept of Córdova et al. (2015)<doi:10.1016/j.apm.2022.09.004> and Debnath et al. (2015) <doi:10.1007/s40710-015-0107-1>.
Routines for state estimate in a linear
    Gaussian state space model and a simple stochastic volatility model using
    particle filtering. Parameter inference is also carried out in these models
    using the particle Metropolis-Hastings algorithm that includes the particle
    filter to provided an unbiased estimator of the likelihood. This package is
    a collection of minimal working examples of these algorithms and is only
    meant for educational use and as a start for learning to them on your own.
Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used.
The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://dmg.org/>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products. The package isofor (used for anomaly detection) can be installed with devtools::install_github("gravesee/isofor").
Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to
    output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms.
Provides a simple interface for extracting various elements from
  the publicly available PubMed XML files, incorporating PubMed's regular
  updates, and combining the data with the NIH Open Citation Collection. See
  Schoenbachler and Hughey (2021) <doi:10.7717/peerj.11071>.
Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.
Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.
Implements multinomial CDF (P(N1<=n1, ..., Nk<=nk)) and tail probabilities (P(N1>n1, ..., Nk>nk)), as well as probabilities with both constraints (P(l1<N1<=u1, ..., lk<Nk<=uk)). Uses a method suggested by Bruce Levin (1981) <doi:10.1214/aos/1176345593>.
Computes the minimum sample size required for the external validation of an existing multivariable prediction model using the criteria proposed by Archer (2020) <doi:10.1002/sim.8766> and Riley (2021) <doi:10.1002/sim.9025>.
Provides an R implementation of the Particle Metropolis within
    Gibbs sampler for model parameter, covariance matrix and random effect
    estimation. A more general implementation of the sampler based on the paper
    by Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D.
    (2020) <doi:10.1016/j.jmp.2020.102368>. An HTML tutorial document describing
    the package is available at
    <https://university-of-newcastle-research.github.io/samplerDoc/> and
    includes several detailed examples, some background and troubleshooting
    steps.
Tools for the practical management of financial
  portfolios: backtesting investment and trading strategies,
  computing profit/loss and returns, analysing trades,
  handling lists of transactions, reporting, and more.  The
  package provides a small set of reliable, efficient and
  convenient tools for processing and analysing
  trade/portfolio data.  The manual provides all the details;
  it is available from
  <https://enricoschumann.net/R/packages/PMwR/manual/PMwR.html>.
  Examples and descriptions of new features are provided at
  <https://enricoschumann.net/notes/PMwR/>.
Provides a user interface to create or modify pharmacometric
    models for various modeling and simulation software platforms.
Estimate coefficient of variance percent (CV%) for any arbitrary distribution, including some built-in estimates for commonly-used transformations in pharmacometrics. Methods are described in various sources, but applied here as summarized in: Prybylski, (2024) <doi:10.1007/s40262-023-01343-2>.
Generate nicely formatted HTML tables to display estimation results for
  pharmacometric models.
Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given 
    times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- 
    and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) 
    <http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf>); parametric simulation from NONMEM-generated parameter estimates 
    and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).
Provides tools for downloading, reading and analyzing the Continuous National
  Household Sample Survey - PNADC, a household survey from Brazilian Institute
  of Geography and Statistics - IBGE. The data must be downloaded from the official
  website <https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.
Quasi likelihood-based methods for estimating linear and log-linear Poisson Network Autoregression models with p lags and covariates. Tools for testing the linearity versus several non-linear alternatives. Tools for simulation of multivariate count distributions, from linear and non-linear PNAR models, by using a specific copula construction. References include: Armillotta, M. and K. Fokianos (2023). "Nonlinear network autoregression". Annals of Statistics, 51(6): 2526--2552. <doi:10.1214/23-AOS2345>. Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584--612. <doi:10.1111/jtsa.12728>. Armillotta, M., Tsagris, M. and Fokianos, K. (2024). "Inference for Network Count Time Series with the R Package PNAR". The R Journal, 15/4: 255--269. <doi:10.32614/RJ-2023-094>.
Provides tools for downloading, reading and analyzing the National
  Survey of Demographic and Health - PNDS, a household survey from Brazilian Institute
  of Geography and Statistics - IBGE. The data must be downloaded from the official
  website <https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.
This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.
Provides tools for downloading, reading and analyzing the National
  Survey of Health - PNS, a household survey from Brazilian Institute
  of Geography and Statistics - IBGE. The data must be downloaded from the official
  website <https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.
PNW-Inspired Palettes for 'R' data visualizations. Palettes are variable 
	in length and checked for colorblind accessibility from hue, saturation, 
	and lightness value scaling using the 'Chroma.js  Color Palette Helper' 
	<https://gka.github.io/palettes/>.
Computes the optimal flow, Nash flow and the Price of Anarchy for any routing game defined within the game theoretical framework. The input is a routing game in the form of it’s cost and flow functions. Then transforms this into an optimisation problem, allowing both Nash and Optimal flows to be solved by nonlinear optimisation. See <https://en.wikipedia.org/wiki/Congestion_game> and Knight and Harper (2013) <doi:10.1016/j.ejor.2013.04.003> for more information.
Functions that interface with the 'Pocket' API (<https://getpocket.com/developer/>). Allows the user to get, add, and modify items in their own 'Pocket' account.
Penalized orthogonal-components regression (POCRE) is a supervised dimension reduction method for high-dimensional data. It sequentially constructs orthogonal components (with selected features) which are maximally correlated to the response residuals. POCRE can also construct common components for multiple responses and thus build up latent-variable models.
Provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) of Wages, Conaway and O'Quigley (2011) <doi:10.1177/1740774511408748> for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.
This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified.
    This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' <http://quodata.de/>. The modelling itself has been described by Uhlig et al. (2015) <doi:10.1007/s00769-015-1112-9>.
Set of functions that implement the PoDBAY method, described in the publication 'A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data' by Julie Dudasova, Regina Laube, Chandni Valiathan, Matthew C. Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite, Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021), <doi:10.1038/s41541-021-00377-6>.
Attempts to clean optical character recognition (OCR) errors in 
  legacy Scottish Post Office Directories. Further attempts to match records from 
  trades and general directories.
Village potential statistics (PODES) collects various information on village potential and challenges faced by villages in Indonesia. Information related to village potential includes economy, security, health, employment, communication and information, sports, entertainment, development, community empowerment, education, socio-culture, transportation in the village. Information related to challenges includes natural disasters, public health, environmental pollution, social problems and security disturbances that occur in the village.
A framework of interoperable R6 classes (Chang, 2020, <https://CRAN.R-project.org/package=R6>) for building ensembles of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,2005, <doi:10.1126/science.1116681>). The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes: model setup; generating model parameters via Latin hyper-cube sampling (Iman & Conover, 1980, <doi:10.1080/03610928008827996>); running multiple sampled model simulations; collating summary results; and validating and selecting an ensemble of models that best match known patterns. By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002, <doi:10.1093/genetics/162.4.2025>), although alternative user-defined functionality could be employed. The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for translocations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes.
Estimate large covariance matrices in approximate factor
        models by thresholding principal orthogonal complements.
Bayesian variable selection for regression models of under-reported
    count data as well as for (overdispersed) Poisson, negative binomal and
    binomial logit regression models using spike and slab priors.
The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment.
Implementation of both the exact and approximation methods for computing the cdf of the Poisson binomial distribution as described in Hong (2013) <doi: 10.1016/j.csda.2012.10.006>. It also provides the pmf, quantile function, and random number generation for the Poisson binomial distribution. The C code for fast Fourier transformation (FFT) is written by R Core Team (2019)<https://www.R-project.org/>, which implements the FFT algorithm in Singleton (1969) <doi: 10.1109/TAU.1969.1162042>.
Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518.
Functions for obtaining the density, random deviates 
             and maximum likelihood estimates of the Poisson lognormal 
             distribution and the bivariate Poisson lognormal distribution.
Provides an implementation of a rare variant association test that utilizes protein tertiary structure to increase signal and to identify likely causal variants. Performs structure-guided collapsing, which leads to local tests that borrow information from neighboring variants on a protein and that provide association information on a variant-specific level. For details of the implemented method see West, R. M.,  Lu, W., Rotroff, D. M., Kuenemann, M., Chang, S-M., Wagner M. J., Buse, J. B., Motsinger-Reif, A., Fourches, D., and Tzeng, J-Y. (2019) <doi:10.1371/journal.pcbi.1006722>.
Validate data in data frames, 'tibble' objects, 'Spark'
    'DataFrames', and database tables. Validation pipelines can be made using
    easily-readable, consecutive validation steps. Upon execution of the
    validation plan, several reporting options are available. User-defined
    thresholds for failure rates allow for the determination of appropriate
    reporting actions. Many other workflows are available including an
    information management workflow, where the aim is to record, collect, and
    generate useful information on data tables.
The function pointdensity returns a density count and the temporal average for
    every point in the original list. The dataframe returned includes four
    columns: lat, lon, count, and date_avg. The "lat" column is the original
    latitude data; the "lon" column is the original longitude data; the "count"
    is the density count of the number of points within a radius of
    radius*grid_size (the neighborhood); and the date_avg column includes the
    average date of each point in the neighborhood.
Integrated species distribution modeling is a rising field in quantitative ecology thanks to significant rises in the quantity of data available, increases in computational speed and the proven benefits of using such models. 
  Despite this, the general software to help ecologists construct such models in an easy-to-use framework is lacking. 
  We therefore introduce the R package 'PointedSDMs': which provides the tools to help ecologists set up integrated models and perform inference on them.
  There are also functions within the package to help run spatial cross-validation for model selection, as well as generic plotting and predicting functions.
  An introduction to these methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) <doi:10.1016/j.tree.2019.08.006>.
Estimate specification models for the state-dependent level of an optimal quantile/expectile forecast.
  Wald Tests and the test of overidentifying restrictions are implemented. Plotting of the estimated specification model is possible.
  The package contains two data sets with forecasts and realizations: the daily accumulated precipitation at London, UK from the high-resolution model of the
  European Centre for Medium-Range Weather Forecasts (ECMWF, <https://www.ecmwf.int/>) and GDP growth Greenbook data by the US Federal Reserve.
  See Schmidt, Katzfuss and Gneiting (2015) <arXiv:1506.01917> for more details on the identification and estimation of a directive behind a point forecast.
R has no built-in pointer functionality. The 'pointr' package fills this gap and lets you create pointers to R objects, including subsets of dataframes. This makes your R code more readable and maintainable.
Functions to calculate and plot event and pointer years as well as resilience indices. Designed for dendroecological applications, but also suitable to analyze patterns in other ecological time series.
Generation of multiple count, binary and continuous variables simultaneously 
             given the marginal characteristics and association structure. Throughout the package,
             the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. 
             The details of the method are explained in Amatya et al. (2015) <DOI:10.1080/00949655.2014.953534>.
Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution.  This package relies on FFTW to implement the discrete Fourier transform, so that it is much faster than the existing implementation of the same algorithm in R.
Generation of multiple count, binary and ordinal variables simultaneously 
             given the marginal characteristics and association structure. Throughout the package,
             the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya, A. and Demirtas, H. (2015) <DOI:10.1080/00949655.2014.953534>.
Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties. The details of the method are explained in Demirtas (2012) <DOI:10.1002/sim.5362>.
Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure.
             The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Functions to create confidence intervals for ratios of Poisson
    rates under misclassification using double sampling. Implementations of the 
    methods described in Kahle, D., P. Young, B. Greer, and D. Young (2016). 
    "Confidence Intervals for the Ratio of Two Poisson Rates Under One-Way 
    Differential Misclassification Using Double Sampling." Computational 
    Statistics & Data Analysis, 95:122–132.
Computation of robust standard errors of Poisson fixed effects 
    models, following Wooldridge (1999).
Creates a non-negative low-rank approximate factorization of a sparse counts matrix by maximizing Poisson
    likelihood with L1/L2 regularization (e.g. for implicit-feedback recommender systems or bag-of-words-based topic modeling)
    (Cortes, (2018) <arXiv:1811.01908>), which usually leads to very sparse user and item factors (over 90% zero-valued).
    Similar to hierarchical Poisson factorization (HPF), but follows an optimization-based approach with regularization
    instead of a hierarchical prior, and is fit through gradient-based methods instead of variational inference.
Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Generates multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture.
             The details of the method are explained in Yahav et al. (2012) <DOI:10.1002/asmb.901>.
Mixtures of Poisson Generalized Linear Models for high dimensional count data clustering. The (multivariate) responses can be partitioned into set of blocks. Three different parameterizations of the linear predictor are considered. The models are estimated according to the EM algorithm with an efficient initialization scheme <doi:10.1016/j.csda.2014.07.005>.
Efficient implementations of multiple exact and approximate methods as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006>, Biscarri, Zhao & Brunner (2018) <doi:10.1016/j.csda.2018.01.007> and Zhang, Hong & Balakrishnan (2018) <doi:10.1080/00949655.2018.1440294> for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalized Poisson binomial distribution.
Poisson disk sampling is a method of generating blue noise 
    sample patterns where all samples are at least a specified distance 
    apart. Poisson samples may be generated in two or three dimensions 
    with this package. The algorithm used is an 
    implementation of  Bridson's "Fast Poisson disk sampling in 
    arbitrary dimensions" <doi:10.1145%2F1278780.1278807>.
Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) <DOI:10.1007/s00180-022-01299-0>.
For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) <arXiv:1904.11745>.
Bindings for Poisson regression models for use with the
    'parsnip' package. Models include simple generalized linear models,
    Bayesian models, and zero-inflated Poisson models (Zeileis, Kleiber,
    and Jackman (2008) <doi:10.18637/jss.v027.i08>).
Provides a dataset of Pokemon information in 
  both English and Brazilian Portuguese. The dataset contains 949 rows and 
  22 columns, including information such as the Pokemon's name, ID, height, 
  weight, stats, type, and more.
Distributes data from the Polarization in Comparative Attitudes Project. Helper
    functions enable data retrieval in wide and tidy formats for user-defined
    countries and years. Provides support for case-insensitive country names in many
    languages. Mehlhaff (2022) <https://imehlhaff.net/files/Polarization%20and%20Democracy.pdf>.
Create hexagonal shape sticker image.
  'polaroid' can be used in user's web browser.
  'polaroid' can be used in 'shinyapps.io'.
  In both way, user can download created 'hexSticker' as 'PNG' image.
  'polaroid' is built based on 'argonDash', 'colourpicker' and 'hexSticker' R package.
Latent class analysis and latent class regression models 
    for polytomous outcome variables.  Also known as latent structure analysis.
Wrangle and annotate different types of political texts.
    It also introduces Urgency Analysis,
    a new method for the analysis of urgency in political texts.
Tools for simplifying the creation and management of data structures
    suitable for dealing with policy portfolios, that is, two-dimensional spaces 
    of policy instruments and policy targets. The package also allows to generate measures of
    portfolio characteristics and facilitates their visualization.
Learn optimal policies via doubly robust empirical
 welfare maximization over trees. Given doubly robust reward estimates, this package
 finds a rule-based treatment prescription policy, where the policy takes the form of
 a shallow decision tree that is globally (or close to) optimal.
This is a collection of data and functions for common metrics in political science research. Data measuring ideology, and functions calculating geographical diffusion and ideological diffusion - geog.diffuse() and ideo.dist(), respectively. Functions derived from methods developed in: Soule and King (2006) <doi:10.1086/499908>, Berry et al. (1998) <doi:10.2307/2991759>, Cruz-Aceves and Mallinson (2019) <doi:10.1177/0160323X20902818>, and Grossback et al. (2004) <doi:10.1177/1532673X04263801>.
Bundles the datasets and functions used in the textbook by Philip
    Pollock and Barry Edwards, an R Companion to Essentials of Political Analysis,
    Second Edition.
Authentication, user administration, hosting, and additional infrastructure for 'shiny' apps. See
  <https://polished.tech> for additional documentation and examples.
A very small package for more convenient use of 'NaileR'. You provide a data set containing a latent variable you want to understand. It generates a description and an interpretation of this latent variable using a Large Language Model. For perceptual data, it describes the stimuli used in the experiment.
Be responsible when scraping data from websites by following polite principles: introduce yourself, ask for permission, take slowly and never ask twice.
Detecting markers of politeness in English natural language. This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness. We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support.
A toolbox to facilitate the calculation of political system indicators for researchers. 
    This package offers a variety of basic indicators related to electoral systems, party systems, elections,
    and parliamentary studies, as well as others. Main references are:
    Loosemore and Hanby (1971) <doi:10.1017/S000712340000925X>;
    Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>;
    Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>;
    Rae (1968) <doi:10.1177/001041406800100305>;
    Hirschmaņ (1945) <ISBN:0-520-04082-1>;
    Kesselman (1966) <doi:10.2307/1953769>;
    Jones and Mainwaring (2003) <doi:10.1177/13540688030092002>;
    Rice (1925) <doi:10.2307/2142407>;
    Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>;
    SANTOS (2002) <ISBN:85-225-0395-8>.
Package for evaluating user-specified finite stage policies and learning optimal treatment policies via doubly robust loss functions. Policy learning methods include doubly robust learning of the blip/conditional average treatment effect and sequential policy tree learning. The package also include methods for optimal subgroup analysis. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.
Supports analysis of aerobiological data. 
    Available features include determination of pollen season limits, 
    replacement of outliers (Kasprzyk and Walanus (2014) <doi:10.1007/s10453-014-9332-8>),
    calculation of growing degree days (Baskerville and Emin (1969) <doi:10.2307/1933912>), 
    and determination of the base temperature for growing degree days
    (Yang et al. (1995) <doi:10.1016/0168-1923(94)02185-M).
Tools to estimate pollinator body size and co-varying traits. This package contains novel Bayesian predictive models of pollinator body size (for bees and hoverflies) as well as preexisting predictive models for pollinator body size (currently implemented for ants, bees, butterflies, flies, moths and wasps) as well as bee tongue length and foraging distance, total field nectar loads and wing loading. An additional GitHub repository <https://github.com/liamkendall/pollimetrydata> provides model objects to use the bodysize function internally. All models are described in Kendall et al (2018) <doi:10.1101/397604>.
Calculate common types of tables for weighted survey data.
    Options include topline and (2-way and 3-way) crosstab tables of
    categorical or ordinal data as well as summary tables of weighted
    numeric variables. Optionally, include the margin of error at
    selected confidence intervals including the design effect. The
    design effect is calculated as described by 
    Kish (1965) <doi:10.1002/bimj.19680100122> beginning
    on page 257. Output takes the form of tibbles (simple data frames).
    This package conveniently handles labelled data, such as that
    commonly used by 'Stata' and 'SPSS.' Complex survey design is 
    not supported at this time.
Package for corpus analysis using the Corpus Workbench 
    ('CWB', <https://cwb.sourceforge.io>) as an efficient back end for indexing
    and querying large corpora. The package offers functionality to flexibly create
    subcorpora and to carry out basic statistical operations (count, co-occurrences
    etc.). The original full text of documents can be reconstructed and inspected at
    any time. Beyond that, the package is intended to serve as an interface to 
    packages implementing advanced statistical procedures. Respective data structures
    (document-term matrices, term-co-occurrence matrices etc.) can be created based 
    on the indexed corpora.
Routines for the polynomial spline fitting routines
  hazard regression, hazard estimation with flexible tails, logspline,
  lspec, polyclass, and polymars, by C. Kooperberg and co-authors.
A 'shiny' app that allows to access and use the 'INVEKOS' API for field polygons in Austria. API documentation is available at <https://gis.lfrz.gv.at/api/geodata/i009501/ogc/features/v1/>.
Functions for evaluating the mass density, cumulative distribution function, quantile function and random variate generation for the Polya-Aeppli distribution, also known as the geometric compound Poisson distribution.  More information on the implementation can be found at Conrad J. Burden (2014) <arXiv:1406.2780>.
Simulate via Markov chain Monte Carlo (hit-and-run algorithm)
    a Dirichlet distribution conditioned to satisfy a finite set of linear
    equality and inequality constraints (hence to lie in a convex polytope
    that is a subset of the unit simplex).
Tools for creating, viewing, and assessing qualitative
 palettes with many (20-30 or more) colors. See Coombes and colleagues
 (2019) <doi:10.18637/jss.v090.c01>.
R port of Angus Johnson's open source library 'Clipper'. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.
Computes polychoric and polyserial correlations by quick "two-step" methods or ML, 
  optionally with standard errors; tetrachoric and biserial correlations are special cases.
A polycross is the pollination by natural hybridization of a group of genotypes, generally selected, grown in isolation from other compatible genotypes in such a way to promote random open pollination. A particular practical application of the polycross method occurs in the production of a synthetic variety resulting from cross-pollinated plants. Laying out these experiments in appropriate designs, known as polycross designs, would not only save experimental resources but also gather more information from the experiment. Different experimental situations may arise in polycross nurseries which may be requiring different polycross designs (Varghese et. al. (2015) <doi:10.1080/02664763.2015.1043860>. " Experimental designs for open pollination in polycross trials"). This package contains a function named PD() which generates nine types of polycross designs suitable for various experimental situations.
Numerical integration of continuously differentiable
    functions f(x,y) over simple closed polygonal domains.
    The following cubature methods are implemented:
    product Gauss cubature (Sommariva and Vianello, 2007,
    <doi:10.1007/s10543-007-0131-2>),
    the simple two-dimensional midpoint rule
    (wrapping 'spatstat.geom' functions), and
    adaptive cubature for radially symmetric functions via line
    integrate() along the polygon boundary (Meyer and Held, 2014,
    <doi:10.1214/14-AOAS743>, Supplement B).
    For simple integration along the axes, the 'cubature' package
    is more appropriate.
Provide easy methods to translate pieces of text.
  Functions send requests to translation services online.
Infer the genetic composition of individuals
    in terms of haplotype dosages for a haploblock, based
    on bi-allelic marker dosages, for any ploidy level.
    Reference: Voorrips and Tumino: PolyHaplotyper: haplotyping in polyploids based on bi-allelic marker dosage data.
    Submitted to BMC Bioinformatics (2021).
Evaluation of multiple integrals over convex polyhedra. This
    is useful when the bounds of the integrals are some linear
    combinations of the variables.
A wrapper around the C++ library 'polylabel' from 'Mapbox',
    providing an efficient routine for finding the approximate pole of
    inaccessibility of a polygon, which usually serves as an excellent candidate
    for labeling of a polygon.
Creation of linkage maps in polyploid species from marker dosage
    scores of an F1 cross from two heterozygous parents. Currently works for outcrossing diploid, autotriploid, autotetraploid and autohexaploid species, 
    as well as segmental allotetraploids. Methods are described in a manuscript of Bourke et al. (2018) <doi:10.1093/bioinformatics/bty371>. Since version 1.1.0,
    both discrete and probabilistic genotypes are acceptable input; for more details on the latter see Liao et al. (2021) <doi:10.1007/s00122-021-03834-x>.
Includes functions implementing the conditionally optimal matching algorithm, 
    which can be used to generate matched samples in designs with multiple groups. The algorithm
    is described in Nattino, Song and Lu (2022) <doi:10.1016/j.csda.2021.107364>.
Implementation of class "polyMatrix" for storing a matrix of polynomials and implements 
  basic matrix operations; including a determinant and characteristic polynomial.
  It is based on the package 'polynom' and uses a lot of its methods to implement matrix operations.
  This package includes 3 methods of triangularization of polynomial matrices:
  Extended Euclidean algorithm which is most classical but numerically unstable;
  Sylvester algorithm based on LQ decomposition;
  Interpolation algorithm is based on LQ decomposition and Newton interpolation.
  Both methods are described in 
  D. Henrion & M. Sebek, Reliable numerical methods for polynomial matrix triangularization,
  IEEE Transactions on Automatic Control (Volume 44, Issue 3, Mar 1999, Pages 497-508) <doi:10.1109/9.751344>
  and in 
  Salah Labhalla, Henri Lombardi & Roger Marlin, 
  Algorithmes de calcule de la reduction de Hermite d'une matrice a coefficients polynomeaux,
  Theoretical Computer Science (Volume 161, Issue 1-2, July 1996, Pages 69-92) <doi:10.1016/0304-3975(95)00090-9>.
A collection of functions to implement a class for univariate
  polynomial manipulations.
Implements univariate polynomial operations in R, including
  polynomial arithmetic, finding zeros, plotting, and some operations on
  lists of polynomials.
Functions to perform paternity exclusion via allele
    matching, in autopolyploid species having ploidy 4, 6, or 8. The
    marker data used can be genotype data (copy numbers known) or
    'allelic phenotype data' (copy numbers not known).
Analyse prescription drug deliveries to calculate several indicators of polypharmacy corresponding to the various definitions found in the literature.
  Bjerrum, L., Rosholm, J. U., Hallas, J., & Kragstrup, J. (1997) <doi:10.1007/s002280050329>.
  Chan, D.-C., Hao, Y.-T., & Wu, S.-C. (2009a) <doi:10.1002/pds.1712>.
  Fincke, B. G., Snyder, K., Cantillon, C., Gaehde, S., Standring, P., Fiore, L., ... Gagnon, D.R. (2005) <doi:10.1002/pds.966>.
  Hovstadius, B., Astrand, B., & Petersson, G. (2009) <doi:10.1186/1472-6904-9-11>.
  Hovstadius, B., Astrand, B., & Petersson, G. (2010) <doi:10.1002/pds.1921>.
  Kennerfalk, A., Ruigómez, A., Wallander, M.-A., Wilhelmsen, L., & Johansson, S. (2002) <doi:10.1345/aph.1A226>.
  Masnoon, N., Shakib, S., Kalisch-Ellett, L., & Caughey, G. E. (2017) <doi:10.1186/s12877-017-0621-2>.
  Narayan, S. W., & Nishtala, P. S. (2015) <doi:10.1007/s40801-015-0020-y>.
  Nishtala, P. S., & Salahudeen, M. S. (2015) <doi:10.1159/000368191>.
  Park, H. Y., Ryu, H. N., Shim, M. K., Sohn, H. S., & Kwon, J. W. (2016) <doi:10.5414/cp202484>.
  Veehof, L., Stewart, R., Haaijer-Ruskamp, F., & Jong, B. M. (2000) <doi:10.1093/fampra/17.3.261>.
Tools for reshaping, plotting, and manipulating matrices of orthogonal polynomials.
Quantitative trait loci (QTL) analysis and exploration of meiotic patterns in 
    autopolyploid bi-parental F1 populations. 
    For all ploidy levels, identity-by-descent (IBD) probabilities can be estimated.
    Significance thresholds, exploring QTL allele effects and visualising results are provided. 
    For more background and to reference the package see <doi:10.1093/bioinformatics/btab574>.
Read depth data from genotyping-by-sequencing (GBS) or restriction 
  site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian
  probability estimates of genotypes in polyploids or diploids.  The genotype 
  probabilities, posterior mean genotypes, or most probable genotypes can then
  be exported for downstream analysis.  'polyRAD' is described by Clark et al.
  (2019) <doi:10.1534/g3.118.200913>, and the Hind/He statistic for marker
  filtering is described by Clark et al. (2022) <doi:10.1186/s12859-022-04635-9>.
  A variant calling pipeline for highly duplicated genomes is also included and
  is described by Clark et al. (2020, Version 1) <doi:10.1101/2020.01.11.902890>.
Automate formation and evaluation of polynomial regression models. The motivation for this package is described in 'Polynomial Regression As an Alternative to Neural Nets' by Xi Cheng, Bohdan Khomtchouk, Norman Matloff, and Pete Mohanty (<arXiv:1806.06850>).
A collection of tools to handle microsatellite data of
 any ploidy (and samples of mixed ploidy) where allele copy number is not
 known in partially heterozygous genotypes.  It can import and export data in
 ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi',
 'POPDIST', 'STRand', and binary presence/absence formats.  It can calculate
 pairwise distances between individuals using a stepwise mutation model or
 infinite alleles model, with or without taking ploidies and allele frequencies
 into account.  These distances can be used for the calculation of clonal
 diversity statistics or used for further analysis in R.  Allelic diversity
 statistics and Polymorphic Information Content are also available.  polysat can 
 assist the user in estimating the ploidy of samples, and it can estimate allele 
 frequencies in populations, calculate pairwise or global differentiation statistics 
 based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'.  
 Functions are also included for assigning alleles to isoloci in cases where one pair 
 of microsatellite primers amplifies alleles from two or more independently
 segregating isoloci.  polysat is described by Clark and Jasieniuk (2011)
 <doi:10.1111/j.1755-0998.2011.02985.x> and Clark and Schreier (2017)
 <doi:10.1111/1755-0998.12639>.
Perform classic chi-squared tests and Ripol et al(1999)
             binomial confidence interval approach for autopolyploid
             dominant markers. Also, dominant markers may be generated
             for families of offspring where either one or both of the
             parents possess the marker. Missing values and
             misclassified markers may be generated at random.
Fits Bayesian mixture models to estimate marker dosage for dominant markers in autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al "Bayesian estimation of marker dosage in sugarcane and other autopolyploids" (2010, <doi:10.1007/s00122-010-1283-z>). May be used in conjunction with polySegratio for simulation studies and comparison with standard methods.
Given a data matrix with rows representing data vectors and columns representing variables, produces a directed polytree for the underlying causal structure. Based on the algorithm developed in Chatterjee and Vidyasagar (2022) <arxiv:2209.07028>. The method is fully nonparametric, making no use of linearity assumptions, and especially useful when the number of variables is large.
This algorithm classifies the trends into linear, quadratic, cubic, concealed and no-trend types. The "concealed trends" are those trends that possess quadratic or cubic forms, but the net change from the start of the time period to the end of the time period hasn't been significant. The "no-trend" category includes simple linear trends with statistically in-significant slope coefficient.
Routines for flexible functional form estimation via basis
    regression, with model selection via the adaptive LASSO or SCAD to prevent
    overfitting.
Provides functions to compute and plot power levels, minimum detectable effect sizes, and minimum required sample sizes for the test of the overall average effect size in meta-analysis of dependent effect sizes.
POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) <DOI:10.1002/gepi.22033>.
Implements the method described at the UCLA Statistical
    Consulting site <https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/>
    for checking if the proportional odds assumption holds for a 
    cumulative logit model.
Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Smallwood and Sondik (1973) <doi:10.1287/opre.21.5.1071>.
Installs an updated version of 'pomdp-solve', a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. A convenient R infrastructure is provided in the separate package pomdp. Kaelbling, Littman and Cassandra (1998) <doi:10.1016/S0004-3702(98)00023-X>.
Calculations of an information criterion are proposed to check the quality of simulations results of Agent-based models (ABM/IBM) or other non-linear rule-based models. The POMDEV measure (Pattern Oriented Modelling DEViance) is based on the Kullback-Leibler divergence and likelihood theory. It basically indicates the deviance of simulation results from field observations. Once POMDEV scores and metropolis-hasting sampling on different model versions are effectuated, POMIC scores (Pattern Oriented Modelling Information Criterion) can be calculated. This method could be further developed to incorporate multiple patterns assessment. Piou C, U Berger and V Grimm (2009) <doi:10.1016/j.ecolmodel.2009.05.003>.
Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits.
Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models).  The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods.  It is also a versatile platform for implementation of inference methods for general POMP models.
An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). 
    Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) <doi:10.1002/hbm.20259>. 
    Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) <doi:10.1016/j.neuroimage.2009.12.117>. 
    Gates, K. M., & Molenaar, P. C. M. (2012) <doi:10.1016/j.neuroimage.2012.06.026>. 
    Lutkepohl, H. (2007, ISBN:3540262393).
Inspired by Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569>,
    this methodology expands the idea by including Marks in the point process.
    Using efficient 'C++' code, the estimation is possible and made faster with
    'OpenMP' <https://www.openmp.org/> enabled computers. This package was
    developed under the project PTDC/MAT-STA/28243/2017, supported by
    Portuguese funds through the Portuguese Foundation for Science and
    Technology (FCT).
Code to identify functional enrichments across diverse taxa
    in phylogenetic tree, particularly where these taxa differ in
    abundance across samples in a non-random pattern. The motivation for
    this approach is to identify microbial functions encoded by diverse
    taxa that are at higher abundance in certain samples compared to
    others, which could indicate that such functions are broadly adaptive
    under certain conditions. See 'GitHub' repository for tutorial and
    examples: <https://github.com/gavinmdouglas/POMS/wiki>. Citation: Gavin M. Douglas, Molly G. Hayes, Morgan G. I. Langille, Elhanan Borenstein (2022) <doi:10.1093/bioinformatics/btac655>.
Homogeneity tests of the coefficients in panel data. Currently,
             only the Hsiao test for determining coefficient homogeneity between
             the panel data individuals is implemented, as described in Hsiao
             (2022), "Analysis of Panel Data" (<doi:10.1017/9781009057745>).
Finds equivalence classes corresponding to a symmetric relation
    or undirected graph.  Finds total order consistent with partial order
    or directed graph (so-called topological sort).
Enables the creation of object pools, which make it less
    computationally expensive to fetch a new object. Currently the only
    supported pooled objects are 'DBI' connections.
Provides functions to simulate Pool-seq data under models of
    demographic formation and to import Pool-seq data from real populations.
    Implements two ABC algorithms for performing parameter estimation and 
    model selection using Pool-seq data. Cross-validation can also be 
    performed to assess the accuracy of ABC estimates and model choice.
    Carvalho et al., (2022) <doi:10.1111/1755-0998.13834>.
When using pooled p-values to adjust for multiple testing, there is an inherent balance that must be struck between rejection based on weak evidence spread among many tests and strong evidence in a few, explored in Salahub and Olford (2023) <arXiv:2310.16600>. This package provides functionality to compute marginal and central rejection levels and the centrality quotient for p-value pooling functions and provides implementations of the chi-squared quantile pooled p-value (described in Salahub and Oldford (2023)) and a proposal from Heard and Rubin-Delanchy (2018) <doi:10.1093/biomet/asx076> to control the quotient's value.
Pool dilution is a isotope tracer technique wherein a 
    biogeochemical pool is artifically enriched with its heavy isotopologue 
    and the gross productive and consumptive fluxes of that pool are 
    quantified by the change in pool size and isotopic composition over time. 
    This package calculates gross production and consumption rates from
    closed-system isotopic pool dilution time series data. Pool size 
    concentrations and heavy isotope (e.g., 15N) content are measured over time 
    and the model optimizes production rate (P) and the first order rate 
    constant (k) by minimizing error in the model-predicted total pool size, 
    as well as the isotopic signature. The model optimizes rates by weighting 
    information against the signal:noise ratio of concentration and heavy-
    isotope signatures using measurement precision as well as the magnitude of 
    change over time. The calculations used here are based on von Fischer and 
    Hedin (2002) <doi:10.1029/2001GB001448> with some modifications.
The 2017 American College of Cardiology and American Heart
  Association blood pressure guideline recommends using 10-year predicted 
  atherosclerotic cardiovascular disease risk to guide the decision to 
  initiate or intensify antihypertensive medication. The guideline recommends 
  using the Pooled Cohort risk prediction equations to predict 10-year 
  atherosclerotic cardiovascular disease risk. This package implements the 
  original Pooled Cohort risk prediction equations and also incorporates 
  updated versions based on more contemporary data and statistical methods.
Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>.
Analyzing genetic data obtained from pooled samples. 
    This package can read in Fragment Analysis output files, process the data, 
    and score peaks, as well as facilitate various analyses, including cluster 
    analysis, calculation of genetic distances and diversity indices, 
    as well as bootstrap resampling for statistical inference. Specifically 
    tailored to handle genetic data efficiently, researchers can explore 
    population structure, genetic differentiation, and genetic relatedness 
    among samples. We updated some functions from Covarrubias-Pazaran et al. 
    (2016) <doi:10.1186/s12863-016-0365-6> to allow for the use of new file formats 
    and referenced the following to write our genetic analysis functions: 
    Long et al. (2022) <doi:10.1038/s41598-022-04776-0>, Jost (2008) 
    <doi:10.1111/j.1365-294x.2008.03887.x>, Nei (1973) 
    <doi:10.1073/pnas.70.12.3321>, Foulley et al. (2006) 
    <doi:10.1016/j.livprodsci.2005.10.021>, Chao et al. (2008) 
    <doi:10.1111/j.1541-0420.2008.01010.x>.
Functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in TreeMix, BayPass and SelEstim format).
Simulates pooled sequencing data under a variety of conditions. 
    Also allows for the evaluation of the average absolute difference between allele frequencies 
    computed from genotypes and those computed from pooled data. 
    Carvalho et al., (2022) <doi:10.1101/2023.01.20.524733>.
Functions for calculating power and fitting regression models in studies where a biomarker is measured in "pooled" samples rather than for each individual. Approaches for handling measurement error follow the framework of Schisterman et al. (2010) <doi:10.1002/sim.3823>.
Functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher's method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method). An introduction can be found in Cinar and Viechtbauer (2022) <doi:10.18637/jss.v101.i01>.
An easy-to-use tool for working with presence/absence tests
    on 'pooled' or 'grouped' samples. The primary application is for
    estimating prevalence of a marker in a population based on the results
    of tests on pooled specimens.  This sampling method is often employed
    in surveillance of rare conditions in humans or animals (e.g.
    molecular xenomonitoring). The package was initially conceived as an
    R-based alternative to the molecular xenomonitoring software,
    'PoolScreen' <https://sites.uab.edu/statgenetics/software/>. However,
    it goes further, allowing for estimates of prevalence to be adjusted
    for hierarchical sampling frames, and perform flexible mixed-effect
    regression analyses (McLure et al. Environmental Modelling and
    Software.  <DOI:10.1016/j.envsoft.2021.105158>). The package is
    currently in early stages, however more features are planned or in the
    works: e.g. adjustments for imperfect test specificity/sensitivity,
    functions for helping with optimal experimental design, and functions
    for spatial modelling.
Gene-based association tests using the actual impurity reduction (AIR) variable importance. The function aggregates AIR importance measures from a group of SNPs or probes and outputs a p-value for each gene. The procedures builds upon the method described in <doi:10.1093/Bioinformatics/Bty373> and will be published soon.
A replication of key functionality from 'dplyr' and the wider 'tidyverse' using only 'base'.
Population dynamic models underpin a range of analyses and applications in ecology and epidemiology. The various approaches for analysing population dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be defined in a different way. This makes it difficult to combine different modelling approaches and data types to solve a given problem. 'pop' aims to provide a flexible and easy to use common interface for constructing population dynamic models and enabling to them to be fitted and analysed in lots of different ways.
Simulate the dynamic of lion populations using a specific Individual-Based Model (IBM) compiled in C.
Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C, see Chapron et al. (2016) <doi:10.1016/j.ecolmodel.2016.08.012>.
Infers the trends of one or several animal populations over time 
    from series of counts. It does so by accounting for count precision 
    (provided or inferred based on expert knowledge, e.g. guesstimates), 
    smoothing the population rate of increase over time, and accounting for the
    maximum demographic potential of species. Inference is carried out in a 
    Bayesian framework. This work is part of the FRB-CESAB working group
    AfroBioDrivers 
    <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.
Construct and analyze projection matrix models from a demography study of marked individuals classified by age or stage. The package covers methods described in Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002).
Tools for modelling populations and demography using matrix projection models,
 with deterministic and stochastic model implementations. Includes population projection,
 indices of short- and long-term population size and growth, perturbation analysis,
 convergence to stability or stationarity, and diagnostic and manipulation tools.
The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). To reach this objective, we introduce a new design for phase I clinical trials, the posterior predictive (PoP) design. The PoP design is an innovative model-assisted design that is as simply as the conventional algorithmic designs as its decision rules can be pre-tabulated prior to the onset of trial, but is of more flexibility of selecting diverse target toxicity rates and cohort sizes. The PoP design has desirable properties, such as coherence and consistency. Moreover, the PoP design provides better empirical performance than the BOIN and Keyboard design with respect to high average probabilities of choosing the MTD and slightly lower risk of treating patients at subtherapeutic or overly toxic doses.
Optimal experimental designs for both population and individual
    studies based on nonlinear mixed-effect models. Often this is based on a
    computation of the Fisher Information Matrix. This package was developed
    for pharmacometric problems, and examples and predefined models are available
    for these types of systems. The methods are described in Nyberg et al. 
    (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) 
    <doi:10.1016/S0169-2607(03)00073-7>.
Enables computation of epidemiological statistics, including
    those where counts or mortality rates of the reference population are
    used.  Currently supported: excess hazard models (Dickman, Sloggett,
    Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean
    survival times, relative/net survival (in particular the Ederer II
    (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and
    Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and
    standardized incidence and mortality ratios, all of which can be
    easily adjusted for by covariates such as age. Fast splitting and
    aggregation of 'Lexis' objects (from package 'Epi') and other
    computations achieved using 'data.table'.
Estimate commonly used population genomic statistics and generate publication quality figures. 'PopGenHelpR' uses vcf, 'geno' (012), and csv files to generate output.
Provides several data sets and functions to accompany the book "Population Genetics with R: An Introduction for Life Scientists" (2021, ISBN:9780198829546).
Provides beginner friendly framework to analyse population genetic
    data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. 
    For detailed information how to use the package refer to the comprehensive
    tutorials or visit <http://www.popgenreport.org/>.
Implementation of assumption-lean and data-adaptive post-prediction inference (POPInf), for valid and efficient statistical inference based on data predicted by machine learning. See Miao, Miao, Wu, Zhao, and Lu (2023) <arXiv:2311.14220>.
Provides functions to estimate the kinship matrix of individuals from a large set of biallelic SNPs, and extract inbreeding coefficients and the generalized FST (Wright's fixation index).  Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.
Estimates DNA target concentration by classifying digital PCR (polymerase chain reaction) droplets as positive, negative, or rain, using Expectation-Maximization Clustering. The fitting is accomplished using the 'EMMIXskew' R package (v. 1.0.3) by Kui Wang, Angus Ng, and Geoff McLachlan (2018) as based on their paper "Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data" <doi:10.1109/DICTA.2009.88>.
Population genetic analyses for hierarchical analysis of partially
    clonal populations built upon the architecture of the 'adegenet' package. 
    Originally described in Kamvar, Tabima, and Grünwald (2014) 
    <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and 
    Grünwald (2015) <doi:10.3389/fgene.2015.00208>.
Functions that facilitate the elaboration of population pyramids.
Implements the Bayesian hierarchical model described by Wheldon, Raftery, Clark and Gerland (see: <doi:10.1080/01621459.2012.737729>) for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows, at the national level.
The use of overparameterization is proposed with combinatorial analysis to test a broader spectrum of possible ARIMA models.
    In the selection of ARIMA models, the most traditional methods such as correlograms or others, do not usually cover many alternatives to define the number of coefficients to be estimated in the model, which represents an estimation method that is not the best.
    The popstudy package contains several tools for statistical analysis in demography and time series based in Shryock research (Shryock et. al. (1980) <https://books.google.co.cr/books?id=8Oo6AQAAMAAJ>).
Functions to estimate and plot smooth or linear population trends, or population indices, 
    from animal or plant count survey data.
Tools for constructing detailed synthetic human populations
     from frequency tables. Add ages based on age groups and sex, create households, add 
     students to education facilities, create employers, add employers to employees, and 
     create interpersonal networks.
Run population simulations using an Individual-Based Model (IBM) compiled in C.
Fit linear splines to species time series to detect population growth scenarios based on Hyndman, R J and Mesgaran, M B and Cousens, R D (2015) <doi:10.1007/s10530-015-0962-8>.
Run simulations to assess the impact of various designs features
    and the underlying biological behaviour on the outcome of a Patient Derived
    Xenograft (PDX) population study. This project can either be deployed to a
    server as a 'shiny' app or installed locally as a package and run the app
    using the command 'populationPDXdesignApp()'.
Given a 
    set of source zone polygons such as
    census tracts or city blocks alongside with population counts and a 
    target zone of incogruent yet superimposed polygon features (such as
    individual buildings) populR transforms population counts from the 
    former to the latter using Areal Interpolation methods.
Perform tasks commonly encountered when
    preparing and analysing demographic data.
    Some functions are intended for end users, and
    others for developers. Includes functions for
    working with life tables.
The main attribute of 'PopVar' is the prediction of genetic variance in bi-parental populations, 
    from which the package derives its name. 'PopVar' contains a set of functions that use phenotypic and genotypic
    data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all, 
    or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction 
    accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015, <doi:10.2135/cropsci2015.01.0030>). 
    A dataset 'think_barley.rda' is included for reference and examples.
The name of the package is derived from the French, 'pour' ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2021) or ii) from non-replicated data by means of either a ridge estimator with multiple shrinkage targets (as presented in van Wieringen et al. 2020, <doi:10.1016/j.jmva.2020.104621>) or the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, <doi:10.1080/10618600.2019.1604374>). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018). On another note, the package also includes functionality for ridge-type estimation of the generalized linear model (as presented in van Wieringen, Binder, 2022, <doi:10.1080/10618600.2022.2035231>).
An R implementation of the cross-platform, language-independent "port4me" algorithm (<https://github.com/HenrikBengtsson/port4me>), which (1) finds a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user can open, (2) is designed to work in multi-user environments, (3), gives different users, different ports, (4) gives the user the same port over time with high probability, (5) gives different ports for different software tools, and (6) requires no configuration.
Obtener listado de datos, acceder y extender series del Portal de 
    Datos de Hacienda.Las proyecciones se realizan con 'forecast', 
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>. 
    Search, download and forecast time-series from the Ministry of Economy 
    of Argentina. Forecasts are built with the 'forecast' package, 
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>.
Download and generate summaries for the rodent,
    plant, ant, and weather data from the Portal Project. Portal is a
    long-term (and ongoing) experimental monitoring site in the Chihuahuan
    desert. The raw data files can be found at
    <https://github.com/weecology/portaldata>.
Contains common univariate and multivariate portmanteau test statistics for time series models. These tests are based on using asymptotic distributions such as chi-square distribution and based on using the Monte Carlo significance tests. Also, it can be used to simulate from univariate and multivariate seasonal time series models.
Classes for analysing and implementing equity portfolios,
    including routines for generating tradelists and calculating
    exposures to user-specified risk factors.
Simplify your portfolio optimization process by applying a contemporary modeling way to model and solve your portfolio problems. While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) <doi:10.1287/mnsc.37.5.519>, Rockafellar and Uryasev (2001) <doi:10.21314/JOR.2000.038> and Markowitz (1952) <doi:10.1111/j.1540-6261.1952.tb01525.x>.
Portfolio optimization and analysis routines and graphics.
Automated backtesting of multiple portfolios over multiple 
    datasets of stock prices in a rolling-window fashion. Intended for 
    researchers and practitioners to backtest a set of different portfolios, 
    as well as by a course instructor to assess the students in their portfolio 
    design in a fully automated and convenient manner, with results conveniently 
    formatted in tables and plots. Each portfolio design is easily defined as a
    function that takes as input a window of the stock prices and outputs the 
    portfolio weights. Multiple portfolios can be easily specified as a list 
    of functions or as files in a folder. Multiple datasets can be conveniently 
    extracted randomly from different markets, different time periods, and 
    different subsets of the stock universe. The results can be later assessed 
    and ranked with tables based on a number of performance criteria (e.g., 
    expected return, volatility, Sharpe ratio, drawdown, turnover rate, return 
    on investment, computational time, etc.), as well as plotted in a number of 
    ways with nice barplots and boxplots.
Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.
Provides a simple method to extract portions of a vector, matrix, or data.frame. 
    The relative portion size and the way the portion is selected can be chosen.
The functions are designed to find the efficient mean-variance frontier or 
 portfolio weights for static portfolio (called  Markowitz portfolio) analysis in resource 
 economics or nature conservation. Using the nonlinear programming solver ('Rsolnp'), 
 this package deals with the quadratic minimization of the variance-covariances without 
 shorting (i.e., non-negative portfolio weights) studied in Ando and Mallory (2012) 
 <doi:10.1073/pnas.1114653109>. See the examples, testing versions, and more details from: 
 <https://github.com/ysd2004/portn>.
Risk Attribution of a portfolio with Volatility Risk Analysis.
Designed to aid both academic researchers and asset managers in conducting factor based portfolio sorts.  
    Provides functionality to sort assets into portfolios for up to three factors via a conditional or unconditional sorting procedure.
Following Sommer (2022) <https://mediatum.ub.tum.de/1658240>
    portfolio level risk estimates (e.g. Value at Risk, Expected
    Shortfall) are estimated by modeling each asset univariately by an
    ARMA-GARCH model and then their cross dependence via a Vine Copula
    model in a rolling window fashion. One can even condition on
    variables/time series at certain quantile levels to stress test the
    risk measure estimates.
Win ratio approach to partially ordered data, such as multivariate ordinal 
  responses under product (consensus) or prioritized order. Two-sample tests and multiplicative
  regression models are implemented (Mao, 2024, under revision).
Provides a set of basic tools for generating, analyzing, summarizing and visualizing finite partially ordered sets. In particular, it implements flexible and very efficient algorithms for the extraction of linear extensions and for the computation of mutual ranking probabilities and other user-defined functionals, over them. The package is meant as a computationally efficient "engine", for the implementation of data analysis procedures, on systems of multidimensional ordinal indicators and partially ordered data, in the spirit of Fattore, M. (2016) "Partially ordered sets and the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-015-1059-6>, and Fattore M. and Arcagni, A. (2018) "A reduced posetic approach to the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-016-1501-4>.
In linear LS regression, calculate for a given design matrix
  the multiplier K of coefficient standard errors such that the
  confidence intervals [b - K*SE(b), b + K*SE(b)] have a
  guaranteed coverage probability for all coefficient estimates
  b in any submodels after performing arbitrary model selection.
Conduct post-selection inference for regression coefficients in linear models after they have been selected by adjusted R squared. The p-values and confidence intervals are valid after model selection with the same data. This allows the user to use all data for both model selection and inference without losing control over the type I error rate. The provided tests are more powerful than data splitting, which bases inference on less data since it discards all information used for selection.
Personalize drug regimens using individual pharmacokinetic (PK) and 
    pharmacokinetic-pharmacodynamic (PK-PD) profiles. By combining therapeutic 
    drug monitoring (TDM) data with a population model, 'posologyr' offers 
    accurate posterior estimates and helps compute optimal individualized dosing
    regimens. The empirical Bayes estimates are computed following the method 
    described by Kang et al. (2012) <doi:10.4196/kjpp.2012.16.2.97>.
Computes the exact probability density function of X/Y conditioned on positive quadrant for series of  bivariate distributions,for more details see Nadarajah,Song and Si (2019) <DOI:10.1080/03610926.2019.1576893>.
Calculates, via simulation, power and appropriate stopping
  alpha boundaries (and/or futility bounds) for sequential analyses (i.e.,
  group sequential design) as well as for multiple hypotheses (multiple tests
  included in an analysis), given any specified global error rate. This enables
  the sequential use of practically any significance test, as long as the
  underlying data can be simulated in advance to a reasonable approximation.
  Lukács (2022) <doi:10.21105/joss.04643>.
A collection of R Markdown templates for creating simple and easy 
  to personalize single page websites.
Free UK geocoding using data from Office for National Statistics.
    It is using several functions to get information about post codes, outward
    codes, reverse geocoding, nearest post codes/outward codes, validation, or
    randomly generate a post code. API wrapper around <https://postcodes.io>.
Generates simple and beautiful one-page HTML reference manuals 
    with package documentation. Math rendering and syntax highlighting are done 
    server-side in R such that no JavaScript libraries are needed in the
    browser, which makes the documentation portable and fast to load.
Use 'rmarkdown' and 'pagedown' to generate
    HTML and PDF conference posters.
Provides useful tools for both users and developers of packages 
  for fitting Bayesian models or working with output from Bayesian models. 
  The primary goals of the package are to: 
  (a) Efficiently convert between many different useful formats of
  draws (samples) from posterior or prior distributions.
  (b) Provide consistent methods for operations commonly performed on draws, 
  for example, subsetting, binding, or mutating draws.
  (c) Provide various summaries of draws in convenient formats.
  (d) Provide lightweight implementations of state of the art posterior 
  inference diagnostics. References: Vehtari et al. (2021) 
  <doi:10.1214/20-BA1221>.
An implementation of a non-parametric statistical model using a
    parallelised Monte Carlo sampling scheme. The method implemented in this
    package allows non-parametric inference to be regularized for small sample
    sizes, while also being more accurate than approximations such as
    variational Bayes. The concentration parameter is an effective sample size
    parameter, determining the faith we have in the model versus the data. When
    the concentration is low, the samples are close to the exact Bayesian
    logistic regression method; when the concentration is high, the samples are
    close to the simplified variational Bayes logistic regression. The method is
    described in full in the paper Lyddon, Walker, and Holmes (2018),
    "Nonparametric learning from Bayesian models with randomized objective
    functions" <arXiv:1806.11544>.
Generate all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data.
Implements a range of facilities for post-hoc analysis and
             summarizing linear models, generalized linear models and 
             generalized linear mixed models, including grouping and clustering 
             via pairwise comparisons using graph representations and efficient
             algorithms for finding maximal cliques of a graph. 
             Includes also non-parametric toos for post-hoc analysis.
             It has S3 methods for printing summarizing, and producing plots, 
             line and barplots suitable for post-hoc analyses.
Functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome.
This is a wrapper for the Mercury Parser API. The Mercury Parser is 
    a single API endpoint that takes a URL and gives you back the content reliably 
    and easily. 
    With just one API request, Mercury takes any web article and returns 
    only the relevant content — headline, author, body text, relevant images and 
    more — free from any clutter. It’s reliable, easy-to-use and free.
    See the webpage here: <https://mercury.postlight.com/>.
Provides adds postfix and infix logic operators for
    if, then, unless, and otherwise.
Implements the Phylogeny-Guided Microbiome OTU-Specific Association 
    Test method, which boosts the testing power by adaptively borrowing 
    information from phylogenetically close OTUs (operational taxonomic units)
    of the target OTU. This method
    is built on a kernel machine regression framework and allows for flexible 
    modeling of complex microbiome effects, adjustments for covariates, and 
    can accommodate both continuous and binary outcomes.
The aim of 'postpack' is to provide the infrastructure for a standardized workflow for 'mcmc.list' objects.
    These objects can be used to store output from models fitted with Bayesian inference using
    'JAGS', 'WinBUGS', 'OpenBUGS', 'NIMBLE', 'Stan', or even custom MCMC algorithms. Although the 'coda' R package provides
    some methods for these objects, it is somewhat limited in easily performing post-processing tasks for
    specific nodes. Models are ever increasing in their complexity and the number of tracked nodes, and oftentimes
    a user may wish to summarize/diagnose sampling behavior for only a small subset of nodes at a time
    for a particular question or figure. Thus, many 'postpack' functions support performing tasks on a
    subset of nodes, where the subset is specified with regular expressions. The functions in 'postpack'
    streamline the extraction, summarization, and diagnostics of specific monitored nodes after model fitting.
    Further, because there is rarely only ever one model under consideration, 'postpack' scales efficiently 
    to perform the same tasks on output from multiple models simultaneously, facilitating rapid assessment 
    of model sensitivity to changes in assumptions.
Some functions useful to perform a Peak Over Threshold
        analysis in univariate and bivariate cases, see Beirlant et al. (2004)
        <doi:10.1002/0470012382>. A user guide is available in the vignette.
Provides functions to compute the potential model as defined by
    Stewart (1941) <doi:10.1126/science.93.2404.89>. Several options are available
    to customize the model, such as the possibility to fine-tune the distance
    friction functions or to use custom distance matrices. Some computations are
    parallelized to improve their efficiency.
Calculate POTH for treatment hierarchies from frequentist and Bayesian network meta-analysis. POTH quantifies the certainty in a treatment hierarchy. Subset POTH, POTH residuals, and cumulative POTH can also be calculated to improve interpretation of treatment hierarchies.
Store and retrieve data from options() using syntax derived from
    the 'here' package. 'potions' makes it straightforward to update and 
    retrieve options, either in the workspace or during package development, 
    without overwriting global options.
Translating messages in R packages is managed using the po
    top-level directory and the 'gettext' program. This package provides
    some helper functions for building this support in R packages, e.g.
    common validation & I/O tasks.
Do Markov chain Monte Carlo (MCMC) simulation of Potts models
   (Potts, 1952, <doi:10.1017/S0305004100027419>),
   which are the multi-color generalization of Ising models
   (so, as as special case, also simulates Ising models).
   Use the Swendsen-Wang algorithm (Swendsen and Wang, 1987,
   <doi:10.1103/PhysRevLett.58.86>) so MCMC is fast.
   Do maximum composite likelihood estimation of parameters
   (Besag, 1975, <doi:10.2307/2987782>,
   Lindsay, 1988, <doi:10.1090/conm/080>).
There are three sets of functions. The first produces
             basic properties of a graph and generates samples from
             multinomial distributions to facilitate the simulation
	     functions (they maybe used for other purposes as well).
	     The second provides various simulation functions for a
	     Potts model in Potts, R. B. (1952)
	     <doi:10.1017/S0305004100027419>.
	     The third currently includes only one function which
	     computes the normalizing constant of a Potts model
	     based on simulation results.
Computes the D', Wn, and conditional asymmetric linkage disequilibrium (ALD) measures for pairs of genetic loci. Performs these linkage disequilibrium (LD) calculations on phased genotype data recorded using Genotype List (GL) String or columnar formats. Alternatively, generates expectation-maximization (EM) estimated haplotypes from phased data, or performs LD calculations on EM estimated haplotypes. Performs sign tests comparing LD values for phased and unphased datasets, and generates heat-maps for each LD measure. Described by Osoegawa et al. (2019a) <doi:10.1016/j.humimm.2019.01.010>, and Osoegawa et. al. (2019b) <doi:10.1016/j.humimm.2019.05.018>.
The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to 
    estimate the phylogenetic heritability of continuous traits, to test 
    hypotheses of neutral evolution versus stabilizing selection, to quantify 
    the strength of stabilizing selection, to estimate measurement error and to
    make predictions about the evolution of a phenotype and phenotypic variation 
    in a population. The package implements combined maximum likelihood and 
    Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed 
    Model, fast parallel likelihood calculation, maximum likelihood 
    inference of the genotypic values at the tips, functions for summarizing and
    plotting traces and posterior samples, functions for simulation of a univariate 
    continuous trait evolution model along a phylogenetic tree. So far, the 
    package has been used for estimating the heritability of quantitative traits
    in macroevolutionary and epidemiological studies, see e.g. 
    Bertels et al. (2017) <doi:10.1093/molbev/msx246> and 
    Mitov and Stadler (2018) <doi:10.1093/molbev/msx328>. The algorithm for 
    parallel POUMM likelihood calculation has been published in 
    Mitov and Stadler (2019) <doi:10.1111/2041-210X.13136>.
An implementation of the Partition Of variation (POV) method as
    developed by Dr. Thomas A Little <https://thomasalittleconsulting.com> in
    1993 for the analysis of semiconductor data for hard drive manufacturing.
    POV is based on sequential sum of squares and is an exact method that
    explains all observed variation. It quantitates both the between and within
    factor variation effects and can quantitate the influence of both continuous
    and categorical factors.
The R package 'povmap' supports small area estimation of means and 
  poverty headcount rates. It adds several new features to the 'emdi' package
  (see "The R Package emdi for Estimating and Mapping Regionally Disaggregated 
  Indicators" by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07>). 
  These include new options for incorporating survey weights, ex-post 
  benchmarking of estimates, two additional transformations, several new 
  convenient functions to assist with reporting results, and a wrapper function 
  to facilitate access from 'Stata'.
Fast exponentiation when the exponent is an integer.
Density, distribution function, quantile function and random generation for the family of power and reversal power distributions.
Full pattern summation of X-ray powder diffraction data as
  described in Chipera and Bish (2002) <doi:10.1107/S0021889802017405> and
  Butler and Hillier (2021) <doi:10.1016/j.cageo.2020.104662>.
  Derives quantitative estimates of crystalline and amorphous phase
  concentrations in complex mixtures.
Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations
    allow for transforming variables with distributions distant from 0 to
    normality. Transformers are implemented as S4 objects. These allow for
    transforming new instances to normality after optimising fitting parameters
    on other data. A test for central normality allows for rejecting 
    transformations that fail to produce a suitably normal distribution, 
    independent of sample number.
Contains functions to obtain the operational characteristics of 
    bioequivalence studies in Two-Stage Designs (TSD) via simulations.
The first goal of this package is to provide a multitude of tree models,
    i.e., functions that generate rooted binary trees with a given number of leaves.
    Second, the package allows for an easy evaluation and comparison of tree shape
    statistics by estimating their power to differentiate between different tree models.
    Please note that this R package was developed alongside the manuscript
    "Tree balance in phylogenetic models" by
    S. J. Kersting, K. Wicke, and M. Fischer (2024) <doi:10.48550/arXiv.2406.05185>,
    which provides further background and the respective mathematical definitions.
    This project was supported by the project ArtIGROW, which is a part of the
    WIR!-Alliance ArtIFARM –  Artificial Intelligence in Farming funded by the
    German Federal Ministry of Education and Research (No. 03WIR4805).
Makes it easy to push data to 'Power BI' using R and the 'Power BI 
    REST APIs' (see <https://docs.microsoft.com/en-us/rest/api/power-bi/>). 
    A set of functions for turning data frames into 'Power BI' datasets and 
    refreshing these datasets are provided. Administrative tasks such as 
    monitoring refresh statuses and pulling metadata about workspaces and users
    are also supported.
Functions for bootstrapping the power of ANOVA designs
    based on estimated means and standard deviations of the conditions.
    Please refer to the documentation of the boot.power.anova() function
    for further details.
A power analysis tool for jointly testing the cause-1 cause-specific hazard and the any-cause hazard with competing risks data.
Power and sample size calculation for bulk tissue and single-cell eQTL analysis
             based on ANOVA, simple linear regression, or linear mixed effects model. It can also calculate power/sample size 
             for testing the association of a SNP to a continuous type phenotype.
             Please see the reference: Dong X, Li X, Chang T-W, Scherzer CR, Weiss ST, Qiu W. (2021) <doi:10.1093/bioinformatics/btab385>.
Power estimation and sample size calculation for 10X Visium Spatial Transcriptomics data to detect differential expressed genes between two conditions based on bootstrap resampling. See Shui et al. (2024) <doi:10.1101/2024.08.30.610564> for method details.
Analytical power calculations for GxE and GxG interactions for case-control studies of candidate genes and genome-wide association studies (GWAS). This includes power calculation for four two-step screening and testing procedures.  It can also calculate power for GxE and GxG without any screening.
Calculate sample size or power for hierarchical endpoints. 
  The package can handle any type of outcomes (binary, continuous, count, ordinal, 
  time-to-event) and any number of such endpoints. It allows users to calculate 
  sample size with a given power or to calculate power with a given sample size 
  for hypothesis testing based on win ratios, win odds, net benefit, or DOOR 
  (desirability of outcome ranking) as treatment effect between two groups for 
  hierarchical endpoints. The methods of this package are described further in 
  the paper by Barnhart, H. X. et al. (2024, <doi:10.1080/19466315.2024.2365629>).
This R package allows the determination of some distributions of the voters' power when passing laws in weighted voting situations.
We extend 'dplyr' and 'fuzzyjoin' join functions with
    features to preprocess the data, apply various data checks, and deal with
    conflicting columns.
An implementation of the generalized power analysis for the local average treatment effect (LATE), proposed by Bansak (2020) <doi:10.1214/19-STS732>. Power analysis is in the context of estimating the LATE (also known as the complier average causal effect, or CACE), with calculations based on a test of the null hypothesis that the LATE equals 0 with a two-sided alternative. The method uses standardized effect sizes to place a conservative bound on the power under minimal assumptions. Package allows users to recover power, sample size requirements, or minimum detectable effect sizes. Package also allows users to work with absolute effects rather than effect sizes, to specify an additional assumption to narrow the bounds, and to incorporate covariate adjustment.
An implementation of maximum likelihood estimators for a
    variety of heavy tailed distributions, including both the discrete and
    continuous power law distributions. Additionally, a goodness-of-fit
    based approach is used to estimate the lower cut-off for the scaling
    region.
An implementation of the sample size computation method for network
    models proposed by Constantin et al. (2021) <doi:10.31234/osf.io/j5v7u>.
    The implementation takes the form of a three-step recursive algorithm
    designed to find an optimal sample size given a model specification and a
    performance measure of interest. It starts with a Monte Carlo simulation
    step for computing the performance measure and a statistic at various sample
    sizes selected from an initial sample size range. It continues with a
    monotone curve-fitting step for interpolating the statistic across the entire
    sample size range. The final step employs stratified bootstrapping to quantify
    the uncertainty around the fitted curve.
Functions to 
        calculate power and sample size for testing
        (1) mediation effects; 
        (2) the slope in a simple linear regression; 
        (3) odds ratio in a simple logistic regression;
        (4) mean change for longitudinal study with 2 time points;
        (5) interaction effect in 2-way ANOVA; and
        (6) the slope in a simple Poisson regression.
Model-implied simulation-based power estimation (MSPE) for
    nonlinear (and linear) SEM, path analysis and regression analysis. A
    theoretical framework is used to approximate the relation between
    power and sample size for given type I error rates and effect sizes.
    The package offers an adaptive search algorithm to find the optimal N for
    given effect sizes and type I error rates. Plots can be used to visualize
    the power relation to N for different parameters of interest (POI). 
    Theoretical justifications are given in Irmer et al. 
    (2024a) <doi:10.31219/osf.io/pe5bj> and detailed description
    are given in Irmer et al. (2024b) <doi:10.3758/s13428-024-02476-3>.
Miscellaneous functions for a descriptive analysis and initial Bayesian and classical 
             inference for the power parameter of the the Power Normal (PN) distribution. This 
             miscellaneous will be extend for more distributions into the power family and the 
             three-parameter model.
There are two main functions: (1) To estimate the power of testing for linkage using an
        affected sib pair design, as a function of the recurrence risk
        ratios. We will use analytical power formulae as implemented in
        R. These are based on a Mathematica notebook created by Martin
        Farrall. (2) To examine how the power of the transmission
        disequilibrium test (TDT) depends on the disease allele
        frequency, the marker allele frequency, the strength of the
        linkage disequilibrium, and the magnitude of the genetic
        effect. We will use an R program that implements the power
        formulae of Abel and Muller-Myhsok (1998). These formulae allow
        one to quickly compute power of the TDT approach under a
        variety of different conditions. This R program was modeled on
        Martin Farrall's Mathematica notebook.
It estimates power and sample size for Partial Least Squares-based methods described in Andreella, et al., (2024), <doi:10.48550/arXiv.2403.10289>.
A set of functions designed to calculate the standardised
    precipitation and standardised precipitation evapotranspiration indices
    using NASA POWER data as described in Blain et al. (2023)
    <doi:10.2139/ssrn.4442843>.  These indices are calculated using a reference
    data source.  The functions verify if the indices' estimates meet the
    assumption of normality and how well NASA POWER estimates represent
    real-world data.  Indices are calculated in a routine mode.  Potential
    evapotranspiration amounts and the difference between rainfall and potential
    evapotranspiration are also calculated.  The functions adopt a basic time
    scale that splits each month into four periods.  Days 1 to 7, days 8 to 14,
    days 15 to 21, and days 22 to 28, 29, 30, or 31, where 'TS=4' corresponds to
    a 1-month length moving window (calculated 4 times per month) and 'TS=48'
    corresponds to a 12-month length moving window (calculated 4 times per
    month).
Functions to calculate power and
                sample size for testing main effect or interaction effect in
                the survival analysis of epidemiological studies
                (non-randomized studies), taking into account the 
                correlation between the covariate of the
                interest and other covariates. Some calculations also take 
                into account the competing risks and stratified analysis. 
                This package also includes
                a set of functions to calculate power and sample size
                for testing main effect in the survival analysis of 
                randomized clinical trials and conditional logistic regression for nested case-control study.
Power and sample size calculations for a variety of study designs and 
    outcomes. Methods include t tests, ANOVA (including tests for interactions, 
    simple effects and contrasts), proportions, categorical data (chi-square tests 
    and proportional odds), linear, logistic and Poisson regression, alternative 
    and coprimary endpoints, power for confidence intervals, correlation 
    coefficient tests, cluster randomized trials, individually randomized group 
    treatment trials, multisite trials, treatment-by-covariate interaction effects 
    and nonparametric tests of location. Utilities are provided for 
    computing various effect sizes. Companion package to the book 
    "Power and Sample Size in R", Crespi (2025, ISBN:9781138591622).
Contains functions to calculate power and sample size for
    various study designs used in bioequivalence studies. Use known.designs() to
    see the designs supported. Power and sample size can be obtained based on
    different methods, amongst them prominently the TOST procedure (two one-sided
    t-tests). See README and NEWS for further information.
Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments (MRE) with continuous outcomes.
 Accomodates 14 types of MRE designs to detect main treatment effect, seven types of MRE designs to detect moderated treatment effect (2-1-1, 2-1-2, 2-2-1, 2-2-2, 3-3-1, 3-3-2, and 3-3-3 designs; <total.lev> - <trt.lev> - <mod.lev>),
 five types of MRE designs to detect mediated treatment effects (2-1-1, 2-2-1, 3-1-1, 3-2-1, and 3-3-1 designs; <trt.lev> - <med.lev> - <out.lev>), four types of partially nested (PN) design to detect main treatment effect,
 and three types of PN designs to detect mediated treatment effects (2/1, 3/1, 3/2; <trt.arm.lev> / <ctrl.arm.lev>). 
 See 'PowerUp!' Excel series at <https://www.causalevaluation.org/>.
Perform user-friendly power analyses for the random
    intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait 
    autoregressive trait state (STARTS) model. The strategy as proposed by 
    Mulder (2023) <doi:10.1080/10705511.2022.2122467> is implemented. Extensions 
    include the use of parameter constraints over time, bounded estimation,
    generation of data with skewness and kurtosis, and the option to setup the power 
    analysis for Mplus.
The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST)
    person parameters for the 1,2,3,4-PL model and the GPCM (generalized
    partial credit model). The parameters are estimated under the assumption
    that the item parameters are known and fixed. The package is useful e.g. in
    the case that items from an item pool / item bank with known item parameters
    are administered to a new population of test-takers and an ability
    estimation for every test-taker is needed.
Perform 1-dim/2-dim projection pursuit, grand tour and guided tour for big data based on data nuggets. 
    Reference papers: [1] Beavers et al., (2024) <doi:10.1080/10618600.2024.2341896>. 
    [2] Duan, Y., Cabrera, J., & Emir, B. (2023). "A New Projection Pursuit Index for Big Data." <doi:10.48550/arXiv.2312.06465>.
Performs partial principal component analysis of a large sparse matrix. 
  The matrix may be stored as a list of matrices to be concatenated (implicitly) horizontally.
  Useful application includes cases where the number of total nonzero entries exceed the capacity of 
  32 bit integers (e.g., with large Single Nucleotide Polymorphism data).
Calculates the Probability Plot Correlation Coefficient (PPCC) 
             between a continuous variable X and a specified distribution. The corresponding 
	     composite hypothesis test that was first introduced by 
	     Filliben (1975) <doi: 10.1080/00401706.1975.10489279> 
	     can be performed to test whether the sample 
	     X is element of either the Normal, log-Normal, Exponential,
	     Uniform, Cauchy, Logistic, Generalized Logistic, Gumbel (GEVI), Weibull,
	     Generalized Extreme Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh
	     or Generalized Logistic Distribution. The PPCC test is performed with
	     a fast Monte-Carlo simulation.
In the era of big data, data redundancy and distributed characteristics present novel challenges to data analysis. This package introduces a method for estimating optimal subsets of redundant distributed data, based on PPCDT (Conjunction of Power and P-value in Distributed Settings). Leveraging PPC technology, this approach can efficiently extract valuable information from redundant distributed data and determine the optimal subset. Experimental results demonstrate that this method not only enhances data quality and utilization efficiency but also assesses its performance effectively. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.
Implements recently developed projection 
    pursuit algorithms for finding optimal linear cluster
    separators. The clustering algorithms use optimal
    hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>;
    minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>;
    and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.
Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) <doi:10.1080/01969727308546047>, Possibilistic C-Means (Krishnapuram & Keller, 1993) <doi:10.1109/91.227387>, Possibilistic Fuzzy C-Means (Pal et al, 2005) <doi:10.1109/TFUZZ.2004.840099>, Possibilistic Clustering Algorithm (Yang et al, 2006) <doi:10.1016/j.patcog.2005.07.005>, Possibilistic C-Means with Repulsion (Wachs et al, 2006) <doi:10.1007/3-540-31662-0_6> and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package 'inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package.
Calculates partial and semi-partial
        (part) correlations along with p-value.
Spatial Analysis for exploration of Pakistan Population Census 2017 (<https://www.pbs.gov.pk/content/population-census>). It uses data from R package 'PakPC2017'.
A suite of diagnostic tools for univariate point processes.
    This includes tools for simulating and fitting both common and more
    complex temporal point processes. We also include functions to 
    visualise these point processes and collect existing diagnostic
    tools of Brown et al. (2002) <doi:10.1162/08997660252741149> and
    Wu et al. (2021) <doi:10.1002/9781119821588.ch7>,
    which can be used to assess the fit of a chosen point process
    model.
Introducing a novel and updated database showcasing Peru's endemic plants. This meticulously compiled and revised botanical collection encompasses a remarkable assemblage of over 7,249 distinct species. The data for this resource was sourced from the work of Govaerts, R., Nic Lughadha, E., Black, N. et al., titled 'The World Checklist of Vascular Plants: A continuously updated resource for exploring global plant diversity', published in Sci Data 8, 215 (2021) <doi:10.1038/s41597-021-00997-6>.
Implements projection pursuit forest algorithm for supervised classification.
Methods for fitting point processes with parameters of generalised additive 
      model (GAM) form are provided. For an introduction to point processes see Cox, D.R & 
      Isham, V. (Point Processes, 1980, CRC Press), GAMs see Wood, S.N. (2017) 
      <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & 
      Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.
Reconstruction of paleoclimate niches using phylogenetic comparative 
    methods and projection reconstructed niches onto paleoclimate maps.
    The user can specify various models of trait evolution or estimate the best fit
    model, include fossils, use one or multiple phylogenies for inference, and make
    animations of shifting suitable habitat through time. This model was first used
    in Lawing and Polly (2011), and further implemented in Lawing et al (2016) and 
    Rivera et al (2020).
    Lawing and Polly (2011) <doi:10.1371/journal.pone.0028554> "Pleistocene climate,
    phylogeny and climate envelope models: An integrative approach to better 
    understand species' response to climate change"
    Lawing et al (2016) <doi:10.1086/687202> "Including fossils in phylogenetic 
    climate reconstructions: A deep time perspective on the climatic niche evolution
    and diversification of spiny lizards (Sceloporus)"
    Rivera et al (2020) <doi:10.1111/jbi.13915> "Reconstructing historical shifts 
    in suitable habitat of Sceloporus lineages using phylogenetic niche modelling.".
Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) <doi:10.1080/10618600.2019.1598871>.
The Poverty Probability Index (PPI) is a poverty measurement tool 
    for organizations and businesses with a mission to serve the poor. The PPI 
    is statistically-sound, yet simple to use: the answers to 10 questions about 
    a household’s characteristics and asset ownership are scored to compute the 
    likelihood that the household is living below the poverty line – or above by 
    only a narrow margin. This package contains country-specific lookup data tables
    used as reference to determine the poverty likelihood of a household based
    on their score from the country-specific PPI questionnaire. These lookup 
    tables have been extracted from documentation of the PPI found at 
    <https://www.povertyindex.org> and managed by Innovations for Poverty Action 
    <https://poverty-action.org/>.
We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper: Zhu et al. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics. 2023 Jan 23;24(1):25.
Implements data processing described in <doi:10.1126/sciadv.abk3283> to align modern differentially private data with formatting of older US Census data releases. The primary goal is to read in Census Privacy Protected Microdata Files data in a reproducible way. This includes tools for aggregating to relevant levels of geography by creating geographic identifiers which match the US Census Bureau's numbering. Additionally, there are tools for grouping race numeric identifiers into categories, consistent with OMB (Office of Management and Budget) classifications. Functions exist for downloading and linking to existing sources of privacy protected microdata.
An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) <doi: 10.1177/0962280219869742>. This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented.
Implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) <doi:10.1007/s00362-023-01418-z>. Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in <arXiv:2303.04754>) and has been found to outperform several other commonly applied estimators.
Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) <doi:10.1111/j.1541-0420.2012.01824.x>. Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.
Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. 
    It can account for the correlated instruments and the horizontal pleiotropy, 
    and can provide the accurate estimates of both causal effect and horizontal 
    pleiotropy effect as well as the two corresponding p-values. There are two main 
    functions in the 'PPMR' package. One is PMR_individual() for individual level data, 
    the other is PMR_summary() for summary data.
Provides a suite of functions that fit models that use PPM type priors for partitions.
                Models include hierarchical Gaussian and probit ordinal models with  a (covariate 
                dependent) PPM.  If a covariate dependent product partition model is selected, 
                then all the options detailed in Page, G.L.; Quintana, F.A. (2018) 
                <doi:10.1007/s11222-017-9777-z> are available.  If covariate values are missing, 
                then the approach detailed in Page, G.L.; Quintana, F.A.; Mueller, P (2020) 
                <doi:10.1080/10618600.2021.1999824> is employed.   Also included in the package is 
                a function that fits a Gaussian likelihood spatial product  partition model that is 
                detailed in Page, G.L.; Quintana, F.A. (2016)  <doi:10.1214/15-BA971>, and 
                multivariate PPM change point models that are detailed in Quinlan, J.J.; Page, G.L.; 
                Castro, L.M. (2023) <doi:10.1214/22-BA1344>. In addition, a function that fits a 
                univariate or bivariate functional data model that employs a PPM or a PPMx to 
                cluster curves based on B-spline coefficients is provided.
Assessment for statistically-based PPQ sampling plan, including calculating the passing probability, optimizing the baseline and high performance cutoff points, visualizing the PPQ plan and power dynamically. The analytical idea is based on the simulation methods from the textbook Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Methods for CMC Applications. In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp. 227-250). Springer, Cham.
Provides functionality for Bayesian analysis of replication studies using power prior approaches (Pawel et al., 2023) <doi:10.1007/s11749-023-00888-5>.
A toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the 'Merge ToolBox' (<https://www.record-linkage.de>) with current privacy-preserving techniques.
Implements linear and generalized linear models for provider profiling,
    incorporating both fixed and random effects. For large-scale providers, the linear 
    profiled-based method and the SerBIN method for binary data 
    reduce the computational burden. Provides post-modeling features, such as 
    indirect and direct standardization measures, hypothesis testing, 
    confidence intervals, and post-estimation visualization. 
    For more information, see Wu et al. (2022) <doi:10.1002/sim.9387>.
Functions to select samples using PPS (probability proportional to size) sampling. The package also includes a function for stratified simple random sampling, a function to compute joint inclusion probabilities for Sampford's method of PPS sampling, and a few utility functions. The user's guide pps-ug.pdf is included in the .../pps/doc directory. The methods are described in standard survey sampling theory books such as Cochran's "Sampling Techniques"; see the user's guide for references.
Stochastic block model used for dynamic graphs represented by Poisson processes.
    To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion.
    Y. Baraud and L. Birgé (2009). <doi:10.1007/s00440-007-0126-6>.
    C. Biernacki, G. Celeux and G. Govaert (2000). <doi:10.1109/34.865189>.
    M. Corneli, P. Latouche and F. Rossi (2016). <doi:10.1016/j.neucom.2016.02.031>.
    J.-J. Daudin, F. Picard and S. Robin (2008). <doi:10.1007/s11222-007-9046-7>.
    A. P. Dempster, N. M. Laird and D. B. Rubin (1977). <http://www.jstor.org/stable/2984875>.
    G. Grégoire (1993). <http://www.jstor.org/stable/4616289>.
    L. Hubert and P. Arabie (1985). <doi:10.1007/BF01908075>.
    M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). <doi:10.1023/A:1007665907178>.
    C. Matias, T. Rebafka and F. Villers (2018). <doi:10.1093/biomet/asy016>.
    C. Matias and S. Robin (2014). <doi:10.1051/proc/201447004>.
    H. Ramlau-Hansen (1983). <doi:10.1214/aos/1176346152>.
    P. Reynaud-Bouret (2006). <doi:10.3150/bj/1155735930>.
Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.
This is an implementation of the partial profile score feature 
    selection (PPSFS) approach to generalized linear (interaction) models. 
    The PPSFS is highly scalable even for ultra-high-dimensional feature space. 
    See the paper by Xu, Luo and Chen (2021, <doi:10.4310/21-SII706>).
The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score 
    that can detect linear or non-linear relationships between two variables. 
    The score ranges from 0 (no predictive power) to 1 (perfect predictive power). 
    PPS can be useful for data exploration purposes, in the same way correlation analysis is.
    For more information on PPS, see <https://github.com/paulvanderlaken/ppsr>.
Provides functionality for the prior and posterior projected Polya tree for the analysis of circular data 
  (Nieto-Barajas and Nunez-Antonio (2019) <arXiv:1902.06020>).
Tools for exploring projection pursuit classification tree using
    various projection pursuit indexes.
This package allows users to calculate the number of
    under-five child deaths caused by consumption of poor quality
    antimalarials across 39 sub-Saharan nations. The package supports one
    function, that starts an interactive web tool created using
    the shiny R package. The web tool runs locally on the user's machine.
    The web tool allows users to set input parameters (prevalence of poor
    quality antimalarials, case fatality rate of children who take poor
    quality antimalarials, and sample size) which are then used to perform
    an uncertainty analysis following the Latin hypercube
    sampling scheme. Users can download the output figures as PDFs, and the
    output data as CSVs. Users can also download their input parameters
    for reference. This package was designed to accompany the analysis
    presented in:
    J. Patrick Renschler, Kelsey Walters, Paul Newton, Ramanan Laxminarayan
    "Estimated under-five deaths associated with poor-quality
    antimalarials in sub-Saharan Africa", 2014. Paper submitted.
In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.
An efficient tool designed for differential analysis of large-scale RNA sequencing (RNAseq) data and Bisulfite sequencing (BSseq) data in the presence of individual relatedness and population structure. 'PQLseq' first fits a Generalized Linear Mixed Model (GLMM) with adjusted covariates, predictor of interest and random effects to account for population structure and individual relatedness, and then performs Wald tests for each gene in RNAseq or site in BSseq.
Bayesian regularized quantile regression utilizing sparse priors to 
    impose exact sparsity leads to efficient Bayesian shrinkage estimation, variable 
    selection and statistical inference. In this package, we have implemented robust
    Bayesian variable selection with spike-and-slab priors under high-dimensional
    linear regression models (Fan et al. (2024) <doi:10.3390/e26090794> and  
    Ren et al. (2023) <doi:10.1111/biom.13670>), and regularized quantile varying
    coefficient models (Zhou et al.(2023) <doi:10.1016/j.csda.2023.107808>). In particular, 
    valid robust Bayesian inferences under both models in the presence of heavy-tailed errors
    can be validated on finite samples. The Markov Chain Monte Carlo (MCMC) algorithms 
    of the proposed and alternative models are implemented in C++.
Quantile regression with fixed effects is a general model for longitudinal data. Here we proposed to solve it by several methods. The estimation methods include three loss functions as check, asymmetric least square and asymmetric Huber functions; and three structures as simple regression, fixed effects and fixed effects with penalized intercepts by LASSO.
Data analysis for Project Risk Management via the Second Moment Method, 
  Monte Carlo Simulation, Contingency Analysis, Sensitivity Analysis, Earned Value Management, 
  Learning Curves, Design Structure Matrices, and more.
Quickly and easily generate plots of acoustic data aligned with transcriptions similar to those made in 'Praat' using either derived signals generated directly in R with 'wrassp' or imported derived signals from 'Praat'. Provides easy and fast out-of-the-box solutions but also a high extent of  flexibility. Also provides options for embedding audio in figures and animating figures.
Distance-based parametric bootstrap tests for clustering with 
  spatial neighborhood information. Some distance measures, 
  Clustering of presence-absence, abundance and multilocus genetic data 
  for species delimitation, nearest neighbor 
  based noise detection. Genetic distances between communities.
  Tests whether various distance-based regressions
  are equal. Try package?prabclus for on overview.
Provides a large number of functions from numerical analysis and
    linear algebra, numerical optimization, differential equations,
    time series, plus some well-known special mathematical functions.
    Uses 'MATLAB' function names where appropriate to simplify porting.
Streamline the creation of 'Docker' images with 'R' packages and dependencies embedded. The 'pracpac' package provides a 'usethis'-like interface to creating Dockerfiles with dependencies managed by 'renv'. The 'pracpac' functionality is described in Nagraj and Turner (2023) <doi:10.48550/arXiv.2303.07876>.
Sample size calculations for practical equivalence trial design with a time to event endpoint.
Consider a possibly nonlinear nonparametric regression
   with p regressors. We provide evaluations by 13 methods to rank
   regressors by their practical significance or importance using 
   various methods, including machine learning tools. Comprehensive
   methods are as follows. 
   m6=Generalized partial correlation coefficient or
   GPCC by Vinod (2021)<doi:10.1007/s10614-021-10190-x> and
   Vinod (2022)<https://www.mdpi.com/1911-8074/15/1/32>.
   m7= a generalization of psychologists' effect size incorporating 
   nonlinearity and many variables.
   m8= local linear partial (dy/dxi) using the 'np' package for kernel 
   regressions.
   m9= partial (dy/dxi) using the 'NNS' package.
   m10= importance measure using the 'NNS' boost function.
   m11= Shapley Value measure of importance (cooperative game theory).
   m12 and m13= two versions of the random forest algorithm.
   Taraldsen's exact density for sampling distribution of correlations added.
Functions and datasets to support Valliant, Dever, and Kreuter (2018), <doi:10.1007/978-3-319-93632-1>, "Practical Tools for Designing and Weighting Survey Samples". Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs, and single-stage audit sample designs. Functions are included that will group geographic units accounting for distances apart and measures of size. Other functions compute variance components for multistage designs and sample sizes in two-phase designs. A number of example data sets are included.
The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained.
Build friendly R packages that
    praise their users if they have done something
    good, or they just need it to feel better.
A novel pseudo-value regression approach for the differential co-expression network analysis in expression data, which can incorporate additional clinical variables in the model. This is a direct regression modeling for the differential network analysis, and it is therefore computationally amenable for the most users. The full methodological details can be found in Ahn S et al (2023) <doi:10.1186/s12859-022-05123-w>.
A toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <https://www.jmlr.org/papers/v13/brown12a.html>.
 Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <https://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.
A testing workbench to evaluate tools that calculate precision-recall curves.
    Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit. In this package, we provide two algorithms for fitting the penalized regression-based clustering (PRclust) with non-convex grouping penalties, such as group truncated lasso, MCP and SCAD. One algorithm is based on quadratic penalty and difference convex method. Another algorithm is based on difference convex and ADMM, called DC-ADD, which is more efficient. Generalized cross validation and stability based method were provided to select the tuning parameters. Rand index, adjusted Rand index and Jaccard index were provided to estimate the agreement between estimated cluster memberships and the truth.
Provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step clustering procedure. As described in Bergman and El-Khouri (1999) <DOI:10.1002/(SICI)1521-4036(199910)41:6%3C753::AID-BIMJ753%3E3.0.CO;2-K>, hierarchical clustering is performed to determine the initial partition for the subsequent k-means clustering procedure.
An implementation of the "Design Analysis" proposed by 
    Gelman and Carlin (2014) <doi:10.1177/1745691614551642>. It combines 
    the evaluation of Power-Analysis with other inferential-risks as 
    Type-M error (i.e. Magnitude) and Type-S error (i.e. Sign). See also
    Altoè et al. (2020) <doi:10.3389/fpsyg.2019.02893> and 
    Bertoldo et al. (2020) <doi:10.31234/osf.io/q9f86>.
Derives prediction rule ensembles (PREs). Largely follows the
    procedure for deriving PREs as described in Friedman & Popescu (2008; 
    <DOI:10.1214/07-AOAS148>), with adjustments and improvements. The 
    main function pre() derives prediction rule ensembles consisting of 
    rules and/or linear terms for continuous, binary, count, multinomial, 
    and multivariate continuous responses. Function gpe() derives 
    generalized prediction ensembles, consisting of rules, hinge and linear 
    functions of the predictor variables.
An efficient data integration method is provided for multiple spatial transcriptomics data with non-cluster-relevant effects such as the complex batch effects. It unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, requiring only partially shared cell/domain clusters across datasets. More details can be referred to Wei Liu, et al. (2023) <doi:10.1038/s41467-023-35947-w>.
It contains functions to analyze the precipitation
    intensity, concentration and anomaly.
An open-access tool/framework to download, validate, visualize, and
  analyze multi-source precipitation data. More information and an example of
  implementation can be found in Vargas Godoy and Markonis (2023,
  <doi:10.1016/j.envsoft.2023.105711>).
Estimate sample size based on precision rather than power.
    'precisely' is a study planning tool to calculate sample size based on
    precision. Power calculations are focused on whether or not an
    estimate will be statistically significant; calculations of precision
    are based on the same principles as power calculation but turn the
    focus to the width of the confidence interval. 'precisely' is based on
    the work of 'Rothman and Greenland' (2018).
Provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par().
Most of the time floating point arithmetic does
  approximately the right thing.  When adding sums or having products
  of numbers that greatly differ in magnitude, the floating point
  arithmetic may be incorrect.  This package implements the Kahan
  (1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum
  <doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from 'NumPy',
  See Castaldo (2008) <doi:10.1137/070679946> for a discussion of
  accuracy), and arbitrary precision sum (adapted from the fsum in
  'Python' ; Shewchuk (1997)
  <https://people.eecs.berkeley.edu/~jrs/papers/robustr.pdf>).  In addition,
  products are changed to long double precision for accuracy, or
  changed into a log-sum for accuracy.
A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.
Useful git hooks for R building on top of the multi-language
    framework 'pre-commit' for hook management. This package provides git
    hooks for common tasks like formatting files with 'styler' or spell
    checking as well as wrapper functions to access the 'pre-commit'
    executable.
Implements fast, safe, and customizable assertions routines, which 
    can be used in place of base::stopifnot().
Accurate calculations and visualization of precision-recall and ROC (Receiver Operator Characteristics)
    curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
A computational model for predicting proteins encoded by circadian genes. The support vector machine has been employed with Laplace kernel for prediction of circadian proteins, where compositional, transitional and physico-chemical features were utilized as numeric features. User can predict for the test dataset using the proposed computational model. Besides, the user can also build their own training model using their training dataset, followed by prediction for the test set.
Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. 
  This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package
  is described by Kamiran, Karim and Zhang (2012) <https://ieeexplore.ieee.org/document/6413831/>.
Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, LightGBM and XGBoost. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).
Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM.
             GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine.
             It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).
Draw 2 dimensional and three dimensional plot for multiple regression models using package 'ggplot2' and 'rgl'.
   Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess).
We included functions to assess the performance of
 risk models. The package contains functions for the various measures that are
 used in empirical studies, including univariate and multivariate odds ratios
 (OR) of the predictors, the c-statistic (or area under the receiver operating
 characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test,
 reclassification table, net reclassification improvement (NRI) and
 integrated discrimination improvement (IDI). Also included are functions
 to create plots, such as risk distributions, ROC curves, calibration plot,
 discrimination box plot and predictiveness curves. In addition to functions
 to assess the performance of risk models, the package includes functions to
 obtain weighted and unweighted risk scores as well as predicted risks using
 logistic regression analysis. These logistic regression functions are
 specifically written for models that include genetic variables, but they
 can also be applied to models that are based on non-genetic risk factors only.
 Finally, the package includes function to construct a simulated dataset with 
 genotypes, genetic risks, and disease status for a hypothetical population, which 
 is used for the evaluation of genetic risk models.
A one-function package containing prediction(), a type-safe alternative to predict() that always returns a data frame. The summary() method provides a data frame with average predictions, possibly over counterfactual versions of the data (à la the margins command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' <https://cran.r-project.org/package=margins>. The package currently supports common model types (e.g., lm, glm) from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README file or main package documentation page for a complete listing.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a prediction interval). This package has functions that calculate
     the prediction interval for the correlation (i.e., r),
     standardized mean difference (i.e., d-value), and mean.
Functions to get prediction intervals and prediction points of future observations from any continuous distribution.
Enables researchers to visualize the prediction performance of any algorithm on the individual level (or close to it), given that the predicted outcome is either binary or continuous. Visual results are instantly comprehensible.
Providing functions to diagnose and make inferences from various linear models, 
    such as those obtained from 'aov', 'lm', 'glm', 'gls', 'lme', 'lmer', 'glmmTMB' and 'semireg'. 
	Inferences include predicted means and standard errors, contrasts, multiple comparisons, 
	permutation tests, adjusted R-square and graphs.
Estimates when and where a model-guided treatment strategy may 
    outperform a treat-all or treat-none approach by Monte Carlo simulation and 
    evaluation of the Net Monetary Benefit. Details can be viewed in 
    Parsons et al. (2023) <doi:10.21105/joss.05328>.
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.
Consider a linear predictive regression setting with a potentially large set of candidate predictors. This work is concerned with detecting the presence of out of sample predictability based on out of sample mean squared error comparisons given in Gonzalo and Pitarakis (2023) <doi:10.1016/j.ijforecast.2023.10.005>.
Predicts the most common race of a surname and based on U.S. Census
    data, and the most common first named based on U.S. Social Security Administration data.
Methods for spatial predictive modeling, especially for spatial distribution models. This includes algorithms for model fitting and prediction, as well as methods for model evaluation.
Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.
An implementation of prediction intervals for overdispersed count data,
            for overdispersed binomial data and for linear random effects models.
Implements the predictive k-means method for clustering observations, using a mixture of experts model to allow covariates to influence cluster centers. Motivated by air pollution epidemiology settings, where cluster membership needs to be predicted across space. Includes functions for predicting cluster membership using spatial splines and principal component analysis (PCA) scores using either multinomial logistic regression or support vector machines (SVMs). For method details see Keller et al. (2017) <doi:10.1214/16-AOAS992>.
Recent years have seen an increased interest in novel methods
    for analyzing quantitative data from experimental psychology. Currently, however, they lack an
    established and accessible software framework. Many existing implementations provide no guidelines,
    consisting of small code snippets, or sets of packages. In addition, the use of existing packages
    often requires advanced programming experience. 'PredPsych' is a user-friendly toolbox based on
    machine learning predictive algorithms. It comprises of multiple functionalities for multivariate
    analyses of quantitative behavioral data based on machine learning models.
An implementation of reliability estimation methods described in the paper (Bosnic, Z., & Kononenko, I. (2008) <doi:10.1007/s10489-007-0084-9>), which allows you to test the reliability of a single predicted instance made
             by your model and prediction function. It also allows you to make a correlation test to estimate which reliability estimate is the most 
             accurate for your model.
Evaluate the predictive performance of an existing (i.e. previously 
    developed) prediction/ prognostic model given relevant information about the 
    existing prediction model (e.g. coefficients) and a new dataset. Provides a 
    range of model updating methods that help tailor the existing model to the 
    new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques 
    to aggregate multiple existing prediction models on the new data are also 
    provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. 
    (2018) <doi:10.1002/sim.7586>).
Global hypothesis tests combine information across multiple endpoints to test a single hypothesis. The prediction test is a recently proposed global hypothesis test with good performance for small sample sizes and many endpoints of interest. The test is also flexible in the types and combinations of expected results across the individual endpoints. This package provides functions for data processing and calculation of the prediction test.
Provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.
Makes the time series prediction easier by automatizing this process
  using four main functions: prep(), modl(), pred() and postp(). Features different
  preprocessing methods to homogenize variance and to remove trend and seasonality.
  Also has the potential to bring together different predictive models to make comparatives.
  Features ARIMA and Data Mining Regression models (using caret).
Implements the Single Transferable Vote (STV) electoral system, with
    clear explanatory graphics. The core function stv() uses Meek's method, the purest
    expression of the simple principles of STV, but which requires electronic
    counting. It can handle votes expressing equal preferences for subsets of
    the candidates. A function stv.wig() implementing the Weighted Inclusive
    Gregory method, as used in Scottish council elections, is also provided,
    and with the same options, as described in the manual. The required vote
    data format is as an R list: a function pref.data() is provided to transform
    some commonly used data formats into this format.
    References for methodology:
    Hill, Wichmann and Woodall (1987) <doi:10.1093/comjnl/30.3.277>,
    Hill, David (2006) <https://www.votingmatters.org.uk/ISSUE22/I22P2.pdf>,
    Mollison, Denis (2023) <arXiv:2303.15310>,
    (see also the package manual pref_pkg_manual.pdf).
Allows users to derive multi-objective weights from pairwise comparisons, which
    research shows is more repeatable, transparent, and intuitive other techniques. These weights
    can be rank existing alternatives or to define a multi-objective utility function for optimization.
Design and analyze two-stage randomized trials with a continuous
    outcome measure. The package contains functions to compute the required 
    sample size needed to detect a given preference, treatment, and selection 
    effect; alternatively, the package contains functions that can report the 
    study power given a fixed sample size. Finally, analysis functions are 
    provided to test each effect using either summary data (i.e. means, 
    variances) or raw study data <doi:10.18637/jss.v094.c02>.
Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach. Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.
Dynamize headers or R code within 'Rmd' files to prevent proliferation of 'Rmd' files for similar reports. Add in external HTML document within 'rmarkdown' rendered HTML doc.
Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) <doi:10.18637/jss.v064.i07>.
A database containing the names 
  of the babies born in Quebec between 1980 and 2020.
Prepares data for statistical analysis (e.g., analysis of variance
  ;ANOVA) by enabling the user to easily and quickly merge (using the
  file_merge() function) raw data files into one merged table and then
  aggregate the merged table (using the prep() function) into a finalized
  table while keeping track and summarizing every step of the preparation.
  The finalized table contains several possibilities for dependent measures of
  the dependent variable. Most suitable when measuring variables in an
  interval or ratio scale (e.g., reaction-times) and/or discrete values such
  as accuracy. Main functions included are file_merge() and prep(). The
  file_merge() function vertically merges individual data files (in a long
  format) in which each line is a single observation to one single dataset.
  The prep() function aggregates the single dataset according to any
  combination of grouping variables (i.e., between-subjects and
  within-subjects independent variables, respectively), and returns a data
  frame with a number of dependent measures for further analysis for each cell
  according to the combination of provided grouping variables. Dependent
  measures for each cell include among others means before and after rejecting
  all values according to a flexible standard deviation criteria, number of
  rejected values according to the flexible standard deviation criteria,
  proportions of rejected values according to the flexible standard deviation
  criteria, number of values before rejection, means after rejecting values
  according to procedures described in Van Selst & Jolicoeur (1994; suitable
  when measuring reaction-times), standard deviations, medians, means according
  to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data
  frame prep() returns can also be exported as a txt file to be used for
  statistical analysis in other statistical programs.
Early generation breeding trials are to be conducted in multiple 
             environments where it may not be possible to replicate all the 
             lines in each environment due to scarcity of resources. For such 
             situations, partially replicated (p-Rep) designs have wide 
             application potential as only a proportion of the test lines are 
             replicated at each environment. A collection of several utility 
             functions related to p-Rep designs have been developed. Here, the 
             package contains six functions for a complete stepwise analytical 
             study of these designs. Five functions pRep1(), pRep2(), pRep3(), 
             pRep4() and pRep5(), are used to generate five new series of p-Rep 
             designs and also compute average variance factors and canonical 
             efficiency factors of generated designs. A fourth function NCEV() 
             is used to generate incidence matrix (N), information matrix (C), 
             canonical efficiency factor (E) and average variance factor (V). 
             This function is general in nature and can be used for studying the 
             characterization properties of any block design. A construction 
             procedure for p-Rep designs was given by Williams et al.(2011) 
             <doi:10.1002/bimj.201000102> which was tedious and time consuming. 
             Here, in this package, five different methods have been given to 
             generate p-Rep designs easily.
A figure region is prepared, creating a plot region with suitable background color, grid lines or shadings, and providing axes and labeling if not suppressed. Subsequently, information carrying graphics elements can be added (points, lines, barplot with add=TRUE and so forth).
Provides classes to pre-process microarray gene
  expression data as part of the OOMPA collection of packages
  described at <http://oompa.r-forge.r-project.org/>.
Preprocess numeric data matrices into desired transformed representations.
    Standardization, Unitization, Cubitization and adaptive intervals are offered.
In this record linkage package, data preprocessing has been meticulously executed
  to cover a wide range of datasets, ensuring that variable names are standardized using synonyms. 
  This approach facilitates seamless data integration and analysis across various datasets. While users 
  have the flexibility to modify variable names, the system intelligently ensures that 
  changes are only permitted when they do not compromise data consistency or essential variable essence.
An interactive document for preprocessing the dataset using  'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/PREPShiny/>.
Miscellaneous small utilities are provided to mitigate issues with messy, inconsistent or high dimensional data and help for preprocessing and preparing analyses.
Provides a collection of templates to author preregistration documents for scientific studies in PDF format.
Preregistrations, or more generally, registrations, enable
    explicit timestamped and (often but not necessarily publicly) frozen
    documentation of plans and expectations as well as decisions and
    justifications. In research, preregistrations are commonly used to
    clearly document plans and facilitate justifications of deviations from
    those plans, as well as decreasing the effects of publication bias by
    enabling identification of research that was conducted but not published.
    Like reporting guidelines, (pre)registration forms often have specific
    structures that facilitate systematic reporting of important items. The
    'preregr' package facilitates specifying (pre)registrations in R and
    exporting them to a human-readable format (using R Markdown partials or
    exporting to an 'HTML' file) as well as human-readable embedded data
    (using 'JSON'), as well as importing such exported (pre)registration
    specifications from such embedded 'JSON'.
Provides a set of functions useful when evaluating the results of presence-absence models. Package includes functions for calculating threshold dependent measures such as confusion matrices, pcc, sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It will calculate optimal threshold choice according to a choice of optimization criteria. It also includes functions to plot the threshold independent ROC curves along with the associated AUC (area under the curve).
Makes output files from select PreSens Fiber Optic Oxygen
    Transmitters easier to work with in R. See <http://www.presens.de> for more
    information about PreSens (Precision Sensing GmbH). Note: this package is
    neither created nor maintained by PreSens.
Consists of custom wrapper functions using packages
    'openxlsx', 'flextable', and 'officer' to create highly formatted MS office friendly output of your data frames.
    These viewer friendly outputs are intended to match expectations of professional looking presentations
    in business and consulting scenarios. The functions are opinionated in the sense that they expect the input data
    frame to have certain properties in order to take advantage of the automated formatting.
Compilation and digitalization of the official registry of victims of state terrorism in Argentina during the last
             military coup. The original data comes from RUVTE-ILID (2019) <https://www.argentina.gob.ar/sitiosdememoria/ruvte/informe> and <http://basededatos.parquedelamemoria.org.ar/registros/>. The title, presentes, comes from present in spanish.
Originally as an R version of Preseq <doi:10.1038/nmeth.2375>, the package has extended its functionality to predict the r-species accumulation curve (r-SAC), which is the number of species represented at least r times as a function of the sampling effort. When r = 1, the curve is known as the species accumulation curve, or the library complexity curve in high-throughput genomic sequencing. The package includes both parametric and nonparametric methods, as described by Deng C, et al. (2018) <arXiv:1607.02804v3>.
Computes probability-scale residuals and residual correlations
    for continuous, ordinal, binary, count, and time-to-event data <doi:10.18637/jss.v094.i12>.
Bland (2009) <doi:10.1136/bmj.b3985> recommended to
    base study sizes on the width of the confidence interval rather the power of 
    a statistical test. The goal of 'presize' is to provide functions for such 
    precision based sample size calculations. For a given sample size, the 
    functions will return the precision (width of the confidence interval), and 
    vice versa.
Multi-state models are essential tools in longitudinal data analysis. 
             One primary goal of these models is the estimation of transition 
             probabilities, a critical metric for predicting clinical prognosis 
             across various stages of diseases or medical conditions. Traditionally, 
             inference in multi-state models relies on the Aalen-Johansen (AJ) 
             estimator which is consistent under the Markov assumption. However, 
             in many practical applications, the Markovian nature of the process
             is often not guaranteed, limiting the applicability of the AJ
             estimator in more complex scenarios. This package extends the landmark Aalen-Johansen
             estimator (Putter, H, Spitoni, C (2018) <doi:10.1177/0962280216674497>)
             incorporating presmoothing techniques described by Soutinho, 
             Meira-Machado and Oliveira (2020) <doi:10.1080/03610918.2020.1762895>, 
             offering a robust alternative for estimating transition probabilities
             in non-Markovian multi-state models with multiple states and potential
             reversible transitions.
Provides a function for estimating the transition probabilities in an illness-death model. 
            The transition probabilities can be estimated from the unsmoothed landmark estimators developed 
            by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>.
            Presmoothed estimates can also be obtained through the use of a parametric family of binary
            regression curves, such as logit, probit or cauchit. The additive logistic regression model 
            and nonparametric regression are also alternatives which have been implemented.
            The idea behind the presmoothed landmark estimators is to use the presmoothing techniques 
            developed by  Cao et al. (2005) <doi:10.1007/s00180-007-0076-6> in the 
            landmark estimation of the transition probabilities.
This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) <doi:10.1007/s00285-017-1163-0>.
Allows biomechanical pressure data from a range of systems to be imported and processed in a reproducible manner. Automatic and manual tools are included to let the user define regions (masks) to be analyzed. Also includes functions for visualizing and animating pressure data. Example methods are described in Shi et al., (2022) <doi:10.1038/s41598-022-19814-0>, Lee et al., (2014) <doi:10.1186/1757-1146-7-18>, van der Zward et al., (2014) <doi:10.1186/1757-1146-7-20>, Najafi et al., (2010) <doi:10.1016/j.gaitpost.2009.09.003>, Cavanagh and Rodgers (1987) <doi:10.1016/0021-9290(87)90255-7>.
This repository contains the codes for using the predictive accuracy comparison tests developed in Pitarakis, J. (2023) <doi:10.1017/S0266466623000154>.
Provides 'RStudio' addins to prettify 'HTML', 'CSS', 'SCSS',
    'JavaScript', 'JSX', 'Markdown', 'C(++)', 'LaTeX', 'Python', 'Julia',
    'XML', 'Java', 'JSON', 'Ruby', and to reindent 'C(++)', 'Fortran',
    'Java', 'Julia', 'Python', 'SAS', 'Scala', 'Shell', 'SQL' and
    "TypeScript". Two kinds of addins are provided: 'Prettify' and
    'Indent'. The 'Indent' addins only reindent the code, while the
    'Prettify' addins also modify the code, e.g. trailing semi-colons are
    added to 'JavaScript' code when they are missing.
Drop-in replacements for standard base graphics
    functions. The replacements are prettier versions of the originals.
Replace the standard print method for functions with one that
    performs syntax highlighting, using ANSI colors, if the terminal
    supports them.
Defines aesthetically pleasing colour palettes.
Creating tiny yet beautiful documents and vignettes from R
    Markdown. The package provides the 'html_pretty' output format as an
    alternative to the 'html_document' and 'html_vignette' engines that
    convert R Markdown into HTML pages. Various themes and syntax highlight
    styles are supported.
One of the main advantages of using Generalised Linear Models
    is their interpretability.  The goal of 'prettyglm' is to provide a
    set of functions which easily create beautiful coefficient summaries
    which can readily be shared and explained. 'prettyglm' helps users
    create coefficient summaries which include categorical base levels,
    variable importance and type III p.values. 'prettyglm' also creates
    beautiful relativity plots for categorical, continuous and splined
    coefficients.
Simple and crisp publication-quality graphics for the ExPosition family of packages.
  See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.
Automates the process of creating a scale bar and north arrow in
    any package that uses base graphics to plot in R. Bounding box tools help find
    and manipulate extents. Finally, there is a function to automate the process
    of setting margins, plotting the map, scale bar, and north arrow, and resetting
    graphic parameters upon completion.
Functions for conventionally formatting descriptive stats,
 reshaping data frames and formatting R output as HTML.
Pretty, human readable formatting of quantities.
    Time intervals: '1337000' -> '15d 11h 23m 20s'.
    Vague time intervals: '2674000' -> 'about a month ago'.
    Bytes: '1337' -> '1.34 kB'.
    Rounding: '99' with 3 significant digits -> '99.0'
    p-values: '0.00001' -> '<0.0001'.
    Colors: '#FF0000' -> 'red'.
    Quantities: '1239437' -> '1.24 M'.
The prevalence package provides Frequentist and Bayesian methods for prevalence assessment studies. IMPORTANT: the truePrev functions in the prevalence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user's system. JAGS can be downloaded from <https://mcmc-jags.sourceforge.io/>.
Easy and efficient access to the API provided by 'Prevedere', 
  an industry insights and predictive analytics company. Query and 
  download indicators, models and workbenches built with 'Prevedere' for further 
  analysis and reporting <https://www.prevedere.com/>.
Implements the American Heart Association Predicting
    Risk of cardiovascular disease EVENTs (PREVENT) equations from Khan
    SS, Matsushita K, Sang Y, and colleagues (2023)
    <doi:10.1161/CIRCULATIONAHA.123.067626>, with optional comparison 
    with their de facto predecessor, the Pooled Cohort Equations from the 
	  American Heart Association and American College of Cardiology (2013)
	  <doi:10.1161/01.cir.0000437741.48606.98> and the revision to the Pooled 
	  Cohort Equations from Yadlowsky and colleagues (2018) 
	  <doi:10.7326/M17-3011>.
For working with the 'Prevision.io' AI model management platform's API <https://prevision.io/>.
Provides functions for both likelihood-based
    and Bayesian analysis of spatially referenced prevalence data. For a tutorial on the use of the R package, see Giorgi and Diggle (2017) <doi:10.18637/jss.v078.i08>.
Spatial estimation of a prevalence surface
    or a relative risks surface, using data from a Demographic and Health
    Survey (DHS) or an analog survey, see Larmarange et al. (2011)
    <doi:10.4000/cybergeo.24606>.
Functions to simulate point prevalence studies (PPSs) of healthcare-associated infections (HAIs) and to convert prevalence to incidence in steady state setups. 
    Companion package to the preprint Willrich et al., From prevalence to incidence - a new approach in the hospital setting;
    <doi:10.1101/554725> , where methods are explained in detail.
Estimate False Discovery Rates (FDRs) for importance metrics from
    random forest runs.
Preparing a scanner data set for price dynamics calculations (data selecting, data classification, data matching, data filtering). Computing bilateral and multilateral indexes. For details on these methods see: Diewert and Fox (2020) 
    <doi:10.1080/07350015.2020.1816176>, Białek (2019) <doi:10.2478/jos-2019-0014> or Białek (2020) <doi:10.2478/jos-2020-0037>.
Price comparisons within or between countries provide an overall measure of the relative difference in prices, often denoted as price levels. This package provides index number methods for such price comparisons (e.g., The World Bank, 2011, <doi:10.1596/978-0-8213-9728-2>). Moreover, it contains functions for sampling and characterizing price data.
Functions to aid in micro and macro economic analysis and handling of price and
    currency data. Includes extraction of relevant inflation and exchange rate data from World Bank
    API, data cleaning/parsing, and standardisation. Inflation adjustment
    calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current
    and historical end of day exchange rates for 171 currencies from the European Central Bank
    Statistical Data Warehouse (2020) <https://sdw.ecb.europa.eu/curConverter.do>.
An implementation of the van Westendorp Price
    Sensitivity Meter in R, which is a survey-based approach
	to analyze consumer price preferences and sensitivity
    (van Westendorp 1976, isbn:9789283100386).
Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.
Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.
Provides functions for working with primary
    event censored distributions and 'Stan' implementations for use in Bayesian
    modeling. Primary event censored distributions are useful for modeling
    delayed reporting scenarios in epidemiology and other fields (Charniga et
    al. (2024) <doi:10.48550/arXiv.2405.08841>). It also provides support for
    arbitrary delay distributions, a range of common primary distributions, and
    allows for truncation and secondary event censoring to be accounted for
    (Park et al. (2024) <doi:10.1101/2024.01.12.24301247>). A subset of
    common distributions also have analytical solutions implemented, allowing
    for faster computation. In addition, it provides multiple methods for
    fitting primary event censored distributions to data via optional
    dependencies.
Data from All the World's Primates relational SQL database and other tabular datasets are made available via drivers and connection functions. Additionally we provide several functions and examples to facilitate the merging and aggregation of these tabular inputs.
Use Prime Factorization for simplifying computations,
    for instance for ratios of large factorials.
Implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. <arXiv:1906.12125>.
Perform sample size, power calculation and subsequent analysis for Immuno-oncology (IO) trials composed of responders and non-responders.
Functions are primarily functions for systems of ordinary differential equations, difference equations, and eigenanalysis and projection of demographic matrices; data are for examples.
Identifies potential target sequences for a given
    set of primers and generates phylogenetic trees annotated with the
    taxonomies of the predicted amplification products.
Fast functions for dealing with prime numbers, such as testing
    whether a number is prime and generating a sequence prime numbers.
    Additional functions include finding prime factors and Ruth-Aaron pairs,
    finding next and previous prime numbers in the series, finding or estimating
    the nth prime, estimating the number of primes less than or equal to an
    arbitrary number, computing primorials, prime k-tuples (e.g., twin primes),
    finding the greatest common divisor and smallest (least) common multiple,
    testing whether two numbers are coprime, and computing Euler's totient
    function. Most functions are vectorized for speed and convenience.
R interface to 'PRIMME' <https://www.cs.wm.edu/~andreas/software/>, a C library for computing a few
    eigenvalues and their corresponding eigenvectors of a real symmetric or complex
    Hermitian matrix, or generalized Hermitian eigenproblem.  It can also compute
    singular values and vectors of a square or rectangular matrix. 'PRIMME' finds
    largest, smallest, or interior singular/eigenvalues and can use preconditioning
    to accelerate convergence. General description of the methods are provided in the papers
    Stathopoulos (2010, <doi:10.1145/1731022.1731031>) and Wu (2017, <doi:10.1137/16M1082214>).
    See 'citation("PRIMME")' for details.
Fitting a principal curve to a data matrix in arbitrary dimensions. 
  Hastie and Stuetzle (1989) <doi:10.2307/2289936>.
Optimization of conditional inference trees from the package 'party'
    for classification and regression.
    For optimization, the model space is searched for the best tree on the full sample by 
    means of repeated subsampling. Restrictions are allowed so that only trees are accepted 
    which do not include pre-specified uninterpretable split results (cf. Weihs & Buschfeld, 2021a).
    The function PrInDT() represents the basic resampling loop for 2-class classification (cf. Weihs 
    & Buschfeld, 2021a). The function RePrInDT() (repeated PrInDT()) allows for repeated  
    applications of PrInDT() for different percentages of the observations of the large and the 
    small classes (cf. Weihs & Buschfeld, 2021c). The function NesPrInDT() (nested PrInDT()) 
    allows for an extra layer of subsampling for a specific factor variable (cf. Weihs & Buschfeld, 
    2021b). The functions PrInDTMulev() and PrInDTMulab() deal with multilevel and multilabel 
    classification. In addition to these PrInDT() variants for classification, the function 
    PrInDTreg() has been developed for regression problems. Finally, the function PostPrInDT() 
    allows for a posterior analysis of the distribution of a specified variable in the terminal 
    nodes of a given tree.
    References are:
    -- Weihs, C., Buschfeld, S. (2021a) "Combining Prediction and Interpretation in 
    Decision Trees (PrInDT) - a Linguistic Example" <arXiv:2103.02336>;
    -- Weihs, C., Buschfeld, S. (2021b) "NesPrInDT: Nested undersampling in PrInDT" 
    <arXiv:2103.14931>;
    -- Weihs, C., Buschfeld, S. (2021c) "Repeated undersampling in PrInDT (RePrInDT): Variation 
    in undersampling and prediction, and ranking of predictors in ensembles" <arXiv:2108.05129>.
Extends the S3 generic function knit_print() in 'knitr'
    to automatically print some objects using an appropriate format such as
    Markdown or LaTeX. For example, data frames are automatically printed as
    tables, and the help() pages can also be rendered in 'knitr' documents.
Provides methods for reducing the number of features within a data set. See Bauer JO (2021) <doi:10.1145/3475827.3475832> and Bauer JO, Drabant B (2021) <doi:10.1016/j.jmva.2021.104754> for more information on principal loading analysis.
In gene sequencing methods, the topological features of protein-protein interaction (PPI) networks are 
    often used, such as ToppNet <https://toppgene.cchmc.org>. In this study, a candidate gene prioritization method
    was proposed for non-communicable diseases considering disease risks transferred between genes in weighted disease 
    PPI networks with weights for nodes and edges based on functional information.
Three-dimensional systematic conservation planning, conducting
             nested prioritization analyses across multiple depth levels and
             ensuring efficient resource allocation throughout the water column.
             It provides a structured workflow designed to address biodiversity
             conservation and management challenges in the 3 dimensions, while
             facilitating users’ choices and parameterization (Doxa et al. 2025
             <doi:10.1016/j.ecolmodel.2024.110919>).
An innovative tool-set that incorporates graph community detection
             methods into systematic conservation planning. It is designed to
             enhance spatial prioritization by focusing on the protection of
             areas with high ecological connectivity. Unlike traditional
             approaches that prioritize individual planning units, 'priorCON'
             focuses on clusters of features that exhibit strong ecological
             linkages. The 'priorCON' package is built upon the 'prioritizr'
             package <doi:10.32614/CRAN.package.prioritizr>, using commercial
             and open-source exact algorithm solvers that ensure optimal
             solutions to prioritization problems.
Translates beliefs into prior information in the form of Beta and Gamma distributions. 
	It can be used for the generation of priors on the prevalence of disease and 
	the sensitivity/specificity of diagnostic tests and any other binomial experiment.
This uses a mixed integer mathematical programming (MIP)
        approach for building and solving multi-action planning problems, 
        where the goal is to find an optimal combination of management actions that
        abate threats, in an efficient way while accounting for spatial aspects. 
        Thus, optimizing the connectivity and conservation effectiveness of 
        the prioritized units and of the deployed actions. The package is capable of 
        handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. 
        Gurobi optimization solver can be installed using comprehensive instructions in 
        the 'gurobi' installation vignette of the prioritizr package (available in 
        <https://prioritizr.net/articles/gurobi_installation_guide.html>). Instead, 'CPLEX'
        optimization solver can be obtain from IBM CPLEX web page (available here 
        <https://www.ibm.com/es-es/products/ilog-cplex-optimization-studio>). Additionally, 
        the 'rcbc' R package (available at
    <https://github.com/dirkschumacher/rcbc>) can be used to obtain solutions
    using the CBC optimization software (<https://github.com/coin-or/Cbc>). Methods used in the 
        package refers to Salgado-Rojas et al. (2020) <doi:10.1016/j.ecolmodel.2019.108901>,
        Beyer et al. (2016) <doi:10.1016/j.ecolmodel.2016.02.005>, Cattarino et al. (2015)
        <doi:10.1371/journal.pone.0128027> and Watts et al. (2009) <doi:10.1016/j.envsoft.2009.06.005>. 
        See the prioriactions website for more information, documentations and examples.
Systematic conservation prioritization using mixed integer linear
    programming (MILP). It provides a flexible interface for building and
    solving conservation planning problems. Once built, conservation planning
    problems can be solved using a variety of commercial and open-source exact
    algorithm solvers. By using exact algorithm solvers, solutions can be
    generated that are guaranteed to be optimal (or within a pre-specified
    optimality gap). Furthermore, conservation problems can be constructed to
    optimize the spatial allocation of different management actions or zones,
    meaning that conservation practitioners can identify solutions that benefit
    multiple stakeholders. To solve large-scale or complex conservation
    planning problems, users should install the Gurobi optimization software
    (available from <https://www.gurobi.com/>) and the 'gurobi' R package (see
    Gurobi Installation Guide vignette for details). Users can also install the
    IBM CPLEX software (<https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer>) and
    the 'cplexAPI' R package (available at <https://github.com/cran/cplexAPI>).
    Additionally, the 'rcbc' R package (available at
    <https://github.com/dirkschumacher/rcbc>) can be used to generate solutions
    using the CBC optimization software (<https://github.com/coin-or/Cbc>). For further details, see Hanson et al. (2024) <doi:10.1111/cobi.14376>.
Conservation planning datasets for learning how to use the
    'prioritizr' package <https://CRAN.R-project.org/package=prioritizr>.
Priority-ElasticNet extends the Priority-LASSO method (Klau et al. (2018) <doi:10.1186/s12859-018-2344-6>) by incorporating the ElasticNet penalty, allowing for both L1 and L2 regularization. 
    This approach fits successive ElasticNet models for several blocks of (omics) data with different priorities, using the predicted values from each block as an offset for the subsequent block. 
    It also offers robust options to handle block-wise missingness in multi-omics data, improving the flexibility and applicability of the model in the presence of incomplete datasets.
Fits successive Lasso models for several blocks of (omics) data with different priorities and takes the predicted values as an offset for the next block. Also offers options to deal with block-wise missingness in multi-omics data.
Provides functions for prior and likelihood sensitivity analysis in Bayesian models. Currently it implements methods to determine the sensitivity of the posterior to power-scaling perturbations of the prior and likelihood.
Allows users to access the Oregon State Prism climate data
    (<https://prism.nacse.org/>). Using the web service API data
    can easily downloaded in bulk and loaded into R for spatial analysis.
    Some user friendly visualizations are also provided.
Implements Penalized Regression with Inferred Seasonality Module (PRISM) to generate forecast estimation of weekly unemployment initial claims using 'Google Trends' data. It includes required data and tools for backtesting the performance in 2007-2020.
Loads and processes huge text
    corpora processed with the sally toolbox (<http://www.mlsec.org/sally/>).
    sally acts as a very fast preprocessor which splits the text files into
    tokens or n-grams. These output files can then be read with the PRISMA
    package which applies testing-based token selection and has some
    replicate-aware, highly tuned non-negative matrix factorization and
    principal component analysis implementation which allows the processing of
    very big data sets even on desktop machines.
Systematic reviews should be described in a high degree of 
    methodological detail. The 'PRISMA' Statement calls for a high level of 
    reporting detail in systematic reviews and meta-analyses. An integral part 
    of the methodological description of a review is a flow diagram. 
    This package produces an interactive flow diagram that conforms to the 
    'PRISMA2020' preprint. When made interactive, the reader/user can click 
    on each box and be directed to another website or file online (e.g. a 
    detailed description of the screening methods, or a list of excluded full 
    texts), with a mouse-over tool tip that describes the information linked 
    to in more detail. Interactive versions can be saved as HTML files, 
    whilst static versions for inclusion in manuscripts can be saved as 
    HTML, PDF, PNG, SVG, PS or WEBP files.
Creates 'PRISMA' <http://prisma-statement.org/> diagram from a minimal dataset of included and excluded studies and allows for more custom diagrams. 'PRISMA' diagrams are used to track the identification, screening, eligibility, and inclusion of studies in a systematic review.
Manipulate and visualize colors in a intuitive,
    low-dependency and functional way.
Prism <https://prismjs.com/> is a lightweight, extensible syntax 
    highlighter, built with modern web standards in mind. This package provides 
    server-side rendering in R using 'V8' such that no JavaScript library is
    required in the resulting HTML documents. Over 400 languages are supported.
Download, parses and tidies information from the World Prison Brief project <http://www.prisonstudies.org/>.
Enables user to perform the following:
    1. Roll 'n' number of die/dice (roll()).
    2. Toss 'n' number of coin(s) (toss()).
    3. Play the game of Rock, Paper, Scissors.
    4. Choose 'n' number of card(s) from a pack of 52 playing cards (Joker optional).
Implements two differentially private algorithms for 
  estimating L2-regularized logistic regression coefficients. A randomized
  algorithm F is epsilon-differentially private (C. Dwork, Differential
  Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if 
     |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
  for any pair D, D' of datasets that differ in exactly one record, any
  measurable set S, and the randomness is taken over the choices F makes.
Calculates seat allocation using the D-Hondt method, Sainte-Lague method, and Modified Sainte-Lague method, all commonly used in proportional representation electoral systems. For more information on these methods, see Michael Gallagher (1991)<doi:10.1016/0261-3794(91)90004-C>.
Provides functions for generating pseudo-random numbers that follow a uniform distribution [0,1]. Randomness tests were conducted using the National Institute of Standards and Technology test suite<https://csrc.nist.gov/pubs/sp/800/22/r1/upd1/final>, along with additional tests. The sequence generated depends on the initial values and parameters. The package includes a linear congruence map as the decision map and three chaotic maps to generate the pseudo-random sequence, which follow a uniform distribution. Other distributions can be generated from the uniform distribution using the Inversion Principle Method and BOX-Muller transformation. Small perturbations in seed values result in entirely different sequences of numbers due to the sensitive nature of the maps being used. The chaotic nature of the maps helps achieve randomness in the generator. Additionally, the generator is capable of producing random bits.
Survey sampling using permanent random numbers (PRN's). A solution
    to the problem of unknown overlap between survey samples, which leads to a
    low precision in estimates when the survey is repeated or combined with
    other surveys. The PRN solution is to supply the U(0, 1) random numbers to
    the sampling procedure, instead of having the sampling procedure generate
    them. In Lindblom (2014) <doi:10.2478/jos-2014-0047>, and therein cited
    papers, it is shown how this is carried out and how it improves the
    estimates. This package supports two common fixed-size sampling procedures
    (simple random sampling and probability-proportional-to-size sampling) and
    includes a function for transforming the PRN's in order to control the
    sample overlap.
Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light.  This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments.  This method provides explicit nonlinear transformations to link the flash point-process with the spiking point-process.  Such response functions can be used to provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation.
Automate the detection of gaps and elevations in mapped sequencing 
  read coverage using a 2D pattern-matching algorithm. 'ProActive' detects, characterizes 
  and visualizes read coverage patterns in both genomes and metagenomes. Optionally, 
  users may provide gene annotations associated with their genome or metagenome 
  in the form of a .gff file. In this case, 'ProActive' will generate an additional 
  output table containing the gene annotations found within the detected regions of gapped 
  and elevated read coverage. Additionally, users can search for gene 
  annotations of interest in the output read coverage plots.
A collection of tools to facilitate standardized analysis 
    and graphical procedures when using the National Cancer Institute’s 
    Patient-Reported Outcomes version of the Common Terminology Criteria for 
    Adverse Events (PRO-CTCAE) and other PRO measurements.
Models can be improved by post-processing class
    probabilities, by: recalibration, conversion to hard probabilities,
    assessment of equivocal zones, and other activities. 'probably'
    contains tools for conducting these operations as well as calibration
    tools and conformal inference techniques for regression models.
Functions and datasets  to accompany J. Albert and J. Hu, "Probability and Bayesian Modeling", CRC Press, (2019, ISBN: 1138492566).
Use probability theory under the Bayesian framework for calculating the risk of selecting candidates in a multi-environment context. Contained are functions used to fit a Bayesian multi-environment model (based on the available presets), extract posterior values and maximum posterior values, compute the variance components, check the model’s convergence, and calculate the probabilities. For both across and within-environments scopes, the package computes the probability of superior performance and the pairwise probability of superior performance. Furthermore, the probability of superior stability and the pairwise probability of superior stability across environments is estimated. A joint probability of superior performance and stability is also provided.
Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) <arXiv:2209.08139>.
Hybridization probes for target sequences can be made based on melting temperature value calculated by R package 'TmCalculator' <https://CRAN.R-project.org/package=TmCalculator> and methods extended from Beliveau, B. J.,(2018) <doi:10.1073/pnas.1714530115>, and those hybridization probes can be used to capture specific target regions in fluorescence in situ hybridization and next generation sequence experiments.
Fast estimation of binomial spatial probit regression models with spatial autocorrelation for big datasets.
Computes predicted probabilities and marginal effects for 
  binary & ordinal logit and probit, (partial) generalized ordinal & 
  multinomial logit models estimated with the glm(), clm() (in the 
  'ordinal' package), and vglm() (in the 'VGAM' package) functions.
Estimates unsupervised outlier probabilities for multivariate numeric data with many observations from a nonparametric outlier statistic.
Performs elementary probability calculations on finite sample spaces, which may be represented by data frames  or lists.
    This package is meant to rescue some widely used functions from the archived 'prob' package (see <https://cran.r-project.org/src/contrib/Archive/prob/>).
    Functionality includes setting up sample spaces, counting tools, defining probability spaces, performing set algebra, calculating probability and conditional probability, tools for simulation and checking the law of  large numbers, adding random variables, and finding marginal distributions.  Characteristic functions for all base R distributions are included.
It allows the user to determine sample sizes, select probabilistic samples,
 make estimates of different parameters for the total finite population and in studio domains,
 using the main design drawings.
An interactive document on  the topic of basic probability using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/BayesShiny/>.
Utilities for multiple hypothesis testing, companion datasets from "Probability and Statistics for Economics and Business: An Introduction Using R" by Jason Abrevaya (MIT Press, under contract).
Confidence intervals and point estimation for R under various parametric model assumptions; likelihood inference based on classical first-order approximations and higher-order asymptotic procedures.
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
Package for processing downloaded MODIS Calibrated radiances
    Product HDF files. Specifically, MOD02 calibrated radiance product files, and
    the associated MOD03 geolocation files (for MODIS-TERRA). The package will be
    most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath
    products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and
    adds the directory with the MRTSwath executable to the default R PATH by editing
    ~/.Rprofile.
Provides tools for exploratory process data analysis. Process data refers to the data describing
    participants' problem-solving processes in computer-based assessments. It is often recorded in computer
    log files. This package provides functions to read, process, and write process data. It also implements
    two feature extraction methods to compress the information stored in process data into standard 
    numerical vectors. This package also provides recurrent neural network based models that relate response processes 
    with other binary or scale variables of interest. The functions that involve training and evaluating neural networks 
    are wrappers of functions in 'keras'.
A small, dependency-free way to generate random names. Methods 
    provided include the adjective-surname approach of Docker containers 
    ('<https://github.com/moby/moby/blob/master/pkg/namesgenerator/names-generator.go>'),
    and combinations of common English or Spanish words.
Provides animated process maps based on the 'procesmapR' package.
  Cases stored in event logs created with with 'bupaR' S3 class eventlog() are
  rendered as tokens (SVG shapes) and animated according to their occurrence 
  times on top of the process map. For rendering SVG animations ('SMIL') and the
  'htmlwidget' package are used.
Check compliance of event-data from (business) processes with respect to specified rules. Rules supported are of three types: frequency (activities that should (not) happen x number of times), order (succession between activities) and exclusiveness (and and exclusive choice between activities).
Visualize event logs using directed graphs, i.e. process maps. Part of the 'bupaR' framework.
Functions for constructing dashboards for business process monitoring. Building on the event log objects class from package 'bupaR'. Allows the use to assemble custom shiny dashboards based on process data.
Means to predict process flow, such as process outcome, next activity, next time, remaining time, and remaining trace. Off-the-shelf predictive models based on the concept of Transformers are provided, as well as multiple ways to customize the models. This package is partly based on work described in Zaharah A. Bukhsh, Aaqib Saeed, & Remco M. Dijkman. (2021). "ProcessTransformer: Predictive Business Process Monitoring with Transformer Network" <arXiv:2104.00721>.
Tools to run system processes in the background.  It can
    check if a background process is running; wait on a background process
    to finish; get the exit status of finished processes; kill background
    processes. It can read the standard output and error of the processes,
    using non-blocking connections. 'processx' can poll a process for
    standard output or error, with a timeout. It can also poll several
    processes at once.
Provides functions for simulating from and fitting the latent hidden
    Markov models for response process data (Tang, 2024) <doi:10.1007/s11336-023-09938-1>. 
    It also includes functions for simulating from and fitting ordinary hidden Markov models.
Portable '/proc/self/maps' as a data frame.
    Determine which library or other region is mapped to a specific
    address of a process. --
    R packages can contain native code, compiled to shared libraries at build or
    installation time.
    When loaded, each shared library occupies a portion of the address space of
    the main process.
    When only a machine instruction pointer is available (e.g. from a backtrace
    during error inspection or profiling), the address space map determines
    which library this instruction pointer corresponds to.
Estimates corrected Procrustean correlation between matrices for removing overfitting effect. Coissac Eric and Gonindard-Melodelima Christelle (2019) <doi:10.1101/842070>.
Contains functions to simulate the most commonly used 
    'SAS®' procedures.  Specifically, the package aims to 
    simulate the functionality of 'proc freq', 'proc means', 'proc ttest',
    'proc reg', 'proc transpose', 'proc sort', and 'proc print'. 
    The simulation will include recreating 
    all statistics with the highest fidelity possible.
A direct and flexible method for estimating an ICA model. This approach estimates the densities for each component directly via a tilted Gaussian. The tilt functions are estimated via a GAM Poisson model. Details can be found in "Elements of Statistical Learning (2nd Edition)" in Section 14.7.4.
Implements the methods proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>, Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>, Ackerberg, D.A. and Caves, K. and Frazer, G. (2015) <doi:10.3982/ECTA13408> and Wooldridge, J.M. (2009) <doi:10.1016/j.econlet.2009.04.026> for structural productivity estimation .
Create a project directory structure, along with typical
    files for that project.  This allows projects to be quickly and easily
    created, as well as for them to be standardized. Designed specifically
    with scientists in mind (mainly bio-medical researchers, but likely
    applies to other fields).
Fast and user friendly implementation of nonparametric estimators
    for censored event history (survival) analysis. Kaplan-Meier and
    Aalen-Johansen method.
Levels and changes of productivity and profitability are measured with various indices.
  The package contains the multiplicatively complete Färe-Primont, Fisher, Hicks-Moorsteen, 
  Laspeyres, Lowe, and Paasche indices, as well as the classic Malmquist productivity index.
  Färe-Primont and Lowe indices verify the transitivity property and can therefore be used for 
  multilateral or multitemporal comparison.
  Fisher, Hicks-Moorsteen, Laspeyres, Malmquist, and Paasche indices are not transitive and are 
  only to be used for binary comparison.
  All indices can also be decomposed into different components, providing insightful information 
  on the sources of productivity and profitability changes.
  In the use of Malmquist productivity index, the technological change index can be further 
  decomposed into bias technological change components.
  The package also allows to prohibit technological regression (negative technological change). In 
  the case of the Fisher, Hicks-Moorsteen, Laspeyres, Paasche and the transitive Färe-Primont 
  and Lowe indices, it is furthermore possible to rule out technological change. 
  Deflated shadow prices can also be obtained. Besides, the package allows parallel computing as 
  an option, depending on the user's computer configuration. 
  All computations are carried out with the nonparametric Data Envelopment Analysis (DEA), and 
  several assumptions regarding returns to scale are available.
  All DEA linear programs are implemented using 'lp_solve'.
Framework for visualising tables of counts, proportions
  and probabilities. The framework is called product plots, alluding to
  the computation of area as a product of height and width, and the
  statistical concept of generating a joint distribution from the
  product of conditional and marginal distributions. The framework,
  with extensions, is sufficient to encompass over 20 visualisations
  previously described in fields of statistical graphics and 'infovis',
  including bar charts, mosaic plots, 'treemaps', equal area plots and
  fluctuation diagrams.
Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations.
    More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.
Programs to determine student grades and create
  examinations from Question banks.  Programs will create numerous
  multiple choice exams, randomly shuffled, for different versions of same question list.
Computes profile extrema functions for arbitrary functions. If the function is expensive-to-evaluate it computes profile extrema by emulating the function with a Gaussian process (using package 'DiceKriging'). In this case uncertainty quantification on the profile extrema can also be computed. The different plotting functions for profile extrema give the user a tool to better locate excursion sets.
Like similar profiling tools,
  the 'proffer' package automatically detects
  sources of slowness in R code.
  The distinguishing feature of 'proffer' is its utilization of
  'pprof', which supplies interactive visualizations
  that are efficient and easy to interpret.
  Behind the scenes, the 'profile' package converts
  native Rprof() data to a protocol buffer
  that 'pprof' understands.
  For the documentation of 'proffer',
  visit <https://r-prof.github.io/proffer/>.
  To learn about the implementations and methodologies of
  'pprof', 'profile', and protocol buffers,
  visit <https://github.com/google/pprof>.
  <https://protobuf.dev>,
  and <https://github.com/r-prof/profile>, respectively.
Defines a data structure for profiler data, and methods to read and
    write from the 'Rprof' and 'pprof' file formats.
Provides profile likelihoods for a parameter of interest in commonly used statistical models. The models include linear models, generalized linear models, proportional odds models, linear mixed-effects models, and linear models for longitudinal responses fitted by generalized least squares. The package also provides plots for normalized profile likelihoods as well as the maximum profile likelihood estimates and the kth likelihood support intervals.
Provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors. More information on the methods that are implemented can be found in Kosmidis (2008) <https://www.r-project.org/doc/Rnews/Rnews_2008-2.pdf>.
A suite of multivariate methods and data visualization 
      tools to implement profile analysis and cross-validation techniques described in 
      Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources.
      The package includes routines to perform criterion-related profile analysis, profile analysis 
      via multidimensional scaling, moderated profile analysis, profile analysis by group, and a 
      within-person factor model to derive score profiles.
Help unify visual output of R analyses in the Profinit EU company. 
  So far, there are color and fill scales for 'ggplot2', plotting theme for 
  'ggplot2', color palettes and utils to make the tools default choices.
A simple and light-weight API for memory profiling of R expressions.  The profiling is built on top of R's built-in memory profiler ('utils::Rprofmem()'), which records every memory allocation done by R (also native code).
Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <doi:10.48550/arXiv.2102.00968> <doi:10.1016/j.jeconom.2021.11.008>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <doi:10.48550/arXiv.1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.
Provides an R interface for the PROFOUND database <doi:10.5880/PIK.2019.008>. The 
    PROFOUND database contains a wide range of data  to evaluate vegetation models and simulate climate 
    impacts at the forest stand scale. It includes 9 forest sites across Europe, and provides for them a site 
    description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level 
    and remote sensing data. Moreover, for a subset of 5 sites, also time series of carbon fluxes, 
    energy balances and soil water are available.
An alternative data structure and visual rendering
    for the profiling information generated by Rprof.
Calculates profile repeatability for replicate stress response 
  curves, or similar time-series data. Profile repeatability is an individual 
  repeatability metric that uses the variances at each timepoint, the maximum 
  variance, the number of crossings (lines that cross over each other), and 
  the number of replicates to compute the repeatability score. 
  For more information see Reed et al. (2019) <doi:10.1016/j.ygcen.2018.09.015>.
Tools for examining Rprof profile output.
Interactive visualizations for profiling R code.
Implementing the Progeny Clustering algorithm, the 'progenyClust' package assesses the clustering stability and identifies the optimal clustering number for a given data matrix. It uses k-means clustering as a default, provides a tailored hierarchical clustering function, and can be customized to work with other clustering algorithms and different parameter settings. The package includes a main function progenyClust(), plot and summary methods for 'progenyClust' object, a function hclust.progenyClust() for hierarchical clustering, and two example datasets (test and cell) for testing.
A novel tool to identify candidate driver modules for predicting the prognosis of patients by integrating exclusive coverage of mutations with clinical characteristics in cancer.
Configurable Progress bars, they may include percentage,
    elapsed time, and/or the estimated completion time. They work in
    terminals, in 'Emacs' 'ESS', 'RStudio', 'Windows' 'Rgui' and the
    'macOS' 'R.app'. The package also provides a 'C++' 'API', that works
    with or without 'Rcpp'.
A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing.  The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it.  The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar(), cli::cli_progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beepr::beep(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce API:s like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. future.apply::future_lapply(), furrr::future_map(), and 'foreach' with 'doFuture'. The package is compatible with Shiny applications.
A wrapper around the generic coordinate transformation software 'PROJ'
  that transforms coordinates from one coordinate reference system ('CRS')
  to another. This includes cartographic projections as well as geodetic transformations.  The intention is for this
  package to be used by user-packages such as 'reproj', and that the older 'PROJ.4' and version 5
  pathways be provided by the 'proj4' package.
A simple interface to lat/long projection and datum
	     transformation of the PROJ.4 cartographic projections library. It
	     allows transformation of geographic coordinates from one projection
	     and/or datum to another.
A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" <DOI:10.1007/s00357-020-09373-2>. Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R.
Provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R in 'EpiEstim' or in 'earlyR', and described by Nouvellet et al. (2017) <doi:10.1016/j.epidem.2017.02.012>. The package provides the S3 class 'projections' which extends 'matrix', with accessors and additional helpers for handling, subsetting, merging, or adding these objects, as well as dedicated printing and plotting methods.
Management problems of deterministic and stochastic projects. It obtains the duration of a project and the appropriate slack for each activity in a deterministic context. In addition it obtains a schedule of activities' time (Castro, Gómez & Tejada (2007) <doi:10.1016/j.orl.2007.01.003>). It also allows the management of resources. When the project is done, and the actual duration for each activity is known, then it can know how long the project is delayed and make a fair delivery of the delay between each activity (Bergantiños, Valencia-Toledo & Vidal-Puga (2018) <doi:10.1016/j.dam.2017.08.012>). In a stochastic context it can estimate the average duration of the project and plot the density of this duration, as well as, the density of the early and last times of the chosen activities. As in the deterministic case, it can make a distribution of the delay generated by observing the project already carried out.
Provides a project infrastructure with a focus on
    manuscript creation. Creates a project folder with a single command,
    containing subdirectories for specific components, templates for
    manuscripts, and so on.
Provides functions to
    automatically build a directory structure for a new R
    project. Using this structure, 'ProjectTemplate'
    automates data loading, preprocessing, library
    importing and unit testing.
Provides programmatic access to 'GitHub' API with a
    focus on project management.  Key functionality includes
    setting up issues and milestones from R objects or 'YAML' configurations,
    querying outstanding or completed tasks, and generating progress updates
    in tables, charts, and RMarkdown reports. Useful for those using 'GitHub' in personal,
    professional, or academic settings with an emphasis on streamlining
    the workflow of data analysis projects.
Performs projection predictive feature selection for generalized linear
    models (Piironen, Paasiniemi, and Vehtari, 2020, <doi:10.1214/20-EJS1711>)
    with or without multilevel or additive terms (Catalina, Bürkner, and
    Vehtari, 2022, <https://proceedings.mlr.press/v151/catalina22a.html>), for
    some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2023,
    <arXiv:2301.01660>), and for many other regression models (using the latent
    projection by Catalina, Bürkner, and Vehtari, 2021, <arXiv:2109.04702>,
    which can also be applied to most of the former models). The package is
    compatible with the 'rstanarm' and 'brms' packages, but other reference
    models can also be used. See the vignettes and the documentation for more
    information and examples.
Provides functions for calculating and analyzing the proliferative 
    index (PI) from an RNA-seq dataset. As described in Ramaker & Lasseigne, 
    et al. bioRxiv, 2016 <doi:10.1101/063057>.
A user-friendly interface for creating and managing empirical crowd-sourcing studies via API access to <https://www.prolific.co>.
Procrustes matching of the posterior samples of person and item latent positions from latent space item response models. The methods implemented in this package are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>, and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) <doi:10.18637/jss.v042.i09>.
Algorithms and subroutines for patient-reported outcome data analysis.
Functions which can be used to support the Multicriteria Decision Analysis (MCDA) process 
    involving multiple criteria, by PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations).
The PROMETHEE method is a multi-criteria decision-making method addressing with outranking problems.
            The method establishes a preference structure between the alternatives, having a preference function for
            each criterion. IN this context, three variants of the method is carried out: PROMETHEE I (Partial Outranking),
            PROMETHEE II (Total Outranking), and PROMETHEE III (Outranking by Intervals).
PROMETHEE (Preference Ranking Organisation METHod for Enrichment of
    Evaluations) based method assesses alternatives to obtain partial and complete
    rankings. The package also provides the GLNF (Global Local Net Flow) sorting
    algorithm to classify alternatives into ordered categories, as well as an index
    function to measure the classification quality. Barrera, F., Segura, M., &
    Maroto, C. (2023) <doi:10.1111/itor.13288>. Brans, J.P.; De Smet, Y., (2016) 
    <doi:10.1007/978-1-4939-3094-4_6>.
Provides fundamental abstractions for doing asynchronous
    programming in R using promises. Asynchronous programming is useful
    for allowing a single R process to orchestrate multiple tasks in the
    background while also attending to something else. Semantics are
    similar to 'JavaScript' promises, but with a syntax that is idiomatic
    R.
A comprehensive, user-friendly package for label-free proteomics data analysis and machine learning-based modeling. Data generated from 'MaxQuant' can be easily used to conduct differential expression analysis, build predictive models with top protein candidates, and assess model performance. promor includes a suite of tools for quality control, visualization, missing data imputation (Lazar et. al. (2016) <doi:10.1021/acs.jproteome.5b00981>), differential expression analysis (Ritchie et. al. (2015) <doi:10.1093/nar/gkv007>), and machine learning-based modeling (Kuhn (2008) <doi:10.18637/jss.v028.i05>).
Deploy, maintain, and invoke predictive models using the 'Alteryx
    Promote' REST API.  'Alteryx Promote' is available at the URL:
    <https://www.alteryx.com/products/alteryx-promote>.
Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) <doi:10.7287/peerj.preprints.3190v2> and some prespecified variables (e.g. start of a month).
Set the 'R' prompt dynamically, from a function. The package
    contains some examples to include various useful dynamic information
    in the prompt: the status of the last command (success or failure);
    the amount of memory allocated by the current 'R' process; the name of
    the R package(s) loaded by 'pkgload' and/or 'devtools'; various 'git'
    information: the name of the active branch, whether it is dirty,
    if it needs pushes pulls. You can also create your own prompt if you
    don't like the predefined examples.
In 'Shiny' apps, it is sometimes useful to store information
    on a particular item in a tooltip. 'Prompter' allows you to easily 
    create such tooltips, using 'Hint.css'.
Format and submit few-shot prompts to OpenAI's Large Language Models (LLMs). Designed to be particularly useful for text classification problems in the social sciences. Methods are described in Ornstein, Blasingame, and Truscott (2024) <https://joeornstein.github.io/publications/ornstein-blasingame-truscott.pdf>.
A native 'R' client library for querying the 'Prometheus' 
    time-series database, using the 'PromQL' query language.
Carrying out inferences about any linear combination of proportions and the ratio of two proportions.
Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation.
Computes two-sample confidence intervals for single, paired and independent proportions.
Implementation of propensity clustering and
        decomposition as described in Ranola et al. (2013) <doi:10.1186/1752-0509-7-21>. 
        Propensity decomposition can be viewed on the
        one hand as a generalization of the eigenvector-based
        approximation of correlation networks, and on the other hand as
        a generalization of random multigraph models and
        conformity-based decompositions.
Allows to parse 'Java' properties files in the context of
  'R Service Bus' applications.
Implements a procedure for forecasting time series data based on
    an additive model where non-linear trends are fit with yearly, weekly, and
    daily seasonality, plus holiday effects. It works best with time series
    that have strong seasonal effects and several seasons of historical data.
    Prophet is robust to missing data and shifts in the trend, and typically
    handles outliers well.
Calculate seat apportionment for legislative bodies with 
    various methods. The algorithms include divisor or highest averages methods
    (e.g. Jefferson, Webster or Adams), largest remainder methods and 
    biproportional apportionment.
    Gaffke, N. & Pukelsheim, F. (2008) <doi:10.1016/j.mathsocsci.2008.01.004>
    Oelbermann, K. F. (2016) <doi:10.1016/j.mathsocsci.2016.02.003>.
A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.
Contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization.  Currently only supports trials comparing two groups.
An API wrapper around the 'ProPublica' API <https://projects.propublica.org/api-docs/congress-api/> for U.S. Congressional Bills. Users can include their API key, U.S. Congress, branch, and offset ranges, to return a dataframe of all results within those parameters. This package is different from the 'RPublica' package because it is for the 'ProPublica' U.S. Congress data API, and the 'RPublica' package is for the Nonprofit Explorer, Forensics, and Free the Files data APIs.
Provides wrapper functions to access the ProPublica's Congress and Campaign Finance APIs.
    The Congress API provides near real-time access to legislative data from the House of 
    Representatives, the Senate and the Library of Congress.
    The Campaign Finance API provides data from United States Federal Election Commission 
    filings and other sources. The API covers summary information for candidates and 
    committees, as well as certain types of itemized data.
    For more information about these APIs go to: <https://www.propublica.org/datastore/apis>.
It offers a wide variety of techniques, such as graphics, recoding, or regression models, for a comprehensive analysis of patient-reported outcomes (PRO). Especially novel is the broad range of regression models based on the beta-binomial distribution useful for analyzing binomial data with over-dispersion in cross-sectional, longitudinal, or multidimensional response studies (see Najera-Zuloaga J., Lee D.-J. and Arostegui I. (2019) <doi:10.1002/bimj.201700251>).
An extensible repository of accurate, up-to-date functions to
    score commonly used patient-reported outcome (PRO), quality of life
    (QOL), and other psychometric and psychological measures.
    'PROscorer', together with the 'PROscorerTools' package, is a system
    to facilitate the incorporation of PRO measures into research studies
    and clinical settings in a scientifically rigorous and reproducible
    manner.  These packages and their vignettes are intended to help
    establish and promote best practices for scoring PRO and PRO-like 
    measures in research.  The 'PROscorer' Instrument Descriptions vignette 
    contains descriptions of each instrument scored by 'PROscorer', complete 
    with references.  These instrument descriptions are suitable for inclusion 
    in formal study protocol documents, grant proposals, and manuscript Method
    sections.  Each 'PROscorer' function is composed of helper functions
    from the 'PROscorerTools' package, and users are encouraged to
    contribute new functions to 'PROscorer'.  More scoring functions are
    currently in development and will be added in future updates.
Provides a reliable and flexible toolbox to score 
    patient-reported outcome (PRO), Quality of Life (QOL), and other 
    psychometric measures. The guiding philosophy is that scoring errors can 
    be eliminated by using a limited number of well-tested, well-behaved 
    functions to score PRO-like measures. The workhorse of the package is 
    the 'scoreScale' function, which can be used to score most single-scale 
    measures. It can reverse code items that need to be reversed before 
    scoring and pro-rate scores for missing item data. Currently, three 
    different types of scores can be output: summed item scores, mean item 
    scores, and scores scaled to range from 0 to 100. The 'PROscorerTools' 
    functions can be used to write new functions that score more complex 
    measures. In fact, 'PROscorerTools' functions are the building blocks of 
    the scoring functions in the 'PROscorer' package (which is a repository 
    of functions that score specific commonly-used instruments). Users are 
    encouraged to use 'PROscorerTools' to write scoring functions for their 
    favorite PRO-like instruments, and to submit these functions for 
    inclusion in 'PROscorer' (a tutorial vignette will be added soon). The 
    long-term vision for the 'PROscorerTools' and 'PROscorer' packages is to 
    provide an easy-to-use system to facilitate the incorporation of PRO 
    measures into research studies in a scientifically rigorous and 
    reproducible manner. These packages and their vignettes are intended to 
    help establish and promote "best practices" for scoring and describing 
    PRO-like measures in research.
Perform scale linking to establish relationships between instruments
    that measure similar constructs according to the PROsetta Stone methodology, as in Choi, Schalet, Cook, & Cella (2014) <doi:10.1037/a0035768>.
Implementation of penalized regression with second-generation p-values for variable
    selection. The algorithm can handle linear regression, GLM, and Cox regression. S3 methods print(), summary(), coef(), predict(), and plot() are available for the algorithm. Technical details
    can be found at Zuo et al. (2021) <doi:10.1080/00031305.2021.1946150>.
Functions to preprocess spectroscopic data 
    and conduct (representative) sample selection/calibration sampling.
An environment to simulate the development of annual plant populations with regard to population dynamics and genetics, especially herbicide resistance. It combines genetics on the individual level (Renton et al. 2011) with a stochastic development on the population level (Daedlow, 2015).
	Renton, M, Diggle, A, Manalil, S and Powles, S (2011) <doi:10.1016/j.jtbi.2011.05.010> 
	Daedlow, Daniel (2015, doctoral dissertation: University of Rostock, Faculty of Agriculture and Environmental Sciences.)
We provide comprehensive draft data for major professional sports leagues, including the National Football League (NFL), National Basketball Association (NBA), and National Hockey League (NHL). It offers access to both historical and current draft data, allowing for detailed analysis and research on player biases and player performance. The package is useful for sports fans and researchers interested in identifying biases and trends within scouting reports. Created by web scraping data from leading websites that cover professional sports player scouting reports, the package allows users to filter and summarize data for analytical purposes. For further details on the methods used, please refer to Wickham (2022) "rvest: Easily Harvest (Scrape) Web Pages" <https://CRAN.R-project.org/package=rvest> and Harrison (2023) "RSelenium: R Bindings for Selenium WebDriver" <https://CRAN.R-project.org/package=RSelenium>.
In a typical protein labelling procedure, proteins are chemically tagged with a functional group, usually at specific sites, then digested into peptides, which are then analyzed using matrix-assisted laser desorption ionization - time of flight mass spectrometry (MALDI-TOF MS) to generate peptide fingerprint. Relative to the control, peptides that are heavier by the mass of the labelling group are informative for sequence determination. Searching for peptides with such mass shifts, however, can be difficult. This package, designed to tackle this inconvenience, takes as input the mass list of two or multiple MALDI-TOF MS mass lists, and makes pairwise comparisons between the labeled groups vs. control, and restores centroid mass spectra with highlighted peaks of interest for easier visual examination. Particularly, peaks differentiated by the mass of the labelling group are defined as a “pair”, those with equal masses as a “match”, and all the other peaks as a “mismatch”.For more bioanalytical background information, refer to following publications: Jingjing Deng (2015) <doi:10.1007/978-1-4939-2550-6_19>; Elizabeth Chang (2016) <doi:10.7171/jbt.16-2702-002>.
The 'Proteomics Eye' ('ProtE') offers a comprehensive and intuitive framework for the univariate analysis of label-free proteomics data. By integrating essential data wrangling and processing steps into a single function, 'ProtE' streamlines pairwise statistical comparisons for categorical variables. It provides quality checks and generates publication-ready visualizations, enabling efficient and robust data analysis. 'ProtE' is compatible with proteomics data outputs from 'MaxQuant' (Cox & Mann, (2008) <doi:10.1038/nbt.1511>), 'DIA-NN' (Demichev et al., (2020) <doi:10.1038/s41592-019-0638-x>), and 'Proteome Discoverer' (Thermo Fisher Scientific, version 2.5). The package leverages 'ggplot2' for visualization (Wickham, (2016) <doi:10.1007/978-3-319-24277-4>) and 'limma' for statistical analysis (Ritchie et al., (2015) <doi:10.1093/nar/gkv007>).
Read Protein Data Bank (PDB) files, performs its analysis, and 
    presents the result using different visualization types including 3D. The 
    package also has additional capability for handling Virus Report data from 
    the National Center for Biotechnology Information (NCBI) database.
    Nature Structural Biology 10, 980 (2003) <doi:10.1038/nsb1203-980>.
    US National Library of Medicine (2021) <https://www.ncbi.nlm.nih.gov/datasets/docs/reference-docs/data-reports/virus/>.
Bayesian toolbox for quantitative proteomics. In particular, this 
    package provides functions to generate synthetic datasets, execute Bayesian
    differential analysis methods, and display results as, described in the 
    associated article Marie Chion and Arthur Leroy (2023) <arXiv:2307.08975>.
Calculates the percentage coefficient of variation (CV) for mass spectrometry-based proteomic data. The CV can be calculated with the traditional formula for raw (non log transformed) intensity data, or log transformed data.
Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable.
Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins. 
    Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take 
    advantage of more accurate multiple sequence alignment by programs such as 'HHBlits' Remmert et al. (2012) 
    <DOI:10.1038/nmeth.1818> and 'HMMer' Eddy (2011) <DOI:10.1371/journal.pcbi.1002195>. Features calculated by 
    this package can be used for protein fold classification, protein structural class prediction, 
    sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features 
    extracted are found in Song et al. (2018) <DOI:10.3390/app8010089>, Jin & Zhu (2021) <DOI:10.1155/2021/8629776>, 
    Lyons et al. (2015) <DOI:10.1109/tnb.2015.2457906> and Saini et al. (2015) <DOI:10.1016/j.jtbi.2015.05.030>.
An object oriented system using object-based, also
	called prototype-based, rather than class-based object oriented ideas.
Performs minimax linkage hierarchical clustering.  Every cluster
    has an associated prototype element that represents that cluster as
    described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering 
    with Prototypes via Minimax Linkage," The Journal of the American 
    Statistical Association, 106(495), 1075-1084.
Pure C++ implementations for reading and writing several common data 
    formats based on Google protocol-buffers. Currently supports 'rexp.proto' for 
    serialized R objects, 'geobuf.proto' for binary geojson, and 'mvt.proto' for 
    vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, 
    hence the entire serialization is optimized at compile time. The 'RProtoBuf' 
    package on the other hand uses the protobuf runtime library to provide a general-
    purpose toolkit for reading and writing arbitrary protocol-buffer data in R.
'The Proton Game' 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.
  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.
  The knowledge of dplyr is not required but may be very helpful.
  This game is linked with the ,,Pietraszko's Cave'' story available at http://biecek.pl/BetaBit/Warsaw.
  It's a part of Beta and Bit series.
  You will find more about the Beta and Bit series at http://biecek.pl/BetaBit.
Shiny app to interactively visualize hierarchical clustering with prototypes. 
    For details on hierarchical clustering with prototypes, see 
    Bien and Tibshirani (2011) <doi:10.1198/jasa.2011.tm10183>. This package currently launches the application.
Procedures for testing for group-wide signal in clusters of variables. Tests can be performed for single groups in isolation (univariate) or multiple groups together (multivariate). Specific tests include the exact and approximate (un)selective likelihood ratio tests described in Reid et al (2015), the selective F test and marginal screening prototype test of Reid and Tibshirani (2015). User may pre-specify columns to be included in prototype formation, or allow the function to select them itself. A mixture of these two is also possible. Any variable selection is accounted for using the selective inference framework. Options for non-sampling and hit-and-run null reference distributions.
Comprehensive toolkit for generating various numerical
    features of protein sequences described in Xiao et al. (2015)
    <DOI:10.1093/bioinformatics/btv042>. For full functionality,
    the software 'ncbi-blast+' is needed, see
    <https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html>
    for more information.
'ProTracker' is a popular music tracker to sequence
    music on a Commodore Amiga machine. This package offers the
    opportunity to import, export, manipulate and play 'ProTracker'
    module files. Even though the file format could be considered
    archaic, it still remains popular to this date. This package
    intends to contribute to this popularity and therewith
    keeping the legacy of 'ProTracker' and the Commodore Amiga
    alive.
'ProTracker' is a popular music tracker to sequence
    music on a Commodore Amiga machine. This package offers the
    opportunity to import, export, manipulate and play 'ProTracker'
    module files. Even though the file format could be considered
    archaic, it still remains popular to this date. This package
    intends to contribute to this popularity and therewith
    keeping the legacy of 'ProTracker' and the Commodore Amiga
    alive. This package is the successor of 'ProTrackR' providing
    better performance.
Useful functions and workflows for proteomics quality control and data analysis of both limited proteolysis-coupled mass spectrometry (LiP-MS) (Feng et. al. (2014) <doi:10.1038/nbt.2999>) and regular bottom-up proteomics experiments. Data generated with search tools such as 'Spectronaut', 'MaxQuant' and 'Proteome Discover' can be easily used due to flexibility of functions.
Helps with quality checks, visualizations 
    and analysis of mass spectrometry data, coming from proteomics 
    experiments. The package is developed, tested and used at the Functional 
    Genomics Center Zurich <https://fgcz.ch>. We use this package
    mainly for prototyping, teaching, and having fun with proteomics data.
    But it can also be used to do data analysis for small scale data sets.
Tools for Natural Language Processing in French and texts from Marcel Proust's collection 
  "A La Recherche Du Temps Perdu". The novels contained in this collection are 
  "Du cote de chez Swann ", "A l'ombre des jeunes filles en fleurs","Le Cote de Guermantes", 
  "Sodome et Gomorrhe I et II", "La Prisonniere", "Albertine disparue", and "Le Temps retrouve".
Uses provenance post-execution to help the user understand
    and debug their script by providing functions to look at intermediate steps and
    data values, their forwards and backwards lineage, and to understand the
    steps leading up to warning and error messages. 'provDebugR' uses
    provenance produced by 'rdtLite' (available on CRAN), stored in PROV-JSON format.
Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.
A simple package to grab a Bible proverb corresponding to the
    day of the month.
Inspects provenance collected by the 'rdt' or 'rdtLite' packages, 
    or other tools providing compatible PROV JSON output created by 
    the execution of a script, and find differences between two provenance 
    collections. Factors under examination included the hardware and 
    software used to execute the script, versions of attached libraries, 
    use of global variables, modified inputs and outputs, and changes 
    in main and sourced scripts. Based on detected changes, 'provExplainR' 
    can be used to study how these factors affect the behavior of 
    the script and generate a promising diagnosis of the causes of different 
    script results. More information about 'rdtLite' and associated tools is available 
    at <https://github.com/End-to-end-provenance/> and Barbara Lerner, 
    Emery Boose, and Luis Perez (2018), Using Introspection to Collect 
    Provenance in R, Informatics, <doi:10.3390/informatics5010012>.
Creates and manages a provenance graph corresponding to the 
    provenance created by the 'rdtLite' package, which
    collects provenance from R scripts.  'rdtLite' is available on CRAN.
    The provenance format is an extension of the 
    W3C PROV JSON format (<https://www.w3.org/Submission/2013/SUBM-prov-json-20130424/>).
    The extended JSON provenance format is described 
    in <https://github.com/End-to-end-provenance/ExtendedProvJson>.
R functions to access provenance information collected by 'rdt' or 
    'rdtLite'.  The information is stored inside a 'ProvInfo' object and can be 
    accessed through a collection of functions that will return the requested 
    data. The exact format of the JSON created by 'rdt' and 'rdtLite' is described 
    in <https://github.com/End-to-end-provenance/ExtendedProvJson>.
Reads the provenance collected by the 'rdtLite' or 'rdt' packages,
    or other tools providing compatible PROV JSON output, created by the
    execution of a script or a console session, and provides a human-readable
    summary identifying the input and output files, the scripts used (if any),
    errors and warnings produced, and the environment in which it was executed.
    It can also optionally package all the files into a zip file.  The exact
    format of the PROV JSON file created by 'rdtLite' and 'rdt' is described
    in <https://github.com/End-to-end-provenance/ExtendedProvJson>.  More
    information about 'rdtLite' and associated tools is available at
    <https://github.com/End-to-end-provenance/> and Lerner, Boose, and Perez
    (2018), Using Introspection to Collect Provenance in R, Informatics,
    <doi: 10.3390/informatics5010012>.
Uses provenance collected by 'rdtLite' package or comparable tool
    to display information about input files, output files, and exchanged files
    for a single R script or a series of R scripts.
Displays provenance graphically for provenance collected by the 'rdt' or
  'rdtLite' packages, or other tools providing compatible PROV JSON output. The exact 
  format of the JSON created by 'rdt' and 'rdtLite' is described in 
  <https://github.com/End-to-end-provenance/ExtendedProvJson>.  More information about
  rdtLite and associated tools is available at <https://github.com/End-to-end-provenance/> 
  and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect 
  Provenance in R, Informatics, <doi: 10.3390/informatics5010012>.
Functions to measure Alpha and Beta Proximity to Irreplaceability.
  The methods for Alpha and Beta irreplaceability were first described in:
    Baisero D., Schuster R. & Plumptre A.J.
    Redefining and Mapping Global Irreplaceability.
    Conservation Biology 2021;1-11.
    <doi:10.1111/cobi.13806>.
Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.
Computes proximity between rows or columns of large matrices efficiently in C++.
    Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries.
    Among several built-in similarity/distance measures, computation of correlation,
    cosine similarity and Euclidean distance is particularly fast.
Determine minimal protein set explaining
    peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants.
    Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein
    level. Computing dynamic swath window sizes based on MS1 or MS2 signal distributions.
Utilize the Bayesian prior and posterior predictive checking
   approach to provide a statistical assessment of replication success 
   and failure. The package is based on the methods proposed in 
   Zhao,Y., Wen X.(2021) <arXiv:2105.03993>.
Provides a function to convert 'PRQL' strings to 'SQL' strings.
    Combined with other R functions that take 'SQL' as an argument,
    'PRQL' can be used on R.
Reverse depends for a given package are queued such that multiple
 workers can run the reverse-dependency tests in parallel.
Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) <doi:10.1145/1143844.1143874>; Keilwagen et al. (2014) <doi:10.1371/journal.pone.0092209>; Grau et al. (2015) <doi:10.1093/bioinformatics/btv153>.
Provides a simulation framework to simulate streamflow time series with similar main characteristics as observed data. These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. The approach is based on the randomization of the phases of the Fourier transform or the phases of the wavelet transform. The function prsim() is applicable to single site simulation and uses the Fourier transform. The function prsim.wave() extends the approach to multiple sites and is based on the complex wavelet transform. The function prsim.weather() extends the approach to multiple variables for weather generation. We further use the flexible four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. 
  A detailed description of the simulation approach for single sites and an application example can be found in Brunner et al. (2019) <doi:10.5194/hess-23-3175-2019>.
  A detailed description and evaluation of the wavelet-based multi-site approach can be found in Brunner and Gilleland (2020) <doi:10.5194/hess-24-3967-2020>.
  A detailed description and evaluation of the multi-variable and multi-site weather generator can be found in Brunner et al. (2021) <doi:10.5194/esd-12-621-2021>.
  A detailed description and evaluation of the non-stationary streamflow generator can be found in Brunner and Gilleland (2024) <doi:10.1029/2023EF004238>.
Construct pharmacogenomics (PGx) polygenic risk score (PRS) with PRS-PGx-Unadj (unadjusted), PRS-PGx-CT (clumping and thresholding), PRS-PGx-L, -GL, -SGL (penalized regression), PRS-PGx-Bayes (Bayesian regression). Package is based on ''Pharmacogenomics Polyenic Risk Score for Drug Response Prediction Using PRS-PGx Methods'' by Zhai, S., Zhang, H., Mehrotra, D.V., and Shen, J., 2021 (submitted).
Tests periodicity in short time series using response surface regression.
Intended for larger-than-memory tabular data, 'prt' objects provide an interface to read row and/or column subsets into memory as data.table objects. Data queries, constructed as 'R' expressions, are evaluated using the non-standard evaluation framework provided by 'rlang' and file-backing is powered by the fast and efficient 'fst' package.
Probabilistic Regression Trees (PRTree). Functions for fitting and predicting PRTree models with some adaptations to handle missing values. The main calculations are performed in 'FORTRAN', resulting in highly efficient algorithms.
	This package's implementation is based on the PRTree methodology described in Alkhoury, S.; Devijver, E.; Clausel, M.; Tami, M.; Gaussier, E.; Oppenheim, G. (2020) - "Smooth And Consistent Probabilistic Regression Trees" <https://proceedings.neurips.cc/paper_files/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf>.
Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) <doi:10.1093/sysbio/syaa007>. Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) <doi:10.5061/dryad.j6q573n8s> and Grunstra et al. (2020) <doi:10.5061/dryad.zkh189373>.
Useful tools to pry back the covers of R and understand the
    language at a deeper level.
Data sets and functions used in the polish book 
    "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). 
    See more at <http://biecek.pl/R>. Among others you will find here 
    data about housing prices, cancer patients, running times and many others.
List, query and manipulate all system processes, on
    'Windows', 'Linux' and 'macOS'.
It is often advantageous to test a hypothesis more than once
    in the context of propensity score analysis (Rosenbaum, 2012)
    <doi:10.1093/biomet/ass032>. The functions in this package facilitate
    bootstrapping for propensity score analysis (PSA). By default,
    bootstrapping using two classification tree methods (using 'rpart' and
    'ctree' functions), two matching methods (using 'Matching' and
    'MatchIt' packages), and stratification with logistic regression.  A
    framework is described for users to implement additional propensity
    score methods.  Visualizations are emphasized for diagnosing balance;
    exploring the correlation relationships between bootstrap samples and
    methods; and to summarize results.
A collection of functions that primarily produce graphics to
    aid in a Propensity Score Analysis (PSA).  Functions include: cat.psa
    and box.psa to test balance within strata of categorical and
    quantitative covariates, circ.psa for a representation of the
    estimated effect size by stratum, loess.psa that provides a graphic
    and loess based effect size estimate, and various balance functions
    that provide measures of the balance achieved via a PSA in a
    categorical covariate.
Connects to the API of <https://pushshift.io/> to search for 'Reddit' comments and submissions.
Hybrid control design is a way to borrow information from external controls to augment concurrent controls in a randomized controlled trial and is expected to overcome the feasibility issue when adequate randomized controlled trials cannot be conducted. A major challenge in the hybrid control design is its inability to eliminate a prior-data conflict caused by systematic imbalances in measured or unmeasured confounding factors between patients in the concurrent treatment/control group and external controls. To prevent the prior-data conflict, a combined use of propensity score matching and Bayesian commensurate prior has been proposed in the context of hybrid control design. The propensity score matching is first performed to guarantee the balance in baseline characteristics, and then the Bayesian commensurate prior is constructed while discounting the information based on the similarity in outcomes between the concurrent and external controls. 'psBayesborrow' is a package to implement the propensity score matching and the Bayesian analysis with commensurate prior, as well as to conduct a simulation study to assess operating characteristics of the hybrid control design, where users can choose design parameters in flexible and straightforward ways depending on their own application.
Algorithms to implement various Bayesian penalized survival regression models including: semiparametric proportional hazards models with lasso priors (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and three  other shrinkage and group priors (Lee et al., Stat Anal Data Min, 2015 <doi:10.1002/sam.11266>); parametric accelerated failure time models with group/ordinary lasso prior (Lee et al. Comput Stat Data Anal, 2017 <doi:10.1016/j.csda.2017.02.014>).
Algorithms to speed up the Bayesian Lasso Cox model (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and the Bayesian Lasso Cox with mandatory variables (Zucknick et al. Biometrical J, 2015 <doi:10.1002/bimj.201400160>).
A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.
Bayesian dynamic borrowing is an approach to incorporating external
   data to supplement a randomized, controlled trial analysis in which
   external data are incorporated in a dynamic way (e.g., based on similarity
   of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview.
   This package implements the hierarchical commensurate prior approach to dynamic borrowing
   as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>.
   There are three main functionalities. First, 'psborrow2' provides a user-friendly
   interface for applying dynamic borrowing on the study results handles the Markov Chain
   Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a
   simulation framework to compare different borrowing parameters (e.g. full borrowing, no
   borrowing, dynamic borrowing) and other trial and borrowing characteristics
   (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides
   a set of functions to generate data for simulation studies, and also allows
   the user to specify their own data generation process. This package is designed to
   use the sampling functions from 'cmdstanr' which can be installed from
   <https://stan-dev.r-universe.dev>.
Allows the comparison of data cohorts (DC) against a Counter Factual Model (CFM) and measures the difference in terms of an efficacy parameter.  Allows the application of Personalised Synthetic Controls.
Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome.  Both tumor-normal paired and tumor-only analyses are supported.
Bayesian analysis of item-response theory (IRT) models,
  roll call analysis; computing highest density regions; 
  maximum likelihood estimation of zero-inflated and hurdle models for count data;
  goodness-of-fit measures for GLMs;
  data sets used in writing	and teaching; seats-votes curves.
Provides a number of functions to
  simplify and automate the scoring, comparison, and evaluation of
  different ways of creating composites of data.  It is particularly
  aimed at facilitating the creation of physiological composites of
  metabolic syndrome symptom score (MetSSS) and allostatic load (AL).
  Provides a wrapper to calculate the MetSSS on new data using the
  Healthy Hearts formula.
Provide estimation for particular cases of the power series cure rate model 
             <doi:10.1080/03610918.2011.639971>. For the distribution of the concurrent causes the 
             alternative models are the Poisson, logarithmic, negative binomial and Bernoulli (which 
             are includes in the original work), the polylogarithm model 
             <doi:10.1080/00949655.2018.1451850> and the Flory-Schulz <doi:10.3390/math10244643>. 
             The estimation procedure is based on the EM algorithm discussed in 
             <doi:10.1080/03610918.2016.1202276>.
             For the distribution of the time-to-event the alternative models are slash half-normal, 
             Weibull, gamma and Birnbaum-Saunders distributions.
Produces power spectral density estimates through iterative
    refinement of the optimal number of sine-tapers at each frequency. This
    optimization procedure is based on the method of Riedel and Sidorenko
    (1995), which minimizes the Mean Square Error (sum of variance and bias)
    at each frequency, but modified for computational stability. The same
    procedure can now be used to calculate the cross spectrum (multivariate
    analyses).
This R package includes functions for gathering commonly used and
    regularly maintained data set in political science. It also includes
    functions for combining components from these data sets into variables that
    have been suggested in the literature, but are not regularly maintained.
Presentation of distributions such as: two-piece power normal (TPPN), plasticizing component (PC), DS normal (DSN), expnormal (EN), Sulewski plasticizing component (SPC), easily changeable kurtosis (ECK) distributions. Density, distribution function, quantile function and random generation are presented. For details on this method see: Sulewski (2019) <doi:10.1080/03610926.2019.1674871>, Sulewski (2021) <doi:10.1080/03610926.2020.1837881>, Sulewski (2021) <doi:10.1134/S1995080221120337>, Sulewski (2022) <"New members of the Johnson family of probability dis-tributions: properties and application">, Sulewski, Volodin (2022) <doi:10.1134/S1995080222110270>, Sulewski (2023) <doi:10.17713/ajs.v52i3.1434>.
Functions that allow you to generate and compare power spectral density (PSD) 
	plots given time series data. Fast Fourier Transform (FFT) is used to take a time series 
	data, analyze the oscillations, and then output the frequencies of these oscillations 
	in the time series in the form of a PSD plot.Thus given a time series, the dominant 
	frequencies in the time series can be identified. Additional functions in this package 
	allow the dominant frequencies of multiple groups of time series to be compared with each other. 
	To see example usage with the main functions of this package, please visit
	this site: <https://yhhc2.github.io/psdr/articles/Introduction.html>. 
	The mathematical operations used to generate the PSDs are described in these sites:
	<https://www.mathworks.com/help/matlab/ref/fft.html>.
	<https://www.mathworks.com/help/signal/ug/power-spectral-density-estimates-using-fft.html>.
Various functions for computing pseudo-observations for censored data regression. Computes pseudo-observations for modeling: competing risks based on the cumulative incidence function, survival function based on the restricted mean,  survival function based on the Kaplan-Meier estimator see Klein et al. (2008) <doi:10.1016/j.cmpb.2007.11.017>.
Calculates the lexicogrammatical and functional features described
  by Biber (1985) <doi:10.1515/ling.1985.23.2.337> and widely used for
  text-type, register, and genre classification tasks.
A collection of easy-to-use tools for regression analysis of survival data with a cure fraction proposed in Su et al. (2022) <doi:10.1177/09622802221108579>. The modeling framework is based on the Cox proportional hazards mixture cure model and the bounded cumulative hazard (promotion time cure) model. The pseudo-observations approach is utilized to assess covariate effects and embedded in the variable selection procedure.
Given an arbitrary set of spatial regions and road networks, 
             generate a set of representative points, or pseudohouseholds, that
             can be used for travel burden analysis. Parallel processing is 
             supported.
Contains the core methods for the evaluation of principal
    surrogates in a single clinical trial. Provides a flexible interface for
    defining models for the risk given treatment and the surrogate, the models
    for integration over the missing counterfactual surrogate responses, and the
    estimation methods. Estimated maximum likelihood and pseudo-score can be used
    for estimation, and the bootstrap for inference. A variety of post-estimation
    summary methods are provided, including print, summary, plot, and testing.
Pattern Sequence Based Forecasting (PSF) takes univariate
    time series data as input and assist to forecast its future values.
    This algorithm forecasts the behavior of time series
    based on similarity of pattern sequences. Initially, clustering is done with the
    labeling of samples from database. The labels associated with samples are then
    used for forecasting the future behaviour of time series data. The further
    technical details and references regarding PSF are discussed in Vignette.
Pooling, backward and forward selection of linear, logistic and Cox regression models in 
	multiply imputed datasets. Backward and forward selection can be done 
	from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and 
	the median p-values method. This is also possible for Mixed models. 
	The models can contain continuous, dichotomous, categorical and restricted 
	cubic spline predictors and interaction terms between	all these type of predictors. 
	The stability of the models	can be evaluated using (cluster) bootstrapping. The package 
	further contains functions to pool model performance measures as ROC/AUC, Reclassification, 
	R-squared, scaled Brier score, H&L test and calibration	plots for logistic regression models. 
	Internal validation can be done across multiply imputed datasets with cross-validation or 
	bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients 
	can be obtained. Backward and forward selection as part of internal validation is possible. 
	A function to externally validate logistic prediction models in multiple imputed 
	datasets is available and a function to compare models. For Cox models a strata variable
	can be included.
	Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
	Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
	Marshall (2009) <doi:10.1186/1471-2288-9-57>.
Functions to generate ensembles of generalized linear models using
             a greedy projected subset gradient descent algorithm. The sparsity 
             and diversity tuning parameters are selected by cross-validation.
Presentation of a new goodness-of-fit normality test based on the Lilliefors method. For details on this method see: Sulewski (2019) <doi:10.1080/03610918.2019.1664580>.
Implements projected sparse Gaussian process Kriging (Ingram 'et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the 'intamap' package. More details on implementation (Barillec 'et. al.', 2010, <doi:10.1016/j.cageo.2010.05.008>).
Functions which facilitate harmonization of data from multiple
    different datasets. Data harmonization involves taking data sources with
    differing values, creating coding instructions to create a harmonized
    set of values, then making those data modifications. 'psHarmonize' will
    assist with data modification once the harmonization instructions are
    written. Coding instructions are written by the user to create a
    "harmonization sheet". This sheet catalogs variable names, domains
    (e.g. clinical, behavioral, outcomes), provides R code instructions for
    mapping or conversion of data, specifies the variable name in the
    harmonized data set, and tracks notes. The package will then harmonize
    the source datasets according to the harmonization sheet to create a
    harmonized dataset. Once harmonization is finished, the package also has
    functions that will create descriptive statistics using 'RMarkdown'. Data
    Harmonization guidelines have been described by Fortier I, Raina P,
    Van den Heuvel ER, et al. (2017) <doi:10.1093/ije/dyw075>. Additional
    details of our R package have been described by Stephen JJ, Carolan P,
    Krefman AE, et al. (2024) <doi:10.1016/j.patter.2024.101003>.
In the situation when multiple alternative treatments or
    interventions available, different population groups may respond differently
    to different treatments. This package implements a method that discovers
    the population subgroups in which a certain treatment has a better effect
    than the other alternative treatments. This is done by first estimating the
    treatment effect for a given treatment and its uncertainty by computing random
    forests, and the resulting model is summarized by a decision tree in which the
    probabilities that the given treatment is best for a given subgroup is shown in
    the corresponding terminal node of the tree.
Makes it easy to build panel data in wide format from Panel Survey
    of Income Dynamics (PSID) delivered raw data. Downloads data directly from
    the PSID server using the 'SAScii' package. 'psidR' takes care of merging
    data from each wave onto a cross-period index file, so that individuals can be
    followed over time. The user must specify which years they are interested in,
    and the 'PSID' variable names (e.g. ER21003) for each year (they differ in each
    year). The package offers helper functions to retrieve variable names from different
    waves. There are different panel data designs and sample subsetting criteria
    implemented ("SRC", "SEO", "immigrant" and "latino" samples). More information about 
    the PSID can be obtained at <https://simba.isr.umich.edu/data/data.aspx>.
Streamline the management, creation, and formatting of panel data from the Panel Study of Income Dynamics ('PSID') <https://psidonline.isr.umich.edu> using this user-friendly tool. Simply define variable names and input code book details directly from the 'PSID' official website, and this toolbox will efficiently facilitate the data preparation process, transforming raw 'PSID' files into a well-organized format ready for further analysis.
The Preference Selection Index Method was created in (2010) and provides an innovative approach to determining the relative importance of criteria without pairwise comparisons, unlike the Analytic Hierarchy Process. The Preference Selection Index Method uses statistical methods to calculate the criteria weights and reflects their relative importance in the final decision-making process, offering an objective and non-subjective solution. This method is beneficial in multi-criteria decision analysis. The 'PSIM' package provides a practical and accessible tool for implementing the Preference Selection Index Method in R. It calculates the weights of criteria and makes the method available to researchers, analysts, and professionals without the need to develop complex calculations manually. More details about the Preference Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010) <doi:10.1016/j.matdes.2009.11.020>.
Presentation two independence tests for two-way, three-way and four-way contingency tables. These tests are: the modular test and the logarithmic minimum test. For details on this method see: Sulewski (2017) <doi:10.18778/0208-6018.330.04>, Sulewski (2018) <doi:10.1080/02664763.2018.1424122>, Sulewski (2019) <doi:10.2478/bile-2019-0003>, Sulewski (2021) <doi:10.1080/00949655.2021.1908286>.
Considering the singly imputed synthetic data generated via plug-in sampling under the multivariate normal model, draws inference procedures including the generalized variance, the sphericity test, the test for independence between two subsets of variables, and the test for the regression of one set of variables on the other. For more details see Klein et al. (2021) <doi:10.1007/s13571-019-00215-9>.
Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).
Fits and evaluates three-state partitioned survival analyses
    (PartSAs) and Markov models (clock forward or clock reset) to
    progression and overall survival data typically collected in oncology clinical trials. These model structures are typically considered in
    cost-effectiveness modeling in advanced/metastatic cancer indications.
    Muston (2024). "Informing structural assumptions for three state oncology cost-effectiveness models through model efficiency and fit". Applied Health Economics and Health Policy.
Compute detailed and aggregated performance spectrum for event data. The detailed performance spectrum describes the event data in terms of segments, where the performance of each segment is measured and plotted for any occurrences of this segment over time and can be classified, e.g., regarding the overall population. The aggregated performance spectrum visualises the amount of cases of particular performance over time. Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2018) <doi:10.1007/978-3-319-98648-7_9>.
Provides an implementation of particle swarm optimisation consistent with the standard PSO 2007/2011 by Maurice Clerc. Additionally a number of ancillary routines are provided for easy testing and graphics.
Particle swarm optimization - a basic variant.
Implements an n-dimensional parameter space partitioning algorithm for evaluating the global behaviour of formal computational models as described by Pitt, Kim, Navarro and Myung (2006) <doi:10.1037/0033-295X.113.1.57>.
Estimation and inference of spatial and spatio-temporal 
    semiparametric models including spatial or spatio-temporal non-parametric 
    trends, parametric and non-parametric covariates and, possibly, a spatial 
    lag for the dependent variable and temporal correlation in the noise.
    The spatio-temporal trend can be decomposed in ANOVA way including main and 
    interaction functional terms. Use of SAP algorithm to estimate the spatial 
    or spatio-temporal trend and non-parametric covariates. The methodology of 
    these models can be found in next references
    Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>;
    Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and,
    particularly referred to the focus of the package, Minguez, R., 
    Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.
Spearman's rank correlation test with precomputed exact
        null distribution for n <= 22.
Smoothing splines with penalties on order m derivatives.
Performs demographic, bifurcation and evolutionary analysis of physiologically structured population models, which is a class of models that consistently translates continuous-time models of individual life history to the population level.
    A model of individual life history has to be implemented specifying the individual-level functions that determine the life history, such as development and mortality rates and fecundity. 
    M.A. Kirkilionis, O. Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001) <doi:10.1142/S0218202501001264>.
    O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003) <doi:10.1016/S0040-5809(02)00058-8>.
    A.M. de Roos (2008) <doi:10.1111/j.1461-0248.2007.01121.x>.
Sample size calculations in causal inference with observational data are increasingly desired. This package is a tool to calculate sample size under prespecified power with minimal summary quantities needed.
Provides quasi-Newton methods to minimize partially separable
    functions. The methods are largely described by  
    Nocedal and Wright (2006) <doi:10.1007/978-0-387-40065-5>.
Power and Sample Size for Health Researchers is a Shiny application that brings together a 
    series of functions  related to sample size and power calculations for common analysis in the healthcare 
    field. There are functionalities to calculate the power, sample size to estimate or test hypotheses for means and 
    proportions (including test for correlated groups, equivalence, non-inferiority and superiority), association, correlations coefficients, 
    regression coefficients (linear, logistic, gamma, and Cox), linear mixed model, 
    Cronbach's alpha, interobserver agreement, intraclass correlation coefficients,
    limit of agreement on Bland-Altman plots,
    area under the curve, sensitivity and specificity incorporating the prevalence of disease. 
    You can also use the online version at <https://hcpa-unidade-bioestatistica.shinyapps.io/PSS_Health/>.
Test-based Image structural similarity measure and test of independence. 
    This package implements the key functions of two tasks: (1) computing
    image structural similarity measure PSSIM of Wang, Maldonado and
    Silwal (2011) <DOI:10.1016/j.csda.2011.04.021>;
    and (2) test of independence between a response and 
    a covariate in presence of heteroscedastic treatment effects 
    proposed by Wang, Tolos, and Wang (2010) <DOI:10.1002/cjs.10068>.
Returns almost all features that has been extracted from Position Specific 
             Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length) 
             and 20 columns produced by 'PSI-BLAST' which is a program to produce
             PSSM Matrix from multiple sequence alignment of proteins
             see <https://www.ncbi.nlm.nih.gov/books/NBK2590/> for mor details. some 
             of these features are described in Zahiri, J., et al.(2013)
             <DOI:10.1016/j.ygeno.2013.05.006>,
             Saini, H., et al.(2016)
             <DOI:10.17706/jsw.11.8.756-767>,
             Ding, S., et al.(2014)
             <DOI:10.1016/j.biochi.2013.09.013>,
             Cheng, C.W., et al.(2008)
             <DOI:10.1186/1471-2105-9-S12-S6>,
             Juan, E.Y., et al.(2009)
             <DOI:10.1109/CISIS.2009.194>.
Implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560 <doi:10.1093/biostatistics/kxy074>. The methods avoid the restrictive 'placebo structural risk' modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results.
A network-based systems biology tool for flexible identification of phenotype-specific subpathways in the cancer gene expression data 
  with multiple categories (such as multiple subtype or developmental stages of cancer). Subtype Set Enrichment Analysis (SubSEA) and Dynamic Changed 
  Subpathway Analysis (DCSA) are developed to flexible identify subtype specific and dynamic changed subpathways respectively. The operation modes 
  include extraction of subpathways from biological pathways, inference of subpathway activities in the context of gene expression data, identification 
  of subtype specific subpathways with SubSEA, identification of dynamic changed subpathways associated with the cancer developmental stage with DCSA, 
  and visualization of the activities of resulting subpathways by using box plots and heat maps. Its capabilities render the tool could find the specific
  abnormal subpathways in the cancer dataset with multi-phenotype samples.
Provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The  library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support.
Calculating Pst values to assess differentiation among populations from a set of quantitative traits is the primary purpose of such a package. The bootstrap method provides confidence intervals and distribution histograms of Pst. Variations of Pst in function of the parameter c/h^2 are studied as well. Finally, the package proposes different transformations especially to eliminate any variation resulting from allometric growth (calculation of residuals from linear regressions, Reist standardizations or Aitchison transformation).
The propensity score is one of the most widely used tools in
    studying the causal effect of a treatment, intervention, or policy. Given that
    the propensity score is usually unknown, it has to be estimated, implying that
    the reliability of many treatment effect estimators depends on the correct
    specification of the (parametric) propensity score. This package implements the
    data-driven nonparametric diagnostic tools for detecting propensity score
    misspecification proposed by Sant'Anna and Song (2019) <doi:10.1016/j.jeconom.2019.02.002>.
Estimating causal effects in the presence of post-treatment confounding using principal stratification. 'PStrata' allows for customized monotonicity assumptions and exclusion restriction assumptions, with automatic full Bayesian inference supported by 'Stan'. The main function to use in this package is PStrata(), which provides posterior estimates of principal causal effect with uncertainty quantification. Visualization tools are also provided for diagnosis and interpretation. See Liu and Li (2023) <arXiv:2304.02740> for details.
For a data matrix with m rows and n columns (m>=n), the power
	     method is used to compute, simultaneously, the eigendecomposition
	     of a square symmetric matrix. This result is used to obtain the
	     singular value decomposition (SVD) and the principal component
             analysis (PCA) results. Compared to the classical SVD method,
	     the first r singular values can be computed.
A unified and user-friendly framework for applying the principal sufficient dimension reduction methods for both linear and nonlinear cases. The package has an extendable power by varying loss functions for the support vector machine, even for an user-defined arbitrary function, unless those are convex and differentiable everywhere over the support (Li et al. (2011) <doi:10.1214/11-AOS932>). Also, it provides a real-time sufficient dimension reduction update procedure using the principal least squares support vector machine (Artemiou et al. (2021) <doi:10.1016/j.patcog.2020.107768>).
Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) augmented estimation of treatment effect with outcome regression. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) <DOI:10.1111/1468-0262.00442>, Lunceford and Davidian (2004) <DOI:10.1002/sim.1903>, Li and Greene (2013) <DOI:10.1515/ijb-2012-0030>, and Li et al (2016) <DOI:10.1080/01621459.2016.1260466>.
Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) <doi:10.1080/01621459.2016.1260466> and Li and Li (2019) <doi:10.1214/19-AOAS1282>.
Kappa, ICC, reliability coefficient, parallel analysis, 
     multi-traits multi-methods, spherical representation of a correlation matrix.
Useful for preparing and cleaning data. It includes functions to center data, reverse coding, dummy code and effect code data, and more.
A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology.  Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using  factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis.  Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions  serve as a useful front end for structural equation modeling.  Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.
Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.
The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, 
   to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output 
   of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity.
Contains functions useful for correlation theory, 
    meta-analysis (validity-generalization), reliability, 
    item analysis, inter-rater reliability, and classical utility.
Psychometric mixture models based on 'flexmix' infrastructure. At the moment Rasch mixture models
  with different parameterizations of the score distribution (saturated vs. mean/variance specification),
  Bradley-Terry mixture models, and MPT mixture models are implemented. These mixture models can be estimated
  with or without concomitant variables. See Frick et al. (2012) <doi:10.18637/jss.v048.i07> and
  Frick et al. (2015) <doi:10.1177/0013164414536183> for details on the Rasch mixture models.
Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.
Infrastructure for psychometric modeling such as data classes (for
  item response data and paired comparisons), basic model fitting functions (for
  Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit,
  rating scale, multinomial processing tree models), extractor functions for
  different types of parameters (item, person, threshold, discrimination,
  guessing, upper asymptotes), unified inference and visualizations, and various
  datasets for illustration.  Intended as a common lightweight and efficient
  toolbox for psychometric modeling and a common building block for fitting
  psychometric mixture models in package "psychomix" and trees based on
  psychometric models in package "psychotree".
Recursive partitioning based on psychometric models,
  employing the general MOB algorithm (from package partykit) to obtain
  Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and
  MPT trees, trees for 1PL, 2PL, 3PL and 4PL models and generalized partial 
  credit models.
Helper functions for producing reports in Psychology (Reproducible Research). Provides required formatted strings (APA style) for use in 'Knitr'/'Latex' integration within *.Rnw files.
Implementation of 'PsychroLib'
    <https://github.com/psychrometrics/psychrolib> library which contains
    functions to enable the calculation properties of moist and dry air in both
    metric (SI) and imperial (IP) systems of units. References: Meyer, D. and
    Thevenard, D (2019) <doi:10.21105/joss.01137>.
Support functions,  data sets, and vignettes for the 'psych' package. Contains several of the biggest data sets for the 'psych' package as well as four vignettes. A few helper functions for file manipulation are included as well. For more information, see the <https://personality-project.org/r/> web page.
An integrative toolbox of word embedding research that provides:
    (1) a collection of 'pre-trained' static word vectors in the '.RData'
    compressed format <https://psychbruce.github.io/WordVector_RData.pdf>;
    (2) a series of functions to process, analyze, and visualize word vectors;
    (3) a range of tests to examine conceptual associations, including
    the Word Embedding Association Test <doi:10.1126/science.aal4230>
    and the Relative Norm Distance <doi:10.1073/pnas.1720347115>,
    with permutation test of significance;
    (4) a set of training methods to locally train (static) word vectors
    from text corpora, including 'Word2Vec' <arXiv:1301.3781>,
    'GloVe' <doi:10.3115/v1/D14-1162>, and 'FastText' <arXiv:1607.04606>;
    (5) a group of functions to download 'pre-trained' language models
    (e.g., 'GPT', 'BERT') and extract contextualized (dynamic) word vectors
    (based on the R package 'text').
A beginner-friendly R package for modeling in psychology or
    related field. It allows fitting models, plotting, checking goodness
    of fit, and model assumption violations all in one place. It also
    produces beautiful and easy-to-read output.
Person fit statistics based on Quality Control measures are provided for questionnaires and tests given a specified IRT model. Statistics based on Cumulative Sum (CUSUM) charts are provided. Options are given for banks with polytomous and dichotomous data.
Data and examples from meta-analyses in psychology research.
Provides functions and data-sets that are helpful 
  for teaching statistics and data analysis. It was originally designed for use
  when teaching students in the Psychology Department at Nottingham Trent University.
An assortment of functions that could be useful in analyzing data from psychophysical experiments. It includes functions for calculating d' from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations.
The constructs used to study the human psychology have
    many definitions and corresponding instructions for eliciting
    and coding qualitative data pertaining to constructs' content and
    for measuring the constructs. This plethora of definitions and
    instructions necessitates unequivocal reference to specific
    definitions and instructions in empirical and secondary research.
    This package implements a human- and machine-readable standard for
    specifying construct definitions and instructions for measurement
    and qualitative research based on 'YAML'. This standard facilitates
    systematic unequivocal reference to specific construct definitions
    and corresponding instructions in a decentralized manner (i.e.
    without requiring central curation; Peters (2020)
    <doi:10.31234/osf.io/xebhn>).
Tabular data from statistical institutes and agencies are mostly 
    confidential and must be protected prior to publications. The cell-key 
    method is a post-tabular Statistical Disclosure Control perturbation 
    technique that adds random noise to tabular data. The statistical 
    properties of the perturbations are defined by some noise probability 
    distributions - also referred to as perturbation tables. 
    This tool can be used to create the perturbation tables based on a maximum 
    entropy approach as described for example in Giessing (2016) 
    <doi:10.1007/978-3-319-45381-1_18>. The perturbation tables created can 
    finally be used to apply a cell-key method to frequency count or magnitude 
    tables.
A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions.
We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant?
Evaluates the strength of a surrogate marker by estimating the proportion of treatment effect explained (PTE) and relative power(RP) for the optimally-transformed version of the surrogate.  Details available in Wang et al (2022) <arXiv:2209.08414>.
Generic code for estimating treatment effects with panel data.  The idea is to break into separate steps organizing the data, looping over groups and time periods, computing group-time average treatment effects, and aggregating group-time average treatment effects.  Often, one is able to implement a new identification/estimation procedure by simply replacing the step on estimating group-time average treatment effects.  See several different examples of this approach in the package documentation.
Function pip3d() tests whether a point in 3D space is
  within, exactly on, or outside an enclosed surface defined by a triangular mesh.
  Function pip2d() tests whether a point in 2D space is within, exactly on, or outside a polygon.
  For a reference, see: Liu et al., A new point containment test algorithm based on preprocessing
  and determining triangles, Computer-Aided Design 42(12):1143-1150.
Permute treatment labels for taxa and environmental gradients to
    generate an empirical distribution of change points.  This is an extension
    for the 'TITAN2' package <https://cran.r-project.org/package=TITAN2>.
Contains utilities for the analysis of post-translational modifications (PTMs) in proteins, 
    with particular emphasis on the sulfoxidation of methionine residues. Features include the ability to download, 
    filter and analyze data from the sulfoxidation database 'MetOSite'. Utilities to search and characterize S-aromatic motifs in proteins are also provided. 
    In addition, functions to analyze sequence environments around modifiable residues in proteins can be found. 
    For instance, 'ptm' allows to search for amino acids either overrepresented or avoided around the modifiable 
    residues from the proteins of interest. Functions tailored to test statistical hypothesis related to
    these differential sequence environments are also implemented. 
    Further and detailed information regarding the methods in this package can be found in (Aledo (2020) <https://metositeptm.com>).
Fits the Poisson-Tweedie generalized linear mixed model
    described in Signorelli et al. (2021, <doi:10.1177/1471082X20936017>).
    Likelihood approximation based on adaptive Gauss Hermite quadrature
    rule.
Functions used for analyzing count data, mostly crime counts. Includes checking difference in two Poisson counts (e-test), checking the fit for a Poisson distribution, small sample tests for counts in bins, Weighted Displacement Difference test (Wheeler and Ratcliffe, 2018) <doi:10.1186/s40163-018-0085-5>, to evaluate crime changes over time in treated/control areas. Additionally includes functions for aggregating spatial data and spatial feature engineering.
Fits and analyses time dependent marked point process models with an emphasis on earthquake modelling. For a more detailed introduction to the package, see the topic "PtProcess". A list of recent changes can be found in the topic "Change Log".
Utility functions produced specifically for (but not limited
    to) working with 'ProjectTemplate' data pipelines. This package helps
    to quickly create and manage sequentially numbered scripts, quickly
    set up logging with 'log4r' and functions to help debug and monitor
    procedures.
A collection of functions to simulate, estimate and forecast a wide range of regression based dynamic models for positive time series. 
	This package implements the results presented in Prass, T.S.; Carlos, J.H.; Taufemback, C.G. and Pumi, G. (2022). "Positive Time Series Regression" <arXiv:2201.03667>.
Includes a collection of functions presented in "Measuring stability in ecological systems without static equilibria" by Clark et al. (2022) <doi:10.1002/ecs2.4328> in Ecosphere.
	These can be used to estimate the parameters of a stochastic state space model (i.e. a model where
	a time series is observed with error). The goal of this package is to estimate the variability
	around a deterministic process, both in terms of observation error - i.e. variability due to
	imperfect observations that does not influence system state - and in terms of process noise - i.e.
	stochastic variation in the actual state of the process. Unlike classical methods for estimating
	variability, this package does not necessarily assume that the deterministic state is fixed (i.e.
	a fixed-point equilibrium), meaning that variability around a dynamic trajectory can be estimated
	(e.g. stochastic fluctuations during predator-prey dynamics).
Access the 'Public Transport Victoria' Timetable API 
    <https://www.ptv.vic.gov.au/footer/data-and-reporting/datasets/ptv-timetable-api/>,
    with results returned as familiar R data structures. Retrieve information on
    stops, routes, disruptions, departures, and more.
Parametric Time Warping aligns patterns, i.e. it aims to
        put corresponding features at the same locations. The algorithm
        searches for an optimal polynomial describing the warping. It
        is possible to align one sample to a reference, several samples
        to the same reference, or several samples to several
        references. One can choose between calculating individual
        warpings, or one global warping for a set of samples and one
        reference. Two optimization criteria are implemented: RMS (Root
        Mean Square error) and WCC (Weighted Cross Correlation). Both
	warping of peak profiles and of peak lists are supported. A
	vignette for the latter is contained in the inst/doc directory
	of the source package - the vignette source can be found on
	the package github site.
Contains a dataset of words used in 15.000 randomly extracted pages
    from the Portuguese Wikipedia (<https://pt.wikipedia.org/>).
Package to Percentile estimation of fetal weight for twins by chorionicity (dichorionic-diamniotic or monochorionic-diamniotic).
Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the 
             MaxQuant software suite (from .txt files) or mzTab files (ideally from OpenMS 'QualityControl' tool).
             Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format.
             Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015)
             <doi:10.1021/acs.jproteome.5b00780>.
Bayesian variable selection for linear regression models using hierarchical
  priors. There is a prior that combines information across responses and one
  that combines information across covariates, as well as a standard spike and
  slab prior for comparison. An MCMC samples from the marginal posterior
  distribution for the 0-1 variables indicating if each covariate belongs to the
  model for each response.
Provides an interface to the 'PubChem' database via the PUG REST <https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest> and 
            PUG View <https://pubchem.ncbi.nlm.nih.gov/docs/pug-view> services. This package allows users to automatically 
            access chemical and biological data from 'PubChem', including compounds, substances, assays, and various other data types. 
            Functions are available to retrieve data in different formats, perform searches, and access detailed annotations.
A toolbox for making R functions and capabilities more
    accessible to students and professionals from Epidemiology and
    Public Health related disciplines. Includes a function to report 
    coefficients and confidence intervals from models using robust
    standard errors (when available), functions that expand 'ggplot2'
    plots and functions relevant for introductory papers in Epidemiology 
    or Public Health. Please note that use of the 
    provided data sets is for educational purposes only.
These are harmonized datasets produced as part of the Clinical 
    Trials Network (CTN) protocol number 0094. This is a US National Institute
    of Drug Abuse (NIDA) funded project; to learn more go to
    <https://ctnlibrary.org/protocol/ctn0094/>. These are datasets which have
    the data harmonized from CTN-0027 (<https://ctnlibrary.org/protocol/ctn0027/>),
    CTN-0030 (<https://ctnlibrary.org/protocol/ctn0030/>), and CTN-0051 
    (<https://ctnlibrary.org/protocol/ctn0051/>).
Engineered features and "helper" functions ancillary to the
    'public.ctn0094data' package, extending this package for ease of use (see 
    <https://CRAN.R-project.org/package=public.ctn0094data>). This
    'public.ctn0094data' package contains harmonized datasets from some of the
    National Institute of Drug Abuse's Clinical Trials Network (NIDA's CTN)
    projects. Specifically, the CTN-0094 project is to harmonize and de-identify
    clinical trials data from the CTN-0027, CTN-0030, and CTN-51 studies for
    opioid use disorder. This current version is built from 'public.ctn0094data'
    v. 1.0.6.
Performs sensitivity analysis for publication bias in meta-analyses
    (per Mathur & VanderWeele, 2020 [<doi:10.31219/osf.io/s9dp6>]). These analyses
    enable statements such as: "For publication bias to shift the observed point
    estimate to the null, 'significant' results would need to be at least
    30-fold more likely to be published than negative or 'nonsignificant'
    results." Comparable statements can be made regarding shifting to a chosen
    non-null value or shifting the confidence interval. Provides a worst-case
    meta-analytic point estimate under maximal publication bias obtained simply
    by conducting a standard meta-analysis of only the negative and
    "nonsignificant" studies.
Facilitates the retrieval and analysis of financial data related to public works in Italy, 
    focusing on soil defense investments. It extracts data from 'OpenCoesione', 'OpenBDAP', 
    and the 'ReNDiS' database, eliminating the need for direct access to these platforms.
    The package boasts a user-friendly design, featuring real time updates and a set of functions 
    tailored for data retrieval and visualization.
    See the webpages for further information
    <http://www.rendis.isprambiente.it/rendisweb/>,
    <https://opencoesione.gov.it/en/>, and    
    <https://bdap-opendata.rgs.mef.gov.it/>.
Tools for Bayesian estimation of meta-analysis models that account
    for publications bias or p-hacking. For publication bias, this package
    implements a variant of the p-value based selection model of Hedges (1992)
    <doi:10.1214/ss/1177011364> with discrete selection probabilities. It also
    implements the mixture of truncated normals model for p-hacking described
    in Moss and De Bin (2019) <arXiv:1911.12445>.
A bunch of convenience functions that transform the results of some basic statistical analyses
       into table format nearly ready for publication. This includes descriptive tables, tables of
       logistic regression and Cox regression results as well as forest plots.
Text mining of PubMed Abstracts (text and XML) from <https://pubmed.ncbi.nlm.nih.gov/>.
Easy function for text-mining the 'PubMed' repository based on defined sets of terms.
    The relationship between fix-terms (related to your research topic) and pub-terms (terms which pivot around your research focus)
    is calculated using the pointwise mutual information algorithm ('PMI'). Church, Kenneth Ward and Hanks, Patrick (1990) <https://www.aclweb.org/anthology/J90-1003/>
    A text file is generated with the 'PMI'-scores for each fix-term. Then for each collocation pairs (a fix-term + a pub-term),
    a text file is generated with related article titles and publishing years. Additional Author section will follow in the next version updates.
A set of tools to extract bibliographic content from 'PubMed' database using 'NCBI' REST API <https://www.ncbi.nlm.nih.gov/home/develop/api/>.
Provides various functions for retrieving and
    interpreting information from 'Pubmed' via the API,
    <https://www.ncbi.nlm.nih.gov/home/develop/api/>.
Create a word cloud using the abstract of publications from 'Pubmed'.
'PubTator' <https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/> is a National Center for Biotechnology Information (NCBI) tool that enhances the annotation of articles on PubMed <https://www.ncbi.nlm.nih.gov/pubmed/>. It makes it possible to rapidly identify potential relationships between genes or proteins using text mining techniques. In contrast, manually searching for and reading the annotated articles would be very time consuming. 'PubTator' offers both an online interface and a RESTful API, however, neither of these approaches are well suited for frequent, high-throughput analyses. The package 'pubtatordb' provides a set of functions that make it easy for the average R user to download 'PubTator' annotations, create, and then query a local version of the database.
Provides function declarations and inline function definitions that
    facilitate cleaning strings in C++ code before passing them to R.
Finite Gaussian mixture models with parsimonious extended ultrametric covariance structures estimated via a grouped coordinate ascent algorithm, which is equivalent to the Expectation-Maximization algorithm. The thirteen ultrametric covariance structures implemented allow for the inspection of different hierarchical relationships among variables. The estimation of an ultrametric correlation matrix is included as a function. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) <doi:10.1007/s11222-024-10405-9>, Cavicchia, Vichi, Zaccaria (2022) <doi:10.1007/s11634-021-00488-x> and Cavicchia, Vichi, Zaccaria (2020) <doi:10.1007/s11634-020-00400-z>.
Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <arXiv:1711.08129>.
R Interface to Pullword Service for natural language processing
    in Chinese. It enables users to extract valuable words from text by deep learning models. 
    For more details please visit the official site (in Chinese) <http://www.pullword.com/>.
A method of clustering functional data using
    subregion information of the curves. It is intended to supplement the
    'fda' and 'fda.usc' packages in functional data object clustering. It
    also facilitates the printing and plotting of the results in a tree
    format and limits the partitioning candidates into a specific set of
    subregions.
Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS). Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) <arXiv:1605.07072>.
R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).
Estimates power, minimum detectable effect size (MDES) and sample size requirements. The context is multilevel randomized experiments with multiple outcomes. The estimation takes into account the use of multiple testing procedures. Development of this package was supported by a grant from the Institute of Education Sciences (R305D170030). For a full package description, including a detailed technical appendix, see <doi:10.18637/jss.v108.i06>.
Contains model fitting functions for linear and non-linear adsorption kinetic and diffusion models. Adsorption kinetics is used for characterizing the rate of solute adsorption and the time necessary for the adsorption process. Adsorption kinetics offers vital information on adsorption rate, adsorbent performance in response time, and mass transfer processes. In addition, diffusion models are included in the package as solute diffusion affects the adsorption kinetic experiments. This package consists of 20 adsorption and diffusion models, including Pseudo First Order (PFO), Pseudo Second Order (PSO), Elovich, and Weber-Morris model (commonly called the intraparticle model) stated by Plazinski et al. (2009) <doi:10.1016/j.cis.2009.07.009>. This package also contains a summary function where the statistical errors of each model are ranked for a more straightforward determination of the best fit model.
Provides a unified pipeline to clean, prepare, plot,
    and run basic analyses on pupillometry experiments.
Pupillometric data collected using SR Research Eyelink eye trackers 
    requires significant preprocessing. This package contains functions for 
    preparing pupil dilation data for visualization and statistical analysis. 
    Specifically, it provides a pipeline of functions which aid in data validation,
    the removal of blinks/artifacts, downsampling, and baselining, among others. 
    Additionally, plotting functions for creating grand average and conditional 
    average plots are provided. See the vignette for samples of the functionality.
    The package is designed for handling data collected with SR Research 
    Eyelink eye trackers using Sample Reports created in SR Research Data 
    Viewer.
Image-based color matching using the "Mycological Colour Chart" by Rayner (1970, ISBN:9780851980263) and its associated fungal pigments. This package will assist mycologists in identifying color during morphological analysis.
Contains sixteen moisture sorption isotherm models, which evaluate the fitness of adsorption and desorption curves for further understanding of the relationship between moisture content and water activity. Fitness evaluation is conducted through parameter estimation and error analysis. Moreover, graphical representation, hysteresis area estimation, and isotherm classification through the equation of Blahovec & Yanniotis (2009) <doi:10.1016/j.jfoodeng.2008.08.007> which is based on the classification system introduced by Brunauer et. al. (1940) <doi:10.1021/ja01864a025> are also included for the visualization of models and hysteresis.
Access a variety of 'PubMed' data through a single, user-friendly interface, including abstracts <https://pubmed.ncbi.nlm.nih.gov/>, bibliometrics from 'iCite' <https://icite.od.nih.gov/>, pubtations from 'PubTator3' <https://www.ncbi.nlm.nih.gov/research/pubtator3/>, and full-text records from 'PMC' <https://www.ncbi.nlm.nih.gov/pmc/>.
Proteins reside in either the cell plasma or in the
    cell membrane. A membrane protein goes through the 
    membrane at least once. Given the amino acid sequence of a
    membrane protein, the tool
    'PureseqTM' (<https://github.com/PureseqTM/pureseqTM_package>,
    as described in "Efficient And Accurate Prediction Of Transmembrane 
    Topology From Amino acid sequence only.", Wang, Qing, et al (2019), 
    <doi:10.1101/627307>),
    can predict the topology of a membrane protein. This package
    allows one to use 'PureseqTM' from R.
Enables the removal of training data from fitted R models while
    retaining predict functionality. The purged models are more portable as their
    memory footprints do not scale with the training sample size.
Inbreeding-purging analysis of pedigreed populations, including the computation of the inbreeding coefficient, partial, ancestral and purged inbreeding coefficients, and measures of the opportunity of purging related to the individual reduction of inbreeding load.
    In addition, functions to calculate the effective population size and other parameters relevant to population genetics are included.
    See López-Cortegano E. (2021) <doi:10.1093/bioinformatics/btab599>.
Simple method of purging independent variables of mediating effects. First, regress the direct variable on the indirect variable. Then, used the stored residuals as the new purged (direct) variable in the updated specification. This purging process allows for use of a new direct variable uncorrelated with the indirect variable. Please cite the method and/or package using Waggoner, Philip D. (2018) <doi:10.1177/1532673X18759644>.
Send requests to the 'PurpleAir' Application Programming Interface (API; <https://community.purpleair.com/c/data/api/18>). Check a 'PurpleAir' API key and get information about the related organization. Download real-time data from a single 'PurpleAir' sensor or many sensors by sensor identifier, geographical bounding box, or time since modified. Download historical data from a single sensor.
Provides tools for retrieving and analyzing air quality data
    from 'PurpleAir' sensors through their API. Functions enable downloading
    historical measurements, accessing sensor metadata, and managing API
    request limitations through chunked data retrieval. For more information 
    about the 'PurpleAir' API, see <https://api.purpleair.com/>.
A complete and consistent functional programming toolkit for
    R.
Some functions at the intersection of 'dplyr' and
    'purrr' that formerly lived in 'purrr'.
Projection pursuit (PP) with 17 methods and grand tour with 3 methods. Being that projection pursuit searches for low-dimensional linear projections in high-dimensional data structures, while grand tour is a technique used to explore multivariate statistical data through animation.
Create sliders from left, right, top and bottom which may include any html or 'Shiny' input or output.
Send push notifications to mobile devices or the desktop
    using 'Pushover' <https://pushover.net>. These notifications can
    display things such as results, job status, plots, or any other text
    or numeric data.
To Simplify the time consuming and error prone task of assembling complex data sets for non-linear mixed effects modeling. Users are able to select from different absorption processes such as zero and first order, or a combination of both. Furthermore, data sets containing data from several entities, responses, and covariates can be simultaneously assembled.
Likelihood based population viability analysis in the
  presence of observation error and missing data.
  The package can be used to fit, compare, predict,
  and forecast various growth model types using data cloning.
Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) <doi:10.1002/sim.8293>; Poole C. (1987) <doi:10.2105/AJPH.77.2.195>; Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>; Bender R, Berg G, Zeeb H. (2005) <doi:10.1002/bimj.200410104> ; Singh K, Xie M, Strawderman WE. (2007) <doi:10.1214/074921707000000102>; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>; Greenland S. (2019) <doi:10.1080/00031305.2018.1529625> and Rafi Z, Greenland S. (2020) <doi:10.1186/s12874-020-01105-9>.
The calculation of p-variation of the finite sample data. 
	This package is a realisation of the procedure described in Butkus, V. & Norvaisa, R. Lith Math J (2018). <doi: 10.1007/s10986-018-9414-3>
	The formal definitions and reference into literature are given in vignette.
Computes nonparametric p-values for the potential class
        memberships of new observations as well as cross-validated
        p-values for the training data. The p-values are based on
        permutation tests applied to an estimated Bayesian likelihood
        ratio, using a plug-in statistic for the Gaussian model, 'k
        nearest neighbors', 'weighted nearest neighbors' or
        'penalized logistic regression'.
        Additionally, it provides graphical displays and quantitative
        analyses of the p-values.
An implementation of multiscale bootstrap resampling for
             assessing the uncertainty in hierarchical cluster analysis.
             It provides SI (selective inference) p-value, AU (approximately unbiased)
             p-value and BP (bootstrap probability) value for each cluster in a dendrogram.
Enables the manufacturing, analysis and display of pressure volume curves. From the progression of the curves, turgor loss point, osmotic potential and apoplastic fraction can be derived. Methods adapted from Bartlett, Scoffoni and Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x>.
Tools for performing disproportionality analysis using the information component, proportional reporting rate and the reporting odds ratio. The anticipated use is passing data to the da() function, which executes the disproportionality analysis. See Norén et al (2011) <doi:10.1177/0962280211403604> and Montastruc et al (2011) <doi:10.1111/j.1365-2125.2011.04037.x> for further details.
Simplifies the manufacturing, analysis and display of pressure volume and leaf drying curves. From the progression of the curves turgor loss point, osmotic potential, apoplastic fraction as well as minimum conductance and stomatal closure can be derived. Methods adapted from Bartlett, Scoffoni, Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x> and Sack, Scoffoni, PrometheusWikiContributors (2011) <http://prometheuswiki.org/tiki-index.php?page=Minimum+epidermal+conductance+%28gmin%2C+a.k.a.+cuticular+conductance%29>.
A suite of likelihood ratio test based methods to use in pharmacovigilance. Contains various testing and post-processing functions.
Estimates (and controls for) phylogenetic signal through phylogenetic eigenvectors regression (PVR) and phylogenetic signal-representation (PSR) curve, along with some plot utilities.
Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project of the same name - 'PvSTATEM', which is an international project aiming for malaria elimination.
Calculates various functions needed for design and monitoring survival trials
    accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual,
    and different censoring distributions between groups. The event time distribution is assumed to be
    piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution.
    As compared with Version 1.2.1, two more types of hybrid crossover are added. 
    A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, 
    density, hazard and cumulative hazard functions. 
    Also, to generate the crossover-adjusted event time random variable,  a more efficient 
    algorithm is used and the output includes crossover indicators.
Price volatility refers to the degree of variation in series over a certain period of time. This volatility is especially noticeable in agricultural commodities, adding uncertainty for farmers, traders, and others in the agricultural supply chain. Commonly and popularly used four volatility models viz, GARCH, Glosten Jagannatan Runkle-GARCH (GJR-GARCH) model, exponentially weighted moving average (EWMA) model and Multiplicative Error Model (MEM) are selected and implemented. PWAVE, weighted ensemble model based on particle swarm optimization (PSO) is proposed to combine the forecast obtained from all the candidate models. This package has been developed using algorithm of Paul et al. <doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.
Build piecewise exponential survival model for study design (planning) and event/timeline prediction.
A function 'PWI()' that calculates prize winner indices based on bibliometric data is provided. The default is the 'Derek de Solla Price Memorial Medal'. Users can provide recipients of other prizes.
Estimates two-level multilevel linear model and two-level multivariate linear multilevel model with weights following Probability Weighted Iterative Generalised Least Squares approach. For details see Veiga et al.(2014) <doi:10.1111/rssc.12020>.
Power analysis functions along the lines of Cohen (1988).
User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers.
Statistical power analysis for designs including t-tests, correlations, 
    multiple regression, ANOVA, mediation, and logistic regression. Functions accompany 
    Aberson (2019) <doi:10.4324/9781315171500>.
Provides tools for calculating statistical power and determining sample size
  for a variety of experimental designs used in agricultural and biological research,
  including completely randomized, block, and split-plot designs. Supports customized
  designs and allows specification of main effects, interactions, and contrasts for
  accurate power analysis.
Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test,
  which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are
  unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give 
  biased results due to using the pooled standard deviation, unlike the Welch's t-test.
Tools for the evaluation of interim analysis plans for sequentially
 monitored trials on a survival endpoint; tools to construct efficacy and 
 futility boundaries, for deriving power of a sequential design at a specified
 alternative, template for evaluating the performance of candidate plans at a 
 set of time varying alternatives. See Izmirlian, G. (2014) <doi:10.4310/SII.2014.v7.n1.a4>.
Statistical power simulation for testing the Rasch Model based on a three-way analysis of variance design with mixed classification.
Statistical power and minimum required sample size calculations for (1) testing a proportion (one-sample) against a constant,  (2) testing a mean (one-sample) against a constant,  (3) testing difference between two proportions (independent samples),  (4) testing difference between two means or groups (parametric and non-parametric tests for independent and paired samples),  (5) testing a correlation (one-sample) against a constant,  (6) testing difference between two correlations (independent samples), (7) testing a single coefficient in multiple linear regression, logistic regression, and Poisson regression (with standardized or unstandardized coefficients, with no covariates or covariate adjusted), (8) testing an indirect effect (with standardized or unstandardized coefficients, with no covariates or covariate adjusted) in the mediation analysis (Sobel, Joint, and Monte Carlo tests), (9) testing an R-squared against zero in linear regression, (10) testing an R-squared difference against zero in hierarchical regression,  (11) testing an eta-squared or f-squared (for main and interaction effects) against zero in analysis of variance (could be one-way, two-way, and three-way),  (12) testing an eta-squared or f-squared (for main and interaction effects) against zero in analysis of covariance (could be one-way, two-way, and three-way),  (13) testing an eta-squared or f-squared (for between, within, and interaction effects) against zero in one-way repeated measures analysis of variance (with non-sphericity correction and repeated measures correlation), and (14) testing goodness-of-fit or independence for contingency tables. Alternative hypothesis can be formulated as "not equal", "less", "greater", "non-inferior", "superior", or "equivalent" in (1), (2), (3), and (4); as "not equal", "less", or "greater" in (5), (6), (7) and (8); but always as "greater" in (9), (10), (11), (12), (13), and (14). Reference: Bulus and Polat (2023) <https://osf.io/ua5fc>.
The Penn World Table provides purchasing power parity and
	national income accounts converted to international prices for
	189 countries for some or all of the years 1950-2010.
The Penn World Table 10.x (<https://www.rug.nl/ggdc/productivity/pwt/>) provides information
	on relative levels of income, output, input, and productivity
	for 183 countries between 1950 and 2019.
The Penn World Table 8.x provides information on relative levels of
	income, output, inputs, and productivity for 167 countries
	between 1950 and 2011.
The Penn World Table 9.x (<http://www.ggdc.net/pwt/>) provides information
	on relative levels of income, output, inputs, and productivity
	for 182 countries between 1950 and 2017.
Create PX-files from scratch or read and modify existing ones. 
    Includes a function for every PX keyword, making metadata manipulation
    simple and human-readable.
Provides a set of functions for reading and writing PC-Axis files, used by different statistical organizations around the globe for data dissemination.
Generic interface for the PX-Web/PC-Axis API. The
    PX-Web/PC-Axis API is used by organizations such as Statistics Sweden
    and Statistics Finland to disseminate data. The R package can interact
    with all PX-Web/PC-Axis APIs to fetch information about the data
    hierarchy, extract metadata and extract and parse statistics to R
    data.frame format. PX-Web is a solution to disseminate PC-Axis data
    files in dynamic tables on the web.  Since 2013 PX-Web contains an API
    to disseminate PC-Axis files.
Function to read PX-Web data into R via API. The example code reads data from the three national statistical institutes, Statistics Norway, Statistics Sweden and Statistics Finland.
Given a SpatialPolygonsDataFrame and a set of populations for each polygon,
 compute a population density estimate based on Tobler's pycnophylactic interpolation
 algorithm. The result is a SpatialGridDataFrame. 
 Methods are described in Tobler Waldo R. (1979) <doi:10.1080/01621459.1979.10481647>.
Deterministic Pena-Yohai initial estimator for robust S estimators
    of regression. The procedure is described in detail in
    Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.
Allow to run 'pylint' on Python files with a R command or a 'RStudio' addin. The report appears in the RStudio viewer pane as a formatted HTML file.
Provides access to the latest 'Amazon Mechanical Turk' ('MTurk') <https://www.mturk.com> Requester API (version '2017–01–17'), replacing the now deprecated 'MTurkR' package.
Drawing population pyramid using (1) data.frame or (2) vectors.
	 The former is named as pyramid() and the latter pyramids(), as wrapper
	 function of pyramid().  pyramidf() is the function to draw population
	 pyramid within the specified frame.
Using the R package 'reticulate', this package creates an interface to the 'pysd' toolset.
    The package provides an R interface to a number of 'pysd' functions, and can read files in
    'Vensim' 'mdl' format, and 'xmile' format. The resulting simulations are returned as a 'tibble', and from 
    that the results can be processed using 'dplyr' and 'ggplot2'. The package has been tested  using 'python3'.
It enables 'sparklyr' to integrate with 'Spark Connect', and
    'Databricks Connect' by providing a wrapper over the 'PySpark' 'python'
    library.
'Google Trends' provides cross-sectional and time-series data on searches, but lacks readily available 
    longitudinal data. Researchers, who want to create longitudinal 'Google Trends' on their own, face practical challenges, such as normalized counts that make it difficult to combine 
    cross-sectional and time-series data and limitations in data formats and timelines that limit data 
    granularity over extended time periods. 
    This package addresses these issues and enables researchers to generate longitudinal 'Google Trends' data.  
    This package is built on 'pytrends', a Python library that acts as the unofficial 'Google Trends API' to collect 'Google Trends' data. As long as the 'Google Trends API', 'pytrends' and all their dependencies are working, this package will work.
    During testing, we noticed that for the same input (keyword, topic, data_format, timeline), the output index can vary from time to time. Besides, if the keyword is not very popular, then the resulting dataset will contain a lot of zeros, which will greatly affect the final result. While this package has no control over the accuracy or quality of 'Google Trends' data, once the data is created, this package coverts it to longitudinal data.  
    In addition, the user may encounter a 429 Too Many Requests error when using cross_section() and time_series() to collect 'Google Trends' data. This error indicates that the user has exceeded the rate limits set by the 'Google Trends API'. For more information about the 'Google Trends API' - 'pytrends', visit <https://pypi.org/project/pytrends/>.
Read and write 'GraphPad Prism' '.pzfx' files in R.
Mortality rates are typically provided in an abridged format, i.e., by age groups 0, [1, 5], [5, 10]', '[10, 15]', and so on. Some applications necessitate a detailed (single) age description. Despite the large number of proposed approaches in the literature, only a few methods ensure great performance at both younger and higher ages. For example, the 6-term 'Lagrange' interpolation function is well suited to mortality interpolation at younger ages (with irregular intervals), but not at older ages. The 'Karup-King' method, on the other hand, performs well at older ages but is not suitable for younger ones. Interested readers can find a full discussion of the two stated methods in the book Shryock, Siegel, and Associates (1993).The Q2q package combines the two methods to allow for the interpolation of mortality rates across all age groups. It begins by implementing each method independently, and then the resulting curves are linked using a 5-age averaged error between the two partial curves.
Construct message-passing style objects with types and features. Q7 types uses composition instead of inheritance in creating derived types, allowing defining any code segment as feature and associating any feature to any object. Compared to R6, Q7 is simpler and more flexible, and is more friendly in syntax.
Functions for descriptive statistics,
  data management, and data visualization.
A copula-based measure for quantifying asymmetry in dependence and associations. Documentation and theory about 'qad' is provided
    by the paper by Junker, Griessenberger & Trutschnig (2021, <doi:10.1016/j.csda.2020.107058>), and the paper by Trutschnig (2011, <doi:10.1016/j.jmaa.2011.06.013>).
The Ensemble Quadratic and Affine Invariant Markov chain Monte 
    Carlo algorithms provide an efficient way to perform Bayesian inference in 
    difficult parameter space geometries. The Ensemble Quadratic Monte Carlo 
    algorithm was developed by Militzer (2023) <doi:10.3847/1538-4357/ace1f1>. 
    The Ensemble Affine Invariant algorithm was developed by Goodman and Weare 
    (2010) <doi:10.2140/camcos.2010.5.65> and it was implemented in Python by 
    Foreman-Mackey et al (2013) <doi:10.48550/arXiv.1202.3665>. The Quadratic 
    Monte Carlo method was shown to perform better than the Affine Invariant 
    method in the paper by Militzer (2023) <doi:10.3847/1538-4357/ace1f1> and 
    the Quadratic Monte Carlo method is the default method used. The Chen-Shao 
    Highest Posterior Density Estimation algorithm is used for obtaining 
    credible intervals and the potential scale reduction factor diagnostic is 
    used for checking the convergence of the chains.
A tool for automatic generation of sibling items from a parent item model defined by
           the user. It is an implementation of the process automatic item generation (AIG) focused
           on generating quantitative multiple-choice type of items (see Embretson, Kingston
           (2018) <doi:10.1111/jedm.12166>).
Implements heuristics for the Quadratic Assignment Problem (QAP). Although, the QAP was introduced as a combinatorial optimization problem for the facility location problem in operations research, it also has many applications in data analysis. The problem is NP-hard and the package implements a simulated annealing heuristic.
Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap is used. The test of normality using Cholesky decomposition is added. Y pop is defined.
Calculates the right-tail probability of quadratic forms of Gaussian variables using the skewness-kurtosis ratio matching method, modified Liu-Tang-Zhang method and Satterthwaite-Welch method. The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2020) <arXiv:2005.00905>.
Implements the Bayesian quantile regression model for binary longitudinal data 
             (QBLD) developed in Rahman and Vossmeyer (2019) <DOI:10.1108/S0731-90532019000040B009>.
             The model handles both fixed and random effects and implements both a blocked
             and an unblocked Gibbs sampler for posterior inference.
This R package assists breeders in linking data systems with their analytic pipelines, 
    a crucial step in digitizing breeding processes. It supports querying and retrieving 
    phenotypic and genotypic data from systems like 'EBS' <https://ebs.excellenceinbreeding.org/>, 
    'BMS' <https://bmspro.io>, 'BreedBase' <https://breedbase.org>, and 
    'GIGWA' <https://github.com/SouthGreenPlatform/Gigwa2> (using 'BrAPI' <https://brapi.org> calls). 
    Extra helper functions support environmental data sources, including 
    'TerraClimate' <https://www.climatologylab.org/terraclimate.html> and 'FAO' 
    'HWSDv2' <https://gaez.fao.org/pages/hwsd> soil database.
Programmatically access the 'Quickbase' JSON API <https://developer.quickbase.com>. 
    You supply parameters for an API call, 'qbr' delivers an http request to the 
    API endpoint and returns its response. Outputs follow 'tidyverse' philosophy.
An extensive set of functions to perform Qualitative Comparative Analysis:
       crisp sets ('csQCA'), temporal ('tQCA'), multi-value ('mvQCA')
       and fuzzy sets ('fsQCA'), using a GUI - graphical user interface.
       'QCA' is a methodology that bridges the qualitative and quantitative
       divide in social science research. It uses a Boolean minimization
       algorithm, resulting in a minimal causal configuration associated
       with a given phenomenon.
Clustered set-relational data in Qualitative Comparative Analysis
    (QCA) can have a hierarchical structure, a panel structure or repeated cross
    sections. 'QCAcluster' allows QCA researchers to supplement the analysis
    of pooled the data with a disaggregated perspective focusing on selected 
    partitions of the data. The pooled data can be partitioned along the 
    dimensions of the clustered data (individual cross sections or time series) 
    to perform partition-specific truth table minimizations. Empirical 
    researchers can further calculate the weight that each partition has on the 
    parameters of the pooled solution and the diversity of the cases under 
    analysis within and across partitions 
    (see <https://ingorohlfing.github.io/QCAcluster/>).
Researchers working with Qualitative Comparative Analysis (QCA)
    can use the package to estimate power of a sufficient term
    using permutation tests. A term can be anything: A condition, conjunction or 
    disjunction of any combination of these. The package further allows users to plot 
    the estimation results and to estimate the number of cases required to achieve a
    certain level of power, given a prespecified null and alternative hypothesis.
    Reference for the article introducing power estimation for QCA is: 
    Rohlfing, Ingo (2018) <doi:10.1017/pan.2017.30> 
    (ungated version: <doi:10.17605/OSF.IO/PC4DF>).
Provides advanced functionality for performing configurational comparative research with Qualitative Comparative Analysis (QCA), including crisp-set, multi-value, and fuzzy-set QCA. It also offers advanced tools for sensitivity diagnostics and methodological evaluations of QCA.
Helper functions for Qualitative Comparative Analysis: evaluate and
    plot Boolean formulae on fuzzy set score data, apply Boolean operations, compute
    consistency and coverage measures.
Quasi-Cauchy quantile regression, proposed by de Oliveira, Ospina, Leiva, Figueroa-Zuniga and Castro (2023) <doi:10.3390/fractalfract7090667>. This regression model is useful for the case where you want to model data of a nature limited to the intervals [0,1], (0,1], [0,1) or (0,1) and you want to use a quantile approach.
Implements the Quantitative Classification-based on
    Association Rules (QCBA)  algorithm (<doi:10.1007/s10489-022-04370-x>). 
    QCBA postprocesses rule classification models making them typically smaller and in some cases more accurate. 
    Supported are 'CBA' implementations from 'rCBA', 'arulesCBA' and 'arc' packages, and 'CPAR', 'CMAR', 'FOIL2' and 'PRM' implementations 
    from 'arulesCBA' package and 'SBRL' implementation from the 'sbrl' package. The result of the post-processing is an ordered CBA-like rule list.
Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.
Functions to calculate Average Sample Numbers (ASN), Average Run Length (ARL1) and value of k, k1 and k2 for quality control charts under repetitive sampling as given in Aslam et al. (2014) (<DOI:10.7232/iems.2014.13.1.101>).
Tools for (automated and manual) quality control of 
  the results of Epigenome-Wide Association Studies.
Tools for (automated and manual) quality control of the results of Genome Wide Association Studies.
Provides functions for the joint analysis of Q sets of
    p-values obtained for the same list of items. This joint analysis is
    performed by querying a composite hypothesis, i.e. an arbitrary
    complex combination of simple hypotheses, as described in Mary-Huard
    et al. (2021) <doi:10.1093/bioinformatics/btab592> and De Walsche et
    al.(2023) <doi:10.1101/2024.03.17.585412>. In this approach, the
    Q-uplet of p-values associated with each item is distributed as a
    multivariate mixture, where each of the 2^Q components corresponds to
    a specific combination of simple hypotheses. The dependence between
    the p-value series is considered using a Gaussian copula function. A
    p-value for the composite hypothesis test is derived from the
    posterior probabilities.
Performs tuning of clustering models, methods and algorithms including the problem of determining an appropriate number of clusters. Validation of cluster analysis results is performed via quadratic scoring using resampling methods, as in Coraggio, L. and Coretto, P. (2023) <doi:10.1016/j.jmva.2023.105181>.
Implements the Quantile Composite-based Path Modeling approach 
	(Davino and Vinzi, 2016 <doi:10.1007/s11634-015-0231-9>; 
	 Dolce et al., 2021 <doi:10.1007/s11634-021-00469-0>). The method complements the traditional PLS Path Modeling approach, analyzing the entire distribution of outcome variables and, therefore, overcoming the classical exploration of only average effects. It exploits quantile regression to investigate changes in the relationships among constructs and between constructs and observed variables.
Quality control of chromatin immunoprecipitation libraries (ChIP-seq) by quantitative polymerase chain reaction (qPCR). This function calculates Enrichment value with respect to reference for each histone modification (specific to 'Vii7' software <http://www.thermofisher.com/ca/en/home/life-science/pcr/real-time-pcr/real-time-pcr-instruments/viia-7-real-time-pcr-system/viia-7-software.html>). This function is applicable to full panel of histone modifications described by International Human Epigenomic Consortium (IHEC).
Univariate and multivariate SQC tools that completes and increases
    the SQC techniques available in R. Apart from integrating different R packages 
    devoted to SQC ('qcc','MSQC'), provides nonparametric tools that are highly 
    useful when Gaussian assumption is not met. 
    This package computes standard univariate control charts for individual measurements, 
    'X-bar', 'S', 'R', 'p', 'np', 'c', 'u', 'EWMA' and 'CUSUM'. In addition, it 
    includes functions to perform multivariate control charts such as 'Hotelling T2', 
    'MEWMA' and 'MCUSUM'. As representative feature, multivariate nonparametric 
    alternatives based on data depth are implemented in this package: 'r', 'Q' and 
    'S' control charts. In addition, Phase I and II control charts for functional 
    data are included. This package also allows the estimation of the most complete 
    set of capability indices from first to fourth generation, covering the nonparametric 
    alternatives, and performing the corresponding capability analysis graphical outputs,
    including the process capability plots. See Flores et al. (2021) <doi:10.32614/RJ-2021-034>.
Simulates a 5 qubit Quantum Computer and evaluates quantum circuits with 1,2
             qubit quantum gates.
Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data.
Primarily, the 'qcv' package computes key indices related to the Quantifying Construct
    Validity procedure (QCV; Westen & Rosenthal, 2003 <doi:10.1037/0022-3514.84.3.608>; 
    see also Furr & Heuckeroth, in press). The qcv() function is the heart of the 'qcv' package, 
    but additional functions in the package provide useful ancillary information related to the QCV procedure.
Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse
              including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted
              analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user
              to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R
              packages that undertake higher level analysis and visualization of text. This affords the user a more efficient
              and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other
              areas of Text Mining/ Natural Language Processing.
A collection of text analysis dictionaries and word lists for use with
        the 'qdap' package.
A collection of regular expression tools associated with
        the 'qdap' package that may be useful outside of the context of
        discourse analysis. Tools include
        removal/extraction/replacement of abbreviations, dates, dollar
        amounts, email addresses, hash tags, numbers, percentages,
        citations, person tags, phone numbers, times, and zip codes.
A collection of tools associated with the 'qdap' package that may be useful outside of the
            context of text analysis.
Allows practitioners to determine (i) if two univariate distributions (which can be continuous, discrete, or even mixed) are equal, (ii) how two distributions differ (shape differences, e.g., location, scale, etc.), and (iii) where two distributions differ (at which quantiles), all using nonparametric LP statistics. The primary reference is Jungreis, D. (2019, Technical Report).
Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by 'ClinRisk'.
This package provides different specifications of a Quadrilateral
    Dissimilarity Model which can be used to fit same-different judgments
    in order to get a predicted matrix that satisfies regular minimality
    [Colonius & Dzhafarov, 2006, Measurement and representations of
    sensations, Erlbaum]. From such a matrix, Fechnerian distances can be
    computed.
The letters 'qe' in the package title stand for "quick and
   easy," alluding to the convenience goal of the package. We bring
   together a variety of machine learning (ML) tools from standard R
   packages, providing wrappers with a simple, convenient, 
   and uniform interface.
Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.
The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms.
Quantile-frequency analysis (QFA) of time series based on trigonometric quantile regression. 
 Spline quantile regression (SQR) for regression coefficient estimation.
 References:
    [1] Li, T.-H. (2012) "Quantile periodograms", Journal of the American Statistical
        Association, 107, 765–776, <doi:10.1080/01621459.2012.682815>.
    [2] Li, T.-H. (2014) Time Series with Mixed Spectra, CRC Press, <doi:10.1201/b15154>
    [3] Li, T.-H. (2022) "Quantile Fourier transform, quantile series, and nonparametric
        estimation of quantile spectra", <doi:10.48550/arXiv.2211.05844>.
    [4] Li, T.-H. (2024) "Quantile crossing spectrum and spline autoregression
         estimation," <doi:10.48550/arXiv.2412.02513>.
    [5] Li, T.-H. (2024) "Spline autoregression method for estimation of quantile spectrum",
        <doi:10.48550/arXiv.2412.17163>.
Accurate estimates of the diets of predators are required
    in many areas of ecology, but for many species current methods are
    imprecise, limited to the last meal, and often biased. The diversity
    of fatty acids and their patterns in organisms, coupled with the
    narrow limitations on their biosynthesis, properties of digestion in
    monogastric animals, and the prevalence of large storage reservoirs of
    lipid in many predators, led to the development of quantitative
    fatty acid signature analysis (QFASA) to study predator diets.
An implementation of Quantitative Fatty Acid Signature
    Analysis (QFASA) in R.  QFASA is a method of estimating the diet
    composition of predators.  The fundamental unit of information in
    QFASA is a fatty acid signature (signature), which is a vector of
    proportions describing the composition of fatty acids within lipids.
    Signature data from at least one predator and from samples of all
    potential prey types are required.  Calibration coefficients, which
    adjust for the differential metabolism of individual fatty acids by
    predators, are also required. Given those data inputs, a predator
    signature is modeled as a mixture of prey signatures and its diet
    estimate is obtained as the mixture that minimizes a measure of
    distance between the observed and modeled signatures.  A variety of
    estimation options and simulation capabilities are implemented.
    Please refer to the vignette for additional details and references.
Evaluates moments of ratios (and products) of quadratic forms
    in normal variables, specifically using recursive algorithms developed by
    Bao and Kan (2013) <doi:10.1016/j.jmva.2013.03.002> and Hillier et al.
    (2014) <doi:10.1017/S0266466613000364>. Also provides distribution,
    quantile, and probability density functions of simple ratios of quadratic
    forms in normal variables with several algorithms. Originally developed as
    a supplement to Watanabe (2023) <doi:10.1007/s00285-023-01930-8> for
    evaluating average evolvability measures in evolutionary quantitative
    genetics, but can be used for a broader class of statistics. Generating 
    functions for these moments are also closely related to the top-order zonal
    and invariant polynomials of matrix arguments.
Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. 
  This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.
Function that implements the Quantum Genetic Algorithm, first proposed by Han and Kim in 2000. This is an R implementation of the 'python' application developed by Lahoz-Beltra  (<https://github.com/ResearchCodesHub/QuantumGeneticAlgorithms>). Each optimization problem is represented as a maximization one, where each solution is a sequence of (qu)bits. Following the quantum paradigm, these qubits are in a superposition state: when measuring them, they collapse in a 0 or 1 state. After measurement, the fitness of the solution is calculated as in usual genetic algorithms. The evolution at each iteration is oriented by the application of two quantum gates to the amplitudes of the qubits: (1) a rotation gate (always); (2) a Pauli-X gate (optionally). The rotation is based on the theta angle values: higher values allow a quicker evolution, and lower values avoid local maxima. The Pauli-X gate is equivalent to the classical mutation operator and determines the swap between alfa and beta amplitudes of a given qubit. The package has been developed in such a way as to permit a complete separation between the engine, and the particular problem subject to combinatorial optimization.
Smooth additive quantile regression models, fitted using
    the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>.
    See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from
    'quantreg', the smoothing parameters are estimated automatically by marginal
    loss minimization, while the regression coefficients are estimated using either
    PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian
    credible intervals of the estimated effects have approximately the correct
    coverage. The main function is qgam() which is similar to gam() in 'mgcv', but
    fits non-parametric quantile regression models.
General purpose toolbox for simulating quantum versions of game theoretic models (Flitney and Abbott 2002) <arXiv:quant-ph/0208069>. Quantum (Nielsen and Chuang 2010, ISBN:978-1-107-00217-3) versions of models that have been handled are: Penny Flip Game (David A. Meyer 1998) <arXiv:quant-ph/9804010>, Prisoner's Dilemma (J. Orlin Grabbe 2005) <arXiv:quant-ph/0506219>, Two Person Duel (Flitney and Abbott 2004) <arXiv:quant-ph/0305058>, Battle of the Sexes (Nawaz and Toor 2004) <arXiv:quant-ph/0110096>, Hawk and Dove Game (Nawaz and Toor 2010) <arXiv:quant-ph/0108075>, Newcomb's Paradox (Piotrowski and Sladkowski 2002) <arXiv:quant-ph/0202074> and Monty Hall Problem (Flitney and Abbott 2002) <arXiv:quant-ph/0109035>.
Density, distribution function, quantile function and 
  random generation for the q-gaussian distribution with parameters mu and sig.
G-computation for a set of time-fixed exposures with
    quantile-based basis functions, possibly under linearity and
    homogeneity assumptions. This approach estimates a regression line
    corresponding to the expected change in the outcome (on the link
    basis) given a simultaneous increase in the quantile-based category
    for all exposures. Works with continuous, binary, and right-censored
    time-to-event outcomes.  Reference: Alexander P. Keil, Jessie P.
    Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
    Alexandra J. White (2019) A quantile-based g-computation approach to
    addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
G-computation for a set of time-fixed exposures
    with quantile-based basis functions, possibly under linearity and
    homogeneity assumptions. Effect measure modification in this method is a way
    to assess how the effect of the mixture varies by a binary, categorical or continuous variable.  
    Reference: Alexander P. Keil, Jessie P.
    Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
    Alexandra J. White (2019) A quantile-based g-computation approach to
    addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses.
    Rohde et al. (2019) <doi:10.1101/503631>.
Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.
Provides seamless access to the 'QGIS'
    (<https://qgis.org>) processing toolbox using the standalone
    'qgis_process' command-line utility.  Both native and third-party
    (plugin) processing providers are supported.  Beside referring data
    sources from file, also common objects from 'sf', 'terra' and 'stars'
    are supported. The native processing algorithms are documented by QGIS.org 
    (2024) <https://docs.qgis.org/latest/en/docs/user_manual/processing_algs/>.
Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.
A 'shiny' application for teaching introductory quantitative genetics
    and plant breeding through interactive simulations. The application relies on 
    established plant breeding and quantitative genetic theory found in 
    Falconer and Mackay (1996, ISBN:0582243025) and Bernardo (2010, ISBN:978-0972072427).
This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs.
Nomograms are constructed to predict the cumulative incidence
    rate which is calculated after adjusting for competing causes to the event of interest. 
    K-fold cross-validation is implemented to validate predictive accuracy using a competing-risk version of the concordance index.
    Methods are as described in: Kattan MW, Heller G, 
    Brennan MF (2003).
The quantity-intensity (Q/I) relationships, first introduced by Beckett
  (1964), can be employed to assess the K supplying capacity of different soils 
  based on solid-solution exchange equilibria. Such relationships describe the 
  changes in K+ concentration in the soil solution (or the intensity factor) in 
  relation to the corresponding changes in K+ at exchange sites of the soil 
  (or the capacity or quantity factor). Activity ratio of K to Ca or Ca+Mg 
  is generally used as the variable denoting the intensity, whereas, change 
  in exchangeable K is used to denote the quantity factor.
Functions for making run charts 
    [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>] and basic
    Shewhart control charts [Mohammed, Worthington, Woodall (2008)
    <doi:10.1136/qshc.2004.012047>] for measure and count data.
    The main function, qic(), creates run and control charts and has a
    simple interface with a rich set of options to control data analysis
    and plotting, including options for automatic data aggregation by
    subgroups, easy analysis of before-and-after data, exclusion of one
    or more data points from analysis, and splitting charts into
    sequential time periods.
    Missing values and empty subgroups are handled gracefully.
Functions for making run charts, Shewhart control charts and
    Pareto charts for continuous quality improvement. Included control charts
    are: I, MR, Xbar, S, T, C, U, U', P, P', and G charts. Non-random variation
    in the form of minor to moderate persistent shifts in data over time is
    identified by the Anhoej rules for unusually long runs and unusually few
    crossing  [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>].
    Non-random variation in the form of larger, possibly transient, shifts is
    identified by Shewhart's 3-sigma rule [Mohammed, Worthington, Woodall (2008)
    <doi:10.1136/qshc.2004.012047>].
Developed to perform the estimation and inference for regression 
    coefficient parameters in longitudinal marginal models using the method of 
    quadratic inference functions. Like generalized estimating equations, this 
    method is also a quasi-likelihood inference method. It has been showed that 
    the method gives consistent estimators of the regression coefficients even if 
    the correlation structure is misspecified, and it is more efficient than GEE 
    when the correlation structure is misspecified. Based on Qu, A., Lindsay, 
    B.G. and Li, B. (2000) <doi:10.1093/biomet/87.4.823>.
Qiita is a technical knowledge sharing and collaboration platform for programmers.
  See <https://qiita.com/api/v2/docs> for more information.
Select optimal functional regression or dichotomized
       quantile predictors for survival/logistic/numeric outcome
       and perform optimistic bias correction for any optimally
       dichotomized numeric predictor(s), as in Yi, et. al.
       (2023) <doi:10.1016/j.labinv.2023.100158>.
Nonlinear machine learning tool for classification, clustering
        and dimensionality reduction. It integrates 12 q-kernel functions and
        15 conditional negative definite kernel functions and includes the 
        q-kernel and conditional negative definite kernel version of
        density-based spatial clustering of applications with noise,
        spectral clustering, generalized discriminant analysis, principal
        component analysis, multidimensional scaling, locally linear embedding,
        sammon's mapping and t-Distributed stochastic neighbor embedding.
'QuantLib' bindings are provided for R using 'Rcpp' via an evolved version
 of the initial header-only 'Quantuccia' project offering an subset of 'QuantLib' (now
 maintained separately just for the calendaring subset). See the included file 'AUTHORS'
 for a full list of contributors to 'QuantLib' (and hence also 'Quantuccia').
Functionality to read, recode, and transcode data as used in 
  quantitative language comparison, specifically to deal with multilingual 
  orthographic variation (Moran & Cysouw (2018) <doi:10.5281/zenodo.1296780>) 
  and with the recoding of nominal data.
Extension of the functionality of the 'Matrix' package for using sparse matrices. Some of the functions are very general, while other are highly specific for special data format as used for quantitative language comparison.
Collection of visualizations as used in quantitative language 
  comparison. Currently implemented are visualisations dealing nominal data with 
  multiple levels ("level map" and "factor map"), and assistance for making 
  weighted geographical Voronoi-maps ("weighted map").
Implements Q-Learning, a model-free form of reinforcement
        learning, described in work by Strehl, Li, Wiewiora, Langford &
        Littman (2006) <doi:10.1145/1143844.1143955>.
Manages, builds and computes statistics and datasets for the 
   construction of quarterly (sub-annual) life tables by exploiting micro-data from
   either a general or an insured population.
   References: 
   Pavía and Lledó (2022) <doi:10.1111/rssa.12769>.
   Pavía and Lledó (2023) <doi:10.1017/asb.2023.16>.
   Acknowledgements:
   The authors wish to thank Consellería de Educación, Universidades y Empleo, Generalitat Valenciana (grant AICO/2021/257), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el cálculo de productos de vida') for supporting this research.
Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.
A multivariate copula-based dependence measure. For more information, see Griessenberger, Junker, Trutschnig (2022), On a multivariate copula-based dependence measure and its estimation, Electronic Journal of Statistics, 16, 2206-2251.
Analysis of Q methodology, used to identify distinct perspectives existing within a group.
  This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see <https://qmethod.org/>).
  A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors.
  The package allows to choose either principal components or centroid factor extraction, manual or automatic flagging, a number of mathematical methods for rotation (or none), and a number of correlation coefficients for the initial correlation matrix, among many other options.
  Additional functions are available to import and export data (from raw *.CSV, 'HTMLQ' and 'FlashQ' *.CSV, 'PQMethod' *.DAT and 'easy-htmlq' *.JSON files), to print and plot, to import raw data from individual *.CSV files, and to make printable cards.
  The package also offers functions to print Q cards and to generate Q distributions for study administration.
  See further details in the package documentation, and in the web pages below, which include a cookbook, guidelines for more advanced analysis (how to perform manual flagging or change the sign of factors), data management, and a graphical user interface (GUI) for online and offline use.
Produces quality scores for each of the US companies from 
    the Russell 3000, following the approach described in 
    "Quality Minus Junk" (Asness, Frazzini, & Pedersen, 2013) 
    <http://www.aqr.com/library/working-papers/quality-minus-junk>. 
    The package includes datasets for users who wish to view the most recently 
    uploaded quality scores. It also provides tools to automatically gather 
    relevant financials and stock price information, allowing users to update 
    their data and customize their universe for further analysis.
Implementation of methods for estimation of quantitative maps
    from Multi-Parameter Mapping (MPM) acquisitions (Weiskopf et al. (2013)
    <doi:10.3389/fnins.2013.00095>) and analysis of Inversion Recovery MRI data.
    Usage of the package is described in
    Polzehl and Tabelow (2023),
    "Magnetic Resonance Brain Imaging", 2nd Edition, Chapter 6 and 7, Springer, Use R! Series.
    <doi:10.1007/978-3-031-38949-8>.
    J. Polzehl and K. Tabelow (2023), "Magnetic Resonance Brain Imaging - Modeling and Data Analysis Using R: Code and Data."
    <doi:10.20347/WIAS.DATA.6> provides extensive example code and data.
Basic functions for building parsers, with an application to PC-AXIS format files.
The queueing model of visual search models the accuracy and response time data in a visual search experiment using queueing models with finite customer population and stopping criteria of completing the service for finite number of customers. It implements the conceptualization of a hybrid model proposed by Moore and Wolfe (2001), in which visual stimuli enter the processing one after the other and then are identified in parallel. This package provides functions that simulate the specified queueing process and calculate the Wasserstein distance between the empirical response times and the model prediction.
The new QOI file format offers a very simple but efficient image compression algorithm. This package provides an easy and simple way to read, write and display bitmap images stored in the QOI (Quite Ok Image) format. It can read and write both files and in-memory raw vectors.
There are three functions: qol, miss_qol and miss_patient takes input of the data set containing the answers of QOL questionnaire. It will compute the three types of domain based scale scores: Global, Functional, and Symptoms. In case of missing data, the miss_qol and miss_patient functions will make the required changes and then calculate the domain-wise scale scores. Finally, provide an output replacing the question columns with the domain-based scale scores in the original data set.
Model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).
Computes normalized cycle threshold (Ct) values (delta Ct) from raw quantitative polymerase 
    chain reaction (qPCR) Ct values and conducts test of significance using t.test(). Plots expression 
    values based from log2(2^(-1*delta delta Ct)) across groups per gene of interest. Methods for calculation
    of delta delta Ct and relative expression (2^(-1*delta delta Ct)) values are described in: 
    Livak & Schmittgen, (2001) <doi:10.1006/meth.2001.1262>.
Content-preserving transformations transformations of PDF files such 
    as split, combine, and compress. This package interfaces directly to the 'qpdf' 
    C++ library <https://qpdf.sourceforge.io/> and does not require any command 
    line utilities. Note that 'qpdf' does not read actual content from PDF files: 
    to extract text and data you need the 'pdftools' package.
Efficiently solve quadratic problems with linear inequality, equality and box constraints. The method used is outlined in D. Goldfarb, and A. Idnani (1983) <doi:10.1007/BF02591962>.
Computes noncompartmental pharmacokinetic parameters
 for drug concentration profiles.  For each profile, data
 imputations and adjustments are made as necessary and
 basic parameters are estimated. Supports single dose, multi-dose,
 and multi-subject data.  Supports steady-state calculations
 and various routes of drug administration. See ?qpNCA and vignettes.
 Methodology follows Rowland and Tozer (2011, ISBN:978-0-683-07404-8), 
 Gabrielsson and Weiner (1997, ISBN:978-91-9765-100-4), and
 Gibaldi and Perrier (1982, ISBN:978-0824710422).
Supports risk assessors in performing the entry step of the
    quantitative Pest Risk Assessment. It allows the estimation of the
    amount of a plant pest entering a risk assessment area (in terms of
    founder populations) through the calculation of the imported
    commodities that could be potential pathways of pest entry, and the
    development of a pathway model. Two 'Shiny' apps based on the
    functionalities of the package are included, that simplify the process
    of assessing the risk of entry of plant pests. The approach is based
    on the work of the European Food Safety Authority (EFSA PLH Panel et
    al., 2018) <doi:10.2903/j.efsa.2018.5350>.
A system to implement the Q-Q boxplot.  It is implemented as an
    extension to 'ggplot2'.  The Q-Q boxplot is an amalgam of the boxplot and the
    Q-Q plot and allows the user to rapidly examine summary statistics and tail
    behavior for multiple distributions in the same pane.  As an extension of
    the 'ggplot2' implementation of the boxplot, possible modifications to the 
    boxplot extend to the Q-Q boxplot.
Provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous 
    testing bands to asses significance of sample deviation from a reference distribution <doi:10.18637/jss.v106.i10>.
Create Q-Q and manhattan plots for GWAS data from PLINK results.
Extensions of 'ggplot2' Q-Q plot functionalities.
Provides the function qqtest which incorporates uncertainty in its
    qqplot display(s) so that the user might have a better sense of the
    evidence against the specified distributional hypothesis.  qqtest draws a
    quantile quantile plot for visually assessing whether the data come from a
    test distribution that has been defined in one of many ways.  The vertical
    axis plots the data quantiles, the horizontal those of a test distribution.
    The default behaviour generates 1000 samples from the test distribution and
    overlays the plot with shaded pointwise interval estimates for the ordered
    quantiles from the test distribution.  A small number of independently
    generated exemplar quantile plots can also be overlaid.  Both the interval
    estimates and the exemplars provide different comparative information to
    assess the evidence provided by the qqplot for or against the hypothesis
    that the data come from the test distribution (default is normal or
    gaussian).  Finally, a visual test of significance (a lineup plot) can also
    be displayed to test the null hypothesis that the data come from the test
    distribution.
Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy.  The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions.  The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot.  The package draws QQ plots for several distributions, either as samples or continuous functions.
This function performs QR factorization without pivoting to a real or complex matrix. It is based on Anderson. E. and ten others (1999) "LAPACK Users' Guide". Third Edition. SIAM.
Functions are provided that implement the 
    use of the Fieller's formula methodology, for 
    calculating a confidence interval for a ratio of
    (commonly, correlated) means.  See Fieller (1954)
    <doi:10.1111/j.2517-6161.1954.tb00159.x>.  Here,
    the application of primary interest is to studies
    of insect mortality response to increasing doses
    of a fumigant, or, e.g., to time in coolstorage.
    The formula is used to calculate a confidence
    interval for the dose or time required to achieve
    a specified mortality proportion, commonly 0.5 
    or 0.99.  Vignettes demonstrate link functions
    that may be considered, checks on fitted models,
    and alternative choices of error family.  Note
    in particular the betabinomial error family.
    See also Maindonald, Waddell, and Petry (2001) 
    <doi:10.1016/S0925-5214(01)00082-5>.
Upload raster data and easily create interactive quantitative risk analysis 'QRA' visualizations. Select
    from numerous color palettes, base-maps, and different configurations.
A Quantile Rank-score based test for the identification of expression quantitative trait loci.
Parametric modeling of quantile regression coefficient functions.
Nonlinear and Penalized parametric modeling of quantile regression coefficient functions. Sottile G, Frumento P, Chiodi M and Bottai M (2020) <doi:10.1177/1471082X19825523>.
Create static QR codes in R. The content of the QR code is
    exactly what the user defines. We don't add a redirect URL, making it
    impossible for us to track the usage of the QR code. This allows to
    generate fast, free to use and privacy friendly QR codes.
Implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.
Quantile regression in varying-coefficient models (VCM) using one particular nonparametric technique called P-splines. The functions can be applied on three types of VCM; (1) Homoscedastic VCM, (2) Simple heteroscedastic VCM, and (3) General heteroscedastic VCM.
Quick Response codes (QR codes) are a type of matrix bar code and can be
    used to authenticate transactions, provide access to multi-factor authentication
    services and enable general data transfer in an image. QR codes use four standardized 
    encoding modes (numeric, alphanumeric, byte/binary, and kanji) to efficiently store 
    data. Matrix barcode generation is performed efficiently in C via the included
    'libqrencoder' library created by Kentaro Fukuchi.
Provides statistical components, tables, and graphs
    that are useful in 'Quarto' and 'RMarkdown' reports and that produce 'Quarto'
    elements for special formatting such as tabs and marginal notes and graphs.
    Some of the functions produce entire report sections with tabs, e.g.,
    the missing data report created by missChk().  Functions for inserting
		variables and tables inside 'graphviz' and 'mermaid' diagrams are included,
		and so are special clinical trial graphics for adverse event reporting.
The QRI_func() function performs quantile regression analysis using age and sex as predictors to calculate the Quantile Regression Index (QRI) score for each individual’s regional brain imaging metrics and then averages across the regional scores to generate an average tissue specific score for each subject. The QRI_plot() is used to plot QRI and generate the normative curves for individual measurements.
A collection of functions is provided by this package to fit quantiles regression models for censored residual lifetimes. It provides various options for regression parameters estimation: the induced smoothing approach (smooth), and L1-minimization (non-smooth).  It also implements the estimation methods for standard errors of the regression parameters estimates based on an efficient partial multiplier bootstrap method and robust sandwich estimator. Furthermore, a simultaneous procedure of estimating regression parameters and their standard errors via an iterative updating procedure is implemented (iterative). For more details, see Kim, K. H., Caplan, D. J., & Kang, S. (2022), "Smoothed quantile regression for censored residual life", Computational Statistics, 1-22 <doi:10.1007/s00180-022-01262-z>.
This function aims to calculate risk of developing cardiovascular disease of individual patients in next 10 years. This unofficial package was based on published open-sourced free risk prediction algorithm QRISK3-2017 <https://qrisk.org/src.php>.
Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.
A no-frills open-source solution for designing plot labels affixed with QR 
    codes. It features 'EasyQrlabelr', a 'BrAPI-compliant' 'shiny' app that 
    simplifies the process of plot label design for non-R users. It builds on the 
    methods described by Wu 'et al.' (2020) <doi:10.1111/2041-210X.13405>.
Quantile regression (QR) for Linear 
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference results 
             for the fixed-effects and variance components. 
             It also provides graphical summaries for assessing the algorithm 
             convergence and fitting results.
Provides functions/methods to accompany the book
 Quantitative Risk Management: Concepts, Techniques and Tools by
 Alexander J. McNeil, Ruediger Frey, and Paul Embrechts.
Various data sets (stocks, stock indices, constituent data, FX,
 zero-coupon bond yield curves, volatility, commodities) for Quantitative
 Risk Management practice.
Implements the robust algorithm for fitting finite mixture models based on quantile regression proposed by Emir et al., 2017 (unpublished).
Functions and data sets for reproducing selected results from
  the book "Quantitative Risk Management: Concepts, Techniques and Tools".
  Furthermore, new developments and auxiliary functions for Quantitative
  Risk Management practice.
Functionality for generating (randomized) quasi-random numbers in
  high dimensions.
Quantile regression (QR) for Nonlinear
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference results 
             for the fixed-effects and variance components.
             It also provides prediction and graphical summaries for assessing the algorithm
             convergence and fitting results.
Fit quantile regression neural network models with optional
    left censoring, partial monotonicity constraints, generalized additive
    model constraints, and the ability to fit multiple non-crossing quantile
    functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005>
    and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.
Provides functions for quickly writing and reading any R object to and from disk.
Streamlines and accelerates the process of saving and loading R objects, improving speed and compression compared to other methods. The package provides two compression formats: the 'qs2' format, which uses R serialization via the C API while optimizing compression and disk I/O, and the 'qdata' format, featuring custom serialization for slightly faster performance and better compression. Additionally, the 'qs2' format can be directly converted to the standard 'RDS' format, ensuring long-term compatibility with future versions of R.
Molecular descriptors and outcomes for several public domain data sets
A quantum computer simulator framework with up to 24 qubits. It allows to
    define general single qubit gates and general controlled single
    qubit gates. For convenience, it currently provides the
    most common gates (X, Y, Z, H, Z, S, T, Rx, Ry, Rz, CNOT, SWAP, Toffoli or
    CCNOT, Fredkin or CSWAP). 'qsimulatR' also implements noise models.
    'qsimulatR' supports plotting of circuits and is able to
    export circuits to 'Qiskit' <https://qiskit.org/>, a python package
    which can be used to run on IBM's hardware <https://quantum-computing.ibm.com/>.
Implementations of the quantile slice sampler of Heiner et al. (2024+, in preparation) as well as other popular slice samplers are provided. 
    Helper functions for specifying pseudo-target distributions are included, both for diagnostics and for tuning the quantile slice sampler. 
    Other implemented methods include the generalized elliptical slice sampler of Nishihara et al. (2014)<https://jmlr.org/papers/v15/nishihara14a.html},
    latent slice sampler of Li and Walker (2023)<doi:10.1016/j.csda.2022.107652>, and stepping-out slice sampler of Neal (2003)<doi:10.1214/aos/1056562461>,
    and independence Metropolis-Hastings sampler.
Computes scores from Q-sort data, using criteria sorts and
  derived scales from subsets of items.
  The 'qsort' package includes descriptions and scoring procedures
  for four different Q-sets commonly used in developmental psychology research:
  Attachment Q-set (version 3.0) (Waters, 1995, <doi:10.1111/j.1540-5834.1995.tb00214.x>);
  California Child Q-set (Block and Block, 1969, <doi:10.1037/0012-1649.21.3.508>);
  Maternal Behaviour Q-set (version 3.1)
  (Pederson et al., 1999, <https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1000&context=psychologypub>);
  Preschool Q-set (Baumrind, 1968 revised by Wanda Bronson, <doi:10.1111/j.1540-5834.1995.tb00214.x>).
Provides routines to create some quaternions splines:
    Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels
    algorithm. The implementations are based on the Python library
    'splines'. Quaternions splines allow to construct spherical curves. 
    References: Barry and Goldman <doi:10.1145/54852.378511>, 
    Kochanek and Bartels <doi:10.1145/800031.808575>.
Symbolic calculation and evaluation of multivariate
    polynomials with rational coefficients. This package is strongly
    inspired by the 'spray' package. It provides a function to 
    compute Gröbner bases (reference <doi:10.1007/978-3-319-16721-3>). It 
    also includes some features for symmetric polynomials, such as the Hall
    inner product.
    The header file of the C++ code can be used by other packages. It 
    provides the templated class 'Qspray' that can be used to represent and 
    to deal with multivariate polynomials with another type of coefficients.
Provides functions for quickly writing (and 
  reading back) a data.frame to file in 'SQLite' format. The name 
  stands for *Store Tables using 'SQLite'*, or alternatively for *Quick 
  Store Tables* (either way, it could be pronounced as *Quest*). 
  For data.frames containing the supported data 
  types it is intended to work as a drop-in replacement for the 
  'write_*()' and 'read_*()' functions provided by similar packages.
Run lapply() calls in parallel by submitting them to 
    'gridengine' clusters using the 'qsub' command.
Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <doi:10.3982/QE935>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).
Provides comprehensive methods for testing, estimating, and conducting uniform inference on quantile treatment effects (QTEs) in sharp regression discontinuity (RD) designs, incorporating covariates and implementing robust bias correction methods of Qu, Yoon, Perron (2024) <doi:10.1162/rest_a_01168>.
Support package for the textbook "An Introduction to
    Quantitative Text Analysis for Linguists: Reproducible Research Using
    R" (Francom, 2024) <doi:10.4324/9781003393764>. Includes functions to
    acquire, clean, and analyze text data as well as functions to document
    and share the results of text analysis. The package is designed to be
    used in conjunction with the book, but can also be used as a standalone
    package for text analysis.
Analysis of experimental crosses to identify genes
  (called quantitative trait loci, QTLs) contributing to variation in
  quantitative traits.
  Broman et al. (2003) <doi:10.1093/bioinformatics/btg112>.
Conduct multiple quantitative trait loci (QTL) and QTL-by-environment interaction (QEI) mapping via ordinary or compressed variance component mixed models with random- or fixed QTL/QEI effects. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve or on each locus curve are viewed as potential main-effect QTLs and QEIs, all their effects are included in a multi-locus model, their effects are estimated by both least angle regression and empirical Bayes (or adaptive lasso) in backcross and F2 populations, and true QTLs and QEIs are identified by likelihood radio test. See Zhou et al. (2022) <doi:10.1093/bib/bbab596> and Wen et al. (2018) <doi:10.1093/bib/bby058>.
Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. See Wen et al. (2018) <doi:10.1093/bib/bby058>.
Provides a set of tools to perform quantitative
    trait locus (QTL) analysis in experimental crosses. It is a
    reimplementation of the 'R/qtl' package to better handle
    high-dimensional data and complex cross designs.
    Broman et al. (2019) <doi:10.1534/genetics.118.301595>.
Functions to convert data structures among the 'qtl2', 'qtl', and 'DOQTL' packages for mapping quantitative trait loci (QTL).
Uses the 'fst' package to store genotype probabilities on disk for the 'qtl2' package. These genotype probabilities are a central data object for mapping quantitative trait loci (QTL), but they can be quite large. The facilities in this package enable the genotype probabilities to be stored on disk, leading to reduced memory usage with only a modest increase in computation time.
Functions to plot QTL (quantitative trait loci) analysis
    results and related diagnostics.
    Part of 'qtl2', an upgrade of the 'qtl'
    package to better handle high-dimensional data and complex cross
    designs.
Routines in 'qtl2' to study allele patterns in quantitative trait loci (QTL)
    mapping over a chromosome.
    Useful in crosses with more than two alleles to identify how sets of alleles,
    genetically different strands at the same locus, have different response levels.
    Plots show profiles over a chromosome.
    Can handle multiple traits together.
    See <https://github.com/byandell/qtl2pattern>.
We implement an
    adaptation of Jiang & Zeng's (1995) <https://www.genetics.org/content/140/3/1111> likelihood ratio test for testing
    the null hypothesis of pleiotropy against the alternative hypothesis,
    two separate quantitative trait loci. The test differs from that in Jiang & Zeng (1995) <https://www.genetics.org/content/140/3/1111> 
    and that in Tian et al. (2016) <doi:10.1534/genetics.115.183624> in
    that our test accommodates multiparental populations.
Datasets for the book, A Guide to QTL Mapping with R/qtl.
    Broman and Sen (2009) <doi:10.1007/978-0-387-92125-9>.
Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates.
Web-based interactive charts (using D3.js) for the analysis of
    experimental crosses to identify genetic loci (quantitative trait
    loci, QTL) contributing to variation in quantitative traits.
    Broman (2015) <doi:10.1534/genetics.114.172742>.
Design of QTL (quantitative trait locus) experiments
  involves choosing which strains to cross, the type of cross,
  genotyping strategies, phenotyping strategies, and the number of
  progeny to raise and phenotype. This package provides tools to help
  make such choices.
  Sen and others (2007) <doi:10.1007/s00335-006-0090-y>.
Functions to infer co-mapping trait hotspots and causal models.
  Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) 
  Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. 
  <doi:10.1534/genetics.112.139451>.
  Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013)
  Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013.
  <doi:10.1534/genetics.112.147124>.
Functions to Simultaneously Infer Causal Graphs and Genetic Architecture.
  Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by
  a few genetic loci (QTL).
  Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010)
  Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes.
  Annals of Applied Statistics 4: 320-339.
  <doi:10.1214/09-AOAS288>.
Performs random-effect multiple interval mapping (REMIM) in full-sib families of autopolyploid species based on restricted maximum likelihood (REML) estimation and score statistics, as described in Pereira et al. (2020) <doi:10.1534/genetics.120.303080>.
This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.
Provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data. 
    Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature.
Functions for unconditional and conditional quantiles. These
    include methods for transformation-based quantile regression,
    quantile-based measures of location, scale and shape, methods for quantiles
    of discrete variables, quantile-based multiple imputation, restricted
    quantile regression, directional quantile classification, and quantile
	ratio regression.
	A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.
Provides three 'Quarto' website templates as an R project, which are commonly used by academics.
    Templates for personal websites and course/workshop websites are included, as well as a template with minimal content for customization.
A tool that can be customized to aid in the clean up of ecological data 
    collected using quadrats and can crop quadrats to ensure comparability between
    quadrats collected under different methodologies.
A range of quadratic forms are evaluated, using efficient methods.
 Unnecessary transposes are not performed.  Complex values are handled
 consistently.
Given inputs A,B and C, this package solves the matrix equation A*X^2 - B*X - C = 0.
Create surface forms from matrix or 'raster' data for flexible plotting and
 conversion to other mesh types. The functions 'quadmesh' or 'triangmesh'
 produce a continuous surface as a 'mesh3d' object as used by the 'rgl'
 package. This is used for plotting raster data in 3D (optionally with
 texture), and allows the application of a map projection without data loss and 
 many processing applications that are restricted by inflexible regular grid rasters.
 There are discrete forms of these continuous surfaces available with
 'dquadmesh' and 'dtriangmesh' functions.
This package contains routines and documentation for
        solving quadratic programming problems.
Extends the quadprog package to solve quadratic programs with
    absolute value constraints and absolute values in the objective function.
Given a dataset, the user is invited to utilize the Empirical Cumulative Distribution Function (ECDF) 
              to guess interactively the mean and the mean deviation. Thereafter,
              using the quadratic curve the user can guess the Root Mean Squared Deviation (RMSD) 
              and visualize the standard deviation (SD). 
              For details, see Sarkar and Rashid (2019)<doi:10.3126/njs.v3i0.25574>, 
              Have You Seen the Standard Deviaton?, Nepalese Journal of Statistics, Vol. 3, 1-10.
It includes test for multivariate normality, test for uniformity on the d-dimensional 
    Sphere, non-parametric two- and k-sample tests, random generation of points from the Poisson 
    kernel-based density and clustering algorithm for spherical data. For more information see
    Saraceno G., Markatou M., Mukhopadhyay R. and Golzy M. (2024)
    <doi:10.48550/arXiv.2402.02290>
    Markatou, M. and Saraceno, G. (2024) <doi:10.48550/arXiv.2407.16374>, 
    Ding, Y., Markatou, M. and Saraceno, G. (2023) <doi:10.5705/ss.202022.0347>, 
    and Golzy, M. and Markatou, M. (2020) <doi:10.1080/10618600.2020.1740713>.
It will assist the user to find simple quadratic roots from any quadratic equation.
Fits classical sparse regression models with
    efficient active set algorithms by solving quadratic problems as described by 
    Grandvalet, Chiquet and Ambroise (2017) <doi:10.48550/arXiv.1210.2077>. Also provides a few
    methods for model selection purpose (cross-validation, stability selection).
Provides functionality for working with raster-like quadtrees
    (also called “region quadtrees”), which allow for variable-sized
    cells. The package allows for flexibility in the quadtree creation
    process.  Several functions defining how to split and aggregate cells
    are provided, and custom functions can be written for both of these
    processes. In addition, quadtrees can be created using other quadtrees
    as “templates”, so that the new quadtree's structure is identical to
    the template quadtree. The package also includes functionality for
    modifying quadtrees, querying values, saving quadtrees to a file, and
    calculating least-cost paths using the quadtree as a resistance
    surface.
Estimate quadratic vector autoregression models with the
    strong hierarchy using the Regularization Algorithm under Marginality
    Principle (RAMP) by Hao et al. (2018)
    <doi:10.1080/01621459.2016.1264956>, compare the performance with
    linear models, and construct networks with partial derivatives.
An easy framework to set a quality control workflow on a dataset.
  Includes a various range of functions that allow to establish an adaptable 
  data quality control.
Provides a set of functions for taking qualitative GIS data, hand drawn on a map, and 
   converting it to a simple features object. These tools are focused on data that are drawn on a map
   that contains some type of polygon features. For each area identified on the map, the id numbers
   of these polygons can be entered as vectors and transformed using qualmap.
Automatic generation of maximally distinct qualitative color palettes,
    optionally tailored to color deficiency. A list of colors or a subspace
    of a color space is used as input and then projected to the DIN99d color space,
    where colors that are maximally distinct are chosen algorithmically.
Provides functions to access survey results directly into R
    using the 'Qualtrics' API. 'Qualtrics'
    <https://www.qualtrics.com/about/> is an online survey and data
    collection software platform. See <https://api.qualtrics.com/> for
    more information about the 'Qualtrics' API.  This package is
    community-maintained and is not officially supported by 'Qualtrics'.
Qualitative methods for the validation of dynamic models.
    It contains (i) an orthogonal set of deviance measures for absolute,
    relative and ordinal scale and (ii) approaches accounting for time
    shifts. The first approach transforms time to take time delays and speed
    differences into account. The second divides the time series into
    interval units according to their main features and finds the longest
    common subsequence (LCS) using a dynamic programming algorithm.
Implements indices of qualitative variation proposed by Wilcox
    (1973).
These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates.
  - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.
Functions for interacting directly with the Quandl API to offer data in a number of formats usable in R, downloading a zip with all data from a Quandl database, and the ability to search. This R package uses the Quandl API. For more information go to <https://docs.quandl.com>. For more help on the package itself go to <https://www.quandl.com/tools/r>.
Values different types of assets and calibrates discount curves 
    for quantitative financial analysis. It covers fixed coupon assets, 
    floating note assets, interest and cross currency swaps with different 
    payment frequencies. Enables the calibration of spot, instantaneous forward 
    and basis curves, making it a powerful tool for accurate and flexible bond 
    valuation and curve generation. The valuation and calibration techniques 
    presented here are consistent with industry standards and incorporates 
    author's own calculations. Tuckman, B., Serrat, A. (2022, ISBN: 978-1-119-83555-4).
Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996) <doi:10.1201/9780203748725> and Perperoglou et al.(2019) <doi:10.1186/s12874-019-0666-3>.
Functions to manipulate dates and count days for quantitative finance analysis. The 'quantdates' package considers leap, holidays and business days for relevant calendars in a financial context to simplify quantitative finance calculations, consistent with International Swaps and Derivatives Association (ISDA) (2006) <https://www.isda.org/book/2006-isda-definitions/> regulations.
An implementation of dimension reduction techniques
    for conditional quantiles. Nonparametric estimation of 
    conditional quantiles is also available.
A fast, flexible, and comprehensive framework for 
    quantitative text analysis in R.  Provides functionality for corpus management,
    creating and manipulating tokens and n-grams, exploring keywords in context, 
    forming and manipulating sparse matrices
    of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and
    distances, applying content dictionaries, applying supervised and unsupervised machine learning, 
    visually representing text and text analyses, and more.
Scaling models and classifiers for sparse matrix objects representing 
    textual data in the form of a document-feature matrix.  Includes original 
    implementations of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>,
    'Wordscores' model, the Perry and 'Benoit' (2017) <doi:10.48550/arXiv.1710.08963> class affinity scaling model, 
    and the 'Slapin' and 'Proksch' (2008) <doi:10.1111/j.1540-5907.2008.00338.x> 'wordfish'
    model, as well as methods for correspondence analysis, latent semantic analysis,
    and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.
Plotting functions for visualising textual data.  Extends 'quanteda' and 
   related packages with plot methods designed specifically for text data, textual statistics, 
   and models fit to textual data. Plot types include word clouds, lexical dispersion plots, 
   scaling plots, network visualisations, and word 'keyness' plots.
Textual statistics functions formerly in the 'quanteda' package.
    Textual statistics for characterizing and comparing textual data. Includes 
    functions for measuring term and document frequency, the co-occurrence of 
    words, similarity and distance between features and documents, feature entropy, 
    keyword occurrence, readability, and lexical diversity.  These functions 
    extend the 'quanteda' package and are specially designed for sparse textual data.
Provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach, the balance approach and the conditional expectations method.
Code for centroid, median and quantile classifiers.
Functions for simulation, estimation, and model
       selection of finite mixtures of Tukey g-and-h
       distributions.
Based on Alan D. Hutson (1999) <doi:10.1080/02664769922458>, "Calculating nonparametric confidence intervals for quantiles using fractional order statistics", Journal of Applied Statistics, 26:3, 343-353.
Estimation and inference methods for the cross-quantilogram.
    The cross-quantilogram is a measure of nonlinear dependence between
    two variables, based on either unconditional or conditional quantile
    functions.  It can be considered an extension of the correlogram,
    which is a correlation function over multiple lag periods that mainly
    focuses on linear dependency.  One can use the cross-quantilogram to
    detect the presence of directional predictability from one time series
    to another.  This package provides a statistical inference method
    based on the stationary bootstrap.  For detailed theoretical and
    empirical explanations, see Linton and Whang (2007) for univariate
    time series analysis and Han, Linton, Oka and Whang (2016) for
    multivariate time series analysis.  The full references for these key
    publications are as follows: (1) Linton, O., and Whang, Y. J. (2007).
    The quantilogram: with an application to evaluating directional
    predictability.  Journal of Econometrics, 141(1), 250-282
    <doi:10.1016/j.jeconom.2007.01.004>; (2) Han, H., Linton, O., Oka, T.,
    and Whang, Y. J. (2016).  The cross-quantilogram: measuring quantile
    dependence and testing directional predictability between time series.
    Journal of Econometrics, 193(1), 251-270
    <doi:10.1016/j.jeconom.2016.03.001>.
Integration of the 'units' and 'errors' packages for a complete
    quantity calculus system for R vectors, matrices and arrays, with automatic
    propagation, conversion, derivation and simplification of magnitudes and
    uncertainties. Documentation about 'units' and 'errors' is provided in the
    papers by Pebesma, Mailund & Hiebert (2016, <doi:10.32614/RJ-2016-061>) and
    by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in
    those packages as vignettes; see 'citation("quantities")' for details.
A re-implementation of quantile kriging. Quantile kriging was described by Plumlee and Tuo (2014) <doi:10.1080/00401706.2013.860919>.  With computational savings when dealing with replication from the recent paper by Binois, Gramacy, and Ludovski (2018) <doi:10.1080/10618600.2018.1458625> it is now possible to apply quantile kriging to a wider class of problems.  In addition to fitting the model, other useful tools are provided such as the ability to automatically perform leave-one-out cross validation.
Specify, build, trade, and analyse quantitative financial trading strategies.
Modifies the distance matrix obtained from data with batch effects, so as to improve the performance of sample pattern detection, such as clustering, dimension reduction, and construction of networks between subjects. The method has been published in Bioinformatics (Fei et al, 2018, <doi:10.1093/bioinformatics/bty117>). Also available on 'GitHub' <https://github.com/tengfei-emory/QuantNorm>.
Estimation methods for optimal treatment regimes under three different criteria, namely marginal quantile, marginal mean, and mean absolute difference. For the first two criteria, both one-stage and two-stage estimation method are implemented. A doubly robust estimator for estimating the quantile-optimal treatment regime is also included.
Contains tools useful for data screening, testing
        moderation (Cohen et. al. 2003)<doi:10.4324/9780203774441>, 
        mediation (MacKinnon et. al. 2002)<doi:10.1037/1082-989x.7.1.83> 
        and estimating power (Murphy & Myors 2014)<ISBN:9781315773155>.
Estimation and inference methods for models for conditional quantile functions: 
  Linear and nonlinear parametric and non-parametric (total variation penalized) models 
  for conditional quantiles of a univariate response and several methods for handling
  censored survival data.  Portfolio selection methods based on expected shortfall
  risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press,
  <doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression, 
  CRC Press, <doi:10.1201/9781315120256>.
Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods.
Quantile Regression Forests is a tree-based ensemble
        method for estimation of conditional quantiles. It is
        particularly well suited for high-dimensional data. Predictor
        variables of mixed classes can be handled. The package is
        dependent on the package 'randomForest', written by Andy Liaw.
Implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying
  coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded
  in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter 
  selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by
  HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies.
  (Toshio Honda, Ching-Kang Ing, Wei-Ying Wu, 2019, <DOI:10.3150/18-BEJ1091>).
Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties.  
    Random intercepts and variable selection are allowed via the lasso penalties.
    The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) <doi:10.1177/1471082X20929802>. Monotonicity and concavity 
    constraints on the fitted curves are allowed, see Muggeo and others (2013) <doi:10.1007/s10651-012-0232-1>, 
    and also <doi:10.13140/RG.2.2.12924.85122> or <doi:10.13140/RG.2.2.29306.21445> some code examples.
This is the implementation of quantile regression forests for the fast random forest package 'ranger'.
A sigmoidal quantile function estimator based on a newly defined 
    generalized expectile function. The generalized sigmoidal quantile function 
    can estimate quantiles beyond the range of the data, which is important for 
    certain applications given smaller sample sizes. The package is based on the 
    method introduced in Hutson (2024) <doi:10.1080/03610918.2022.2032161>.
Methods to determine, smooth and plot quantile periodograms for
    univariate and multivariate time series. See Kley (2016) <doi:10.18637/jss.v070.i03>
    for a description and tutorial.
Contains basic structures and operations used frequently in quantum computing. Intended to be a convenient tool to help learn quantum mechanics and algorithms. Can create arbitrarily sized kets and bras and implements quantum gates, inner products, and tensor products. Creates arbitrarily controlled versions of all gates and can simulate complete or partial measurements of kets. Has functionality to convert functions into equivalent quantum gates and model quantum noise. Includes larger applications, such as Steane error correction <DOI:10.1103/physrevlett.77.793>, Quantum Fourier Transform and Shor's algorithm (Shor 1999), Grover's algorithm (1996), Quantum Approximation Optimization Algorithm (QAOA) (Farhi, Goldstone, and Gutmann 2014) <arXiv:1411.4028>, and a variational quantum classifier (Schuld 2018) <arXiv:1804.00633>. Can be used with the gridsynth algorithm <arXiv:1212.6253> to perform decomposition into the Clifford+T set.
Enables the user to calculate Value at Risk (VaR)
    and Expected Shortfall (ES) by means of various types of historical
    simulation. Currently plain-, age-, volatility-weighted- and filtered
    historical simulation are implemented in this package. Volatility weighting
    can be carried out via an exponentially weighted moving average model
    (EWMA) or other GARCH-type models. The performance can be assessed via
    Traffic Light Test, Coverage Tests and Loss Functions. The methods of the
    package are described in Gurrola-Perez, P. and Murphy, D. (2015)
    <https://EconPapers.repec.org/RePEc:boe:boeewp:0525> as well as McNeil, J.,
    Frey, R., and Embrechts, P. (2015) <https://ideas.repec.org/b/pup/pbooks/10496.html>.
An implementation of two interaction indices between extractive
    activity and groundwater resources based on hazard and vulnerability
    parameters used in the assessment of natural hazards. One index is based
    on a discrete choice model and the other is relying on an artificial
    neural network.
Calculates the number of four-taxon subtrees consistent with a pair
  of cladograms, calculating the symmetric quartet distance of Bandelt & Dress
  (1986), Reconstructing the shape of a tree from observed dissimilarity data,
  Advances in Applied Mathematics, 7, 309-343 <doi:10.1016/0196-8858(86)90038-2>, 
  and using the tqDist algorithm of Sand et al. (2014), tqDist: a library for
  computing the quartet and triplet distances between binary or general trees,
  Bioinformatics, 30, 2079–2080 <doi:10.1093/bioinformatics/btu157>
  for pairs of binary trees.
In the spirit of Anscombe's quartet, this package includes datasets
   that demonstrate the importance of visualizing your data, the importance of 
   not relying on statistical summary measures alone, and why additional 
   assumptions about the data generating mechanism are needed when estimating 
   causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>,
   D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.48550/arXiv.2304.02683>, 
   "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and
   Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.
Convert R Markdown documents and 'Jupyter' notebooks to a
    variety of output formats using 'Quarto'.
Functions for estimating the potential dispersal of tree species 
  using regeneration densities and dispersal distances to nearest seed trees. 
  A quantile regression is implemented to determine the dispersal potential.
  Spatial prediction can be used to identify natural regeneration potential for forest restoration as described in Axer et al (2021) <doi:10.1016/j.foreco.2020.118802>.
Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883  <doi:10.1214/19-EJS1640>.
Syntax for defining complex filtering expressions in a programmatic way.
  A filtering query, built as a nested list configuration, can be easily stored in other formats like 'YAML' or 'JSON'.
  What's more, it's possible to convert such configuration to a valid expression that can be applied to 
  popular 'dplyr' package operations.
Translate 'SQL' 'SELECT' statements into lists of 'R' expressions.
Retrieve protein information from
    the 'UniProtKB' REST API (see <https://www.uniprot.org/help/api_queries>).
Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame.
Set of functions to make the processing and analysis of
    surveys easier : interactive shiny apps and addins for data recoding,
    contingency tables, dataset metadata handling, and several convenience
    functions.
A method for prediction of environmental conditions based on 
    transcriptome data linked with the environmental gradients. This package 
    provides functions to overview gene-environment relationships, to construct 
    the prediction model, and to predict environmental conditions where the 
    transcriptomes were generated. This package can quest for candidate genes 
    for the model construction even in non-model organisms' transcriptomes 
    without any genetic information.
Implementation of a computationally efficient method for
    simulating queues with arbitrary arrival and service times. 
    Please see Ebert, Wu, Mengersen & Ruggeri (2020, <doi:10.18637/jss.v095.i05>) 
    for further details.
It provides versatile tools for analysis of birth and death based Markovian Queueing Models
    and Single and Multiclass Product-Form Queueing Networks.
    It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K,
    Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks,
    Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks
    and Single Channel Multiple Class Mixed Networks.
    Also it provides a B-Erlang, C-Erlang and Engset calculators.
    This work is dedicated to the memory of D. Sixto Rios Insua.
Provides functions for assigning treatments in randomized experiments using
    near-optimal threshold blocking. The package is made with large data sets in
    mind and derives blocks more than an order of magnitude quicker than other
    methods.
Property based testing, inspired by 
    the original 'QuickCheck'. This package builds on 
    the property based testing framework provided by 
    'hedgehog' and is designed to seamlessly integrate with 
    'testthat'.
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to improve your scripts. Improve the quality and reproducibility of 'R' scripts.
An 'R' interface to the 'QuickJS' portable 'JavaScript'
    engine.  The engine and all 'R' to 'JavaScript' interoperability is bundled
    within the package, requiring no dependencies beyond a 'C' compiler.
Provides functions for constructing near-optimal generalized full matching.
    Generalized full matching is an extension of the original full matching method
    to situations with more intricate study designs. The package is made with
    large data sets in mind and derives matches more than an order of magnitude
    quicker than other methods.
For fitting N-mixture models using either FFT or asymptotic approaches. FFT N-mixture models extend the work of Cowen et al. (2017) <doi:10.1111/biom.12701>. Asymptotic N-mixture models extend the work of Dail and Madsen (2011) <doi:10.1111/j.1541-0420.2010.01465.x>, to consider asymptotic solutions to the open population N-mixture models. The FFT models are derived and described in "Parker, M.R.P., Elliott, L., Cowen, L.L.E. (2022). Computational efficiency and precision for replicated-count and batch-marked hidden population models [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". The asymptotic models are derived and described in: "Parker, M.R.P., Elliott, L., Cowen, L.L.E., Cao, J. (2022). Fast asymptotic solutions for N-mixtures on large populations [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.".