Evaluating risk (that a patient arises a side effect) during hospitalization is the main purpose of this package. Several methods (Parametric, non parametric and De Vielder estimation) to estimate the risk constant (R) are implemented in this package. There are also functions to simulate the different models of this issue in order to quantify the previous estimators. It is necessary to read at least the first six pages of the report to understand the topic.
Microbenchmarks for determining the run time
  performance of aspects of the R programming environment and packages
  relevant to high-performance computation.  The benchmarks are divided into
  three categories: dense matrix linear algebra kernels, sparse matrix linear
  algebra kernels, and machine learning functionality.
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'OpenBLAS', 'ACML', 'BLIS' and 'MKL').
  And possible to control the number of threads in 'OpenMP'.
  Get a number of logical cores and physical cores if feasible.
Methods to scan RR interval data for Premature Ventricular Complexes (PVCs) and parameterise and plot the resulting Heart Rate Turbulence (HRT). The methodology of HRT analysis is based on the original publication by Schmidt et al. <doi:10.1016/S0140-6736(98)08428-1> and extended with suggestions from <doi:10.1088/1361-6579/ab98b3>.
Allows users to import data files containing heartbeat	positions in the most broadly used formats, to remove outliers or points with unacceptable physiological values present in the time series, to plot HRV data, and to perform time domain, frequency domain and nonlinear HRV analysis. See Garcia et al. (2017) <DOI:10.1007/978-3-319-65355-6>.
The Ryan-Holm step-down Bonferroni or Sidak procedure is to control the family-wise (experiment-wise) type I error rate in the multiple comparisons.
    This procedure provides the adjusting p-values and adjusting CIs. The methods used in this package are referenced from John Ludbrook (2000) <doi:10.1046/j.1440-1681.2000.03223.x>.
Run 'R CMD check' on any of the 'R-hub' (<https://builder.r-hub.io/>)
    architectures, from the command line. The current architectures include
    'Windows', 'macOS', 'Solaris' and various 'Linux' distributions.
Tool for the analysis Mass Spectrometry (MS) data in the context of 
             immunopeptidomic analysis for the identification of hybrid 
             peptides and the predictions of binding affinity of all peptides 
             using 'netMHCpan' <doi:10.1093/nar/gkaa379> while providing a summary 
             of the netMHCpan output. 'RHybridFinder' (RHF) is destined for 
             researchers who are looking to analyze their MS data for the purpose 
             of identification of potential spliced peptides. This package, 
             developed mainly in base R, is based on the workflow published by 
             Faridi et al. in 2018 <doi:10.1126/sciimmunol.aar3947>.
Wrapper for 'Datamuse' API to find rhyming and other associated words.
    This includes words of similar meaning, spelling, or other related words. Learn
    more about the 'Datamuse' API here <https://www.datamuse.com/api/>.
Create and manipulate hypergraph objects. This early version of
    rhype allows for the output of matrices associated with the hypergraphs 
    themselves. It also uses these matrices to calculate hypergraph spectra and
    perform spectral comparison. Functionality coming soon includes calculation
    of hyperpaths and hypergraph centrality measures.
Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.
Computes attributable effects based confidence interval, permutation
    test confidence interval, or asymptotic confidence interval for the
    the average treatment effect on a binary outcome.
Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI,
             nrrd and npy (numpy array) file formats.
             RIA calculates first-order, gray level co-occurrence matrix, gray level run length matrix and
             geometry-based statistics. Almost all calculations are done using vectorized formulas to
             optimize run speeds. Calculation of several thousands of parameters only takes minutes
             on a single core of a conventional PC. Detailed methodology has been published: Kolossvary
             et al. Circ: Cardiovascular Imaging. 2017;10(12):e006843 <doi: 10.1161/CIRCIMAGING.117.006843>.
Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.
Allows interaction with 'Interactive Brokers' 'Trader Workstation'
    <https://interactivebrokers.github.io/tws-api/>.
    Handles the connection over the network and the exchange of messages.
    Data is encoded and decoded between user and wire formats.
    Data structures and functionality closely mirror the official implementations.
Recursive algorithms for computing various relatedness
    coefficients, including pairwise kinship, kappa and identity
    coefficients. Both autosomal and X-linked coefficients are computed.
    Founders are allowed to be inbred, which enables construction of any
    given kappa coefficients, as described in Vigeland (2020)
    <doi:10.1007/s00285-020-01505-x>. In addition to the standard
    coefficients, 'ribd' also computes a range of lesser-known
    coefficients, including generalised kinship coefficients (Karigl
    (1981) <doi:10.1111/j.1469-1809.1981.tb00341.x>; Weeks and Lange
    (1988) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1715269>),
    two-locus coefficients (Thompson (1988) <doi:10.1093/imammb/5.4.261>)
    and multi-person coefficients. This package is part of the 'ped
    suite', a collection of packages for pedigree analysis in R. Several
    methods of 'ribd' are featured in the online app 'QuickPed' available
    at <https://magnusdv.shinyapps.io/quickped>. See Vigeland (2022)
    <doi:10.1186/s12859-022-04759-y>.
The provided benchmark suite enables the automated evaluation and comparison of any existing and novel indirect method for reference interval ('RI') estimation in a systematic way.
	Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and use sophisticated
	statistical methods to derive a model describing the distribution of the non-pathological samples, which can then be 
	used to derive reference intervals. The benchmark suite contains 5,760 simulated test sets with varying difficulty.
	To include any indirect method, a custom wrapper function needs to be provided. 
	The package offers functions for generating the test sets, executing the indirect method and evaluating the results.
	See ?RIbench or vignette("RIbench_package") for a more comprehensive description of the features. 
	A detailed description and application is described in Ammer T., Schuetzenmeister A., Prokosch H.-U., Zierk J., Rank C.M., Rauh M. "RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation". Clinical Chemistry (2022) <doi:10.1093/clinchem/hvac142>.
Gene annotation of rice (Oryza Sativa L.spp.japonica).
    The package is based on the annotation file from the website <http://plants.ensembl.org/Oryza_sativa/Info/Index>. 
    Input gene's name then return some information, including the from position, the end position, 
    the position type and the chromosome number.
Convert one biological ID to another of rice (Oryza sativa).
    Rice(Oryza sativa) has more than one form gene ID for the genome. 
    The two main gene ID for rice genome are  the RAP (The Rice Annotation Project, <https://rapdb.dna.affrc.go.jp/>, 
    and the MSU(The Rice Genome Annotation Project, <http://rice.plantbiology.msu.edu/>.
    All RAP rice gene IDs are of the form Os##g####### as explained on the website <https://rapdb.dna.affrc.go.jp/>.
    All MSU rice gene IDs are of the form LOC_Os##g##### as explained on the website <http://rice.plantbiology.msu.edu/analyses_nomenclature.shtml>.
    All SYMBOL rice gene IDs are the unique name on the NCBI(National Center for Biotechnology Information, <https://www.ncbi.nlm.nih.gov/>.
    The TRANSCRIPTID, is the transcript id of rice, are of the form Os##t#######.
    The researchers usually need to converter between various IDs. Such as converter RAP to SYMBOLS for function searching on NCBI.
    There are a lot of websites with the function for converting RAP to MSU or MSU to RA, such as 'ID Converter' <https://rapdb.dna.affrc.go.jp/tools/converter>.
    But it is difficult to convert super multiple IDs on these websites.
    The package can convert all IDs between the three IDs (RAP, MSU and SYMBOL) regardless of the number.
The Diceware method can be used to generate strong passphrases.
  In short, you roll a 6-faced dice 5 times in a row, the number obtained is
  matched against a dictionary of easily remembered words. By combining together 7
  words thus generated, you obtain a password that is relatively easy to remember,
  but would take several millions years (on average) for a powerful computer to guess.
Computes rarefaction curves, cumulated and mean species
        richness. Compares these estimates by means of randomization
        tests.
Methods for randomization inference in group-randomized trials. Specifically, it can be used to analyze the treatment effect of stratified data with multiple clusters in each stratum with treatment given on cluster level. 
    User may also input as many covariates as they want to fit the data. Methods are described by Dylan S Small et al., (2012) <doi:10.1198/016214507000000897>.
Focused on (but not exclusive to) data sets hosted on PhysioNet
    (<https://physionet.org>), 'ricu' provides utilities for download, setup
    and access of intensive care unit (ICU) data sets. In addition to
    functions for running arbitrary queries against available data sets, a
    system for defining clinical concepts and encoding their representations
    in tabular ICU data is presented.
For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data on idiograms with no restriction of species.
Linear and logistic ridge regression functions. Additionally includes special functions for 
            genome-wide single-nucleotide polymorphism (SNP) data. More details can be found in
            <doi: 10.1002/gepi.21750> and <doi: 10.1186/1471-2105-12-372>.
Implementation of a Principal Component Analysis (PCA) in the
    torus via density ridge estimation. The main function, ridge_pca(), obtains
    the relevant density ridge for bivariate sine von Mises and bivariate
    wrapped Cauchy distribution models and provides the associated scores and
    variance decomposition. Auxiliary functions for evaluating, fitting, and
    sampling these models are also provided. The package provides replicability
    to García-Portugués and Prieto-Tirado (2022) <arXiv:2212.10856>.
It is a package that provides alternative approach for finding optimum parameters of ridge regression. This package focuses on finding the ridge parameter value k which makes the variance inflation factors closest to 1, while keeping them above 1 as addressed by Michael Kutner, Christopher Nachtsheim, John Neter, William Li (2004, ISBN:978-0073108742). Moreover, the package offers end-to-end functionality to find optimum k value and presents the detailed ridge regression results. Finally it shows three sets of graphs consisting k versus variance inflation factors, regression coefficients and standard errors of them.
An interface to iDigBio's search API that allows downloading
    specimen records. Searches are returned as a data.frame. Other
    functions such as the metadata end points return lists of information.
    iDigBio is a US project focused on digitizing and serving museum
    specimen collections on the web. See <https://www.idigbio.org> for
    information on iDigBio.
Functions to compute ridit scores of vectors, compute mean ridits
  and their standard errors for vectors compared to a reference vector,
  as described in Fleiss (1981, ISBN:0-471-06428-9), and compute
  means/SEs for multiple groups in matrices. Data can be either
  counts or proportions. Emphasis is on ridit analysis of ordered categorical
  data such as Likert items and pain-rating scales.
Allows to get weather data from Automated Surface Observing
    System (ASOS) stations (airports) in the whole world thanks to the
    Iowa Environment Mesonet website.
We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.
We provide a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. Also, C++ header files are contained that implement elementary operations on manifolds such as Sphere, Grassmann, and others. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> if you are interested in statistics on manifolds, and Absil et al (2007, ISBN:9780691132983) on computational aspects of optimization on matrix manifolds.
Retrieves efficiently and reliably Investors Exchange ('IEX') stock and market data using 'IEX Cloud API'. The platform is offered by Investors Exchange Group (IEX Group).
    Main goal is to leverage 'R' capabilities including existing packages to effectively provide financial and statistical analysis as well as visualization in support of fact-based decisions.
    In addition, continuously improve and enhance 'Riex' by applying best practices and being in tune with users' feedback and requirements.
    Please, make sure to review and acknowledge Investors Exchange Group (IEX Group) terms and conditions before using 'Riex' (<https://iexcloud.io/terms/>).
Implements the algorithms for solving sparse generalized eigenvalue problem by Tan, et. al. (2018). Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow.  To appear in Journal of the Royal Statistical Society: Series B. <arXiv:1604.08697>.
Pointwise generation and display of attractors (prefractals) of the random iterated function system (RIFS) for various combinations of probabilistic and geometric parameters of some fixed point sets (protofractals), described by Bukhovets A.G. (2012) <doi:10.1134/S0005117912020154>.
The goal of Rigma is to provide a user friendly client to the 'Figma'
 API <https://www.figma.com/developers/api>. It uses the latest `httr2` for a 
 stable interface with the REST API. More than 20 methods are provided to 
 interact with 'Figma' files, and teams. Get design data into R by reading 
 published components and styles, converting and downloading images, getting 
 access to the full 'Figma' file as a hierarchical data structure, and much 
 more. Enhance your creativity and streamline the application development by
 automating the extraction, transformation, and loading of design data to your 
 applications and 'HTML' documents.
A set of tools to streamline data analysis. Learning both R and introductory statistics at the same time can be challenging, and so we created 'rigr' to facilitate common data analysis tasks and enable learners to focus on statistical concepts. We provide easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. 'rigr' output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroscedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function can fit both linear and generalized linear models, allowing students to more easily make connections between different classes of models.
Functionality to download stock prices,
    cryptocurrency data, and more from the 'Tiingo' API
    <https://api.tiingo.com/>.
Tools to download data from the ilostat database
    <https://ilostat.ilo.org> together with search and
    manipulation utilities.
Provides functions to read 'ImageJ' (<http://imagej.nih.gov/ij/>)
    Region of Interest (ROI) files, to plot the ROIs and to convert them to
    'spatstat' (<http://spatstat.org/>) spatial patterns.
A pure R implementation of the median cut algorithm.
    Extracts the dominant colors from an image, and turns them into
    a scale for use in plots or for fun!
The metrics() function calculates measures of scholarly impact. 
    These include conventional measures, such as the number of publications
    and the total citations to all publications, as well as modern and 
    robust metrics based on the vector of citations associated with each 
    publication, such as the h index and many of its variants or rivals.
    These methods are described in Ruscio et al. (2012) 
    <DOI: 10.1080/15366367.2012.711147>.
Tools for manipulating, exploring, and visualising  multiple-response data, including scored or ranked responses. Conversions to and from factors, lists, strings, matrices; reordering, lumping, flattening; set operations; tables; frequency and co-occurrence plots.
A programmatic interface to the API provided by the 'iNaturalist' website <https://www.inaturalist.org/> to download species occurrence data submitted by citizen scientists.
Relative, generalized, and Erreygers corrected concentration index; plot Lorenz curves; and decompose health
    inequalities into contributing factors. The package currently works with (generalized) linear models, survival models, complex survey models, and marginal effects probit models.
    originally forked by Brecht Devleesschauwer from the 'decomp' package  (no longer on CRAN), rineq is now maintained by Kaspar Walter Meili. Compared to the earlier 'rineq' version on 'github' by Brecht Devleesschauwer (<https://github.com/brechtdv/rineq>), the regression tree functionality has been removed.
    Improvements compared to earlier versions include improved plotting of decomposition and concentration, added functionality to calculate the concentration index with different methods, calculation of robust standard errors, and support for the decomposition analysis using marginal effects probit regression models. The development version is available at <https://github.com/kdevkdev/rineq>.
Circular / ring buffers in R and C.  There are a couple
    of different buffers here with different implementations that
    represent different trade-offs.
Loading calls data from 'Ringostat API'. 
    See <https://help.ringostat.com/knowledge-base/article/integration-with-ringostat-via-api>.
Installs shiny apps packaged as stand-alone Electron apps using Inno Setup, an open source software that builds installers for Windows programs <http://www.jrsoftware.org/ishelp/>.
C++ classes to embed R in C++ (and C) applications
 A C++ class providing the R interpreter is offered by this package
 making it easier to have "R inside" your C++ application. As R itself
 is embedded into your application, a shared library build of R is
 required. This works on Linux, OS X and even on Windows provided you
 use the same tools used to build R itself. Numerous examples are
 provided in the nine subdirectories of the examples/ directory of
 the installed package: standard, 'mpi' (for parallel computing), 'qt'
 (showing how to embed 'RInside' inside a Qt GUI application), 'wt'
 (showing how to build a "web-application" using the Wt toolkit),
 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for
 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C
 interface and 'Ruby' illustration.  The examples use 'GNUmakefile(s)'
 with GNU extensions, so a GNU make is required (and will use the
 'GNUmakefile' automatically). 'Doxygen'-generated documentation of
 the C++ classes is available at the 'RInside' website as well.
Functions to calculate several ecological indices of individual 
    and population niche width (Araujo's E, clustering and pairwise similarity 
    among individuals, IS, Petraitis' W, and Roughgarden's WIC/TNW) to assess 
    individual specialization based on data of resource use. Resource use can 
    be quantified by counts of categories, measures of mass or length, or 
    proportions. Monte Carlo resampling procedures are available for hypothesis 
    testing against multinomial null models.
    Details are provided in Zaccarelli et al. (2013) <doi:10.1111/2041-210X.12079>
    and associated references.
The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided as a data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, to plot the curves and their underlying data, to calibrate radiocarbon dates and to transform between different radiocarbon 'domains'.
This tool can be used to build binary interval trees using real number inputs. 
    The tree supports queries of intervals overlapping a single number or an interval (start, end). 
    Intervals with same bounds but different names are treated as distinct intervals. 
    Insertion of intervals is also allowed. Deletion of intervals is not implemented at this point.
    See  Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars (2008). Computational Geometry: Algorithms and Applications, for a reference.
Allows the user to view an image in full screen when clicking on it in 'RMarkdown' documents and 'shiny' applications. 
  The package relies on the 'JavaScript' library 'intense-images'. See <https://tholman.com/intense-images/> for more information.
A wrapper for the 'Intro.js' library (For more info: <https://introjs.com/>). 
  This package makes it easy to include step-by-step introductions, and clickable hints in a 'Shiny' 
  application. It supports both static introductions in the UI, and programmatic introductions from 
  the server-side.
Streamlined data import and export by making assumptions that
    the user is probably willing to make: 'import()' and 'export()' determine
    the data structure from the file extension, reasonable defaults are used for
    data import and export (e.g., 'stringsAsFactors=FALSE'), web-based import is
    natively supported (including from SSL/HTTPS), compressed files can be read
    directly without explicit decompression, and fast import packages are used where
    appropriate. An additional convenience function, 'convert()', provides a simple
    method for converting between file types.
Constrained clustering, transfer functions, and other methods for analysing Quaternary science data.
Ports the Ripser <arXiv:1908.02518> and Cubical Ripser
        <arXiv:2005.12692> persistent homology calculation engines from
        C++. Can be used as a rapid calculation tool in topological
        data analysis pipelines.
Parameter estimation, computation of probability, information, and 
    (log-)likelihood, and visualization of item/test characteristic curves and
    item/test information functions for three uni-dimensional item response theory
    models: the 3-parameter-logistic model, generalized partial credit model, 
    and graded response model. The full documentation and tutorials are at 
    <https://github.com/xluo11/Rirt>.
Numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events, competing events, and multi-state data. For multistate data, semi-Markov model with interval censoring may be considered, and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables. For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders. Finally, several functions are available to assess time-dependent ROC curves or survival curves from aggregated data.
Implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) 
    <doi:10.19173/irrodl.v18i2.2952>.	Automates the process of identifying learning materials that are not effectively supporting student learning in 
    technology-mediated courses by synthesizing information about access to course content and performance on assessments.
Fits an Ising model to a binary dataset using L1 regularized
    logistic regression and extended BIC. Also includes a fast lasso logistic
    regression function for high-dimensional problems. Uses the 'libLBFGS'
    optimization library by Naoaki Okazaki.
Computes 26 financial risk measures for any continuous distribution.  The 26 financial risk measures  include value at risk, expected shortfall due to Artzner et al. (1999) <DOI:10.1007/s10957-011-9968-2>, tail conditional median due to Kou et al. (2013) <DOI:10.1287/moor.1120.0577>, expectiles due to Newey and Powell (1987) <DOI:10.2307/1911031>, beyond value at risk due to Longin (2001) <DOI:10.3905/jod.2001.319161>, expected proportional shortfall due to Belzunce et al. (2012) <DOI:10.1016/j.insmatheco.2012.05.003>, elementary risk measure due to Ahmadi-Javid (2012) <DOI:10.1007/s10957-011-9968-2>, omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa  due to Kaplan and Knowles  (2004), Wang (1998)'s <DOI:10.1080/10920277.1998.10595708> risk measures, Stone (1973)'s <DOI:10.2307/2978638> risk measures, Luce (1980)'s <DOI:10.1007/BF00135033> risk measures, Sarin (1987)'s <DOI:10.1007/BF00126387> risk measures, Bronshtein and Kurelenkova (2009)'s risk measures.
A collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) <doi:10.1002/sim.5902>.
Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting.  It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) <doi:10.1016/0270-0255(86)90088-6> and has been described in detail by Ahern, Hubbard, and Galea (2009) <doi:10.1093/aje/kwp015>; Snowden, Rose, and Mortimer (2011) <doi:10.1093/aje/kwq472>; and Westreich et al. (2012) <doi:10.1002/sim.5316>.
Facilities for assessing R packages against a number of metrics to 
    help quantify their robustness.
Fast design of risk parity portfolios for financial investment.
    The goal of the risk parity portfolio formulation is to equalize or distribute
    the risk contributions of the different assets, which is missing if we simply
    consider the overall volatility of the portfolio as in the mean-variance
    Markowitz portfolio. In addition to the vanilla formulation, where the risk
    contributions are perfectly equalized subject to no shortselling and budget
    constraints, many other formulations are considered that allow for box
    constraints and shortselling, as well as the inclusion of additional
    objectives like the expected return and overall variance. See vignette for
    a detailed documentation and comparison, with several illustrative examples.
    The package is based on the papers:
    Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods
    for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63,
    no. 19, pp. 5285-5300. <doi:10.1109/TSP.2015.2452219>.
    F. Spinu (2013), An Algorithm for Computing Risk Parity Weights.
    <doi:10.2139/ssrn.2297383>.
    T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing
    High-dimensional risk parity portfolios. <arXiv:1311.4057>.
Collection of functions designed to compute risk-based portfolios as described 
    in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.
Assessing and comparing risk prediction rules for clustered data. The method is based on the paper: Rosner B, Qiu W, and Lee MLT.(2013) <doi: 10.1007/s10985-012-9240-6>.
Computes risk measures from data, as well as performs risk management procedures.
Implementation of the following methods for event history analysis.
    Risk regression models for survival endpoints also in the presence of competing
    risks are fitted using binomial regression based on a time sequence of binary
    event status variables. A formula interface for the Fine-Gray regression model
    and an interface for the combination of cause-specific Cox regression models.
    A toolbox for assessing and comparing performance of risk predictions (risk
    markers and risk prediction models). Prediction performance is measured by the
    Brier score and the area under the ROC curve for binary possibly time-dependent
    outcome. Inverse probability of censoring weighting and pseudo values are used
    to deal with right censored data. Lists of risk markers and lists of risk models
    are assessed simultaneously. Cross-validation repeatedly splits the data, trains
    the risk prediction models on one part of each split and then summarizes and
    compares the performance across splits.
Risk ratios and risk differences are estimated using regression 
  models that allow for binary, categorical, and continuous exposures and 
  confounders. Implemented are marginal standardization after fitting logistic 
  models (g-computation) with delta-method and bootstrap standard errors, 
  Miettinen's case-duplication approach (Schouten et al. 1993, 
  <doi:10.1002/sim.4780121808>), log-binomial (Poisson) models with empirical 
  variance (Zou 2004, <doi:10.1093/aje/kwh090>), binomial models with starting 
  values from Poisson models (Spiegelman and Hertzmark 2005, 
  <doi:10.1093/aje/kwi188>), and others.
Compute time-dependent Incident/dynamic accuracy measures
        (ROC curve, AUC, integrated AUC )from censored survival data
        under proportional or non-proportional hazard assumption of
        Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).
Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals.
Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent.
A set of tools to extract bibliographic content from the National
    Center for Biotechnology Information (NCBI) databases, including PubMed. The
    name RISmed is a portmanteau of RIS (for Research Information Systems, a common
    tag format for bibliographic data) and PubMed.
Automated performance of common transformations used to fulfill parametric
    assumptions of normality and identification of the best performing method 
    for the user. Output for various normality tests (Thode, 2002) corresponding 
    to the best performing method and a descriptive statistical report of the 
    input data in its original units (5-number summary and mathematical moments) 
    are also presented. Lastly, the Rankit, an empirical normal quantile transformation 
    (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard 
    use cases and facilitate adoption.  
    <DOI: 10.1201/9780203910894>.
    <DOI: 10.22237/jmasm/1257034080>.
Implements the simulation and regression of
        integrated Isothermal Titration Calorimetry (ITC) data using
        the most commonly used one-to-one binding reaction model.
An interface to the Integrated Taxonomic Information System ('ITIS')
    (<https://www.itis.gov>). Includes functions to work with the 'ITIS' REST
    API methods (<https://www.itis.gov/ws_description.html>), as well as the
    'Solr' web service (<https://www.itis.gov/solr_documentation.html>).
Tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008)  <doi:10.1214/08-STS254> . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.
Finds a robust instrumental variables estimator using a
             high breakdown point S-estimator of multivariate location
             and scatter matrix.
Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function.
Indices for assessing riverscape fragmentation, including the Dendritic Connectivity Index, the Population Connectivity Index, the River Fragmentation Index, the Probability of Connectivity, and the Integral Index of connectivity. For a review, see Jumani et al. (2020) <doi:10.1088/1748-9326/abcb37> and Baldan et al. (2022) <doi:10.1016/j.envsoft.2022.105470> Functions to calculate temporal indices improvement when fragmentation due to barriers is reduced are also included.
Reads river network shape files and computes network distances.
    Also included are a variety of computation and graphical tools designed 
    for fisheries telemetry research, such as minimum home range, kernel density 
    estimation, and clustering analysis using empirical k-functions with 
    a bootstrap envelope.  Tools are also provided for editing the river 
    networks, meaning there is no reliance on external software.
Functions for reading, analysing and plotting river networks.
  For this package, river networks consist of sections and nodes with associated attributes, 
  e.g. to characterise their morphological, chemical and biological state.
  The package provides functions to read this data from text files, to analyse the network
  structure and network paths and regions consisting of sections and nodes that fulfill
  prescribed criteria, and to plot the river network and associated properties.
Sankey plots are a type of diagram that is convenient to
    illustrate how flow of information, resources etc. separates and joins,
    much like observing how rivers split and merge. For example, they can be
    used to compare different clusterings.
It is devoted to the IVIVC linear level A with numerical deconvolution method. The latter is working for inequal and incompatible timepoints between impulse and response curves. A numerical convolution method is also available. Application domains include pharamaceutical industry QA/QC and R&D together with academic research.
Seamless extraction of river networks from digital 
    elevation models data. The package allows analysis of digital 
	elevation models that can be either externally provided or
	downloaded from open source repositories (thus interfacing
	with the 'elevatr' package). Extraction is performed via the 
	'D8' flow direction algorithm of TauDEM (Terrain Analysis Using 
	Digital Elevation Models), thus interfacing with the 'traudem' 
	package. Resulting river networks are compatible with functions 
	from the 'OCNet' package.
A tool for undergraduate and graduate courses in open-channel
    hydraulics. Provides functions for computing normal and critical depths,
    steady-state water surface profiles (e.g. backwater curves) and unsteady flow
    computations (e.g. flood wave routing) as described in
    Koohafkan MC, Younis BA (2015). "Open-channel computation with R."
    The R Journal, 7(2), 249–262. <doi: 10.32614/RJ-2015-034>.
Jade is a high performance template engine heavily influenced by
    Haml and implemented with JavaScript for node and browsers.
Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.
This is the official 'Jazz' client. 'Jazz' is a lightweight modular data processing
    framework, including a web server. It provides data persistence and computation capabilities
    accessible from 'R' and 'Python' and also through a REST API. <https://github.com/bbvadata/Jazz>
    See ?rjazz::rjazz to get a 'Jazz' server.
Clustering algorithm for high dimensional data. Assuming that P feature measurements on N objects are arranged in an N×P matrix X, this package provides clustering based on the left Gram matrix XX^T. To simulate test data, type "help('simulate_HD_data')" and to learn how to use the clustering algorithm, type "help('RJclust')". To cite this package, type 'citation("RJcluster")'.
The RJDBC package is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.
Interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially
    recommended to the members of the European Statistical System (ESS) and the European System of Central Banks.
    It offers full access to all options and outputs of 'JDemetra+', including the two leading seasonal adjustment methods
    TRAMO/SEATS+ and X-12ARIMA/X-13ARIMA-SEATS.
Functions to have nice 'rmarkdown' outputs of the 
  seasonal and trading day adjustment models made with 'RJDemetra'.
Add-in to the 'RJDemetra' package on seasonal adjustments.
    It allows to produce quality assessments outputs (dashboards, quality report matrix, etc.).
A series of functions in some way considered useful to the author.  These 
    include methods for subsetting tables and generating indices for arrays, 
    conditioning and intervening in probability distributions, generating 
    combinations, fast transformations, and more...
Performs reversible-jump Markov chain Monte Carlo (Green, 1995)
    <doi:10.2307/2337340>, specifically the restriction introduced by 
    Barker & Link (2013) <doi:10.1080/00031305.2013.791644>. By utilising 
    a 'universal parameter' space, RJMCMC is treated as a Gibbs sampling 
    problem. Previously-calculated posterior distributions are used to 
    quickly estimate posterior model probabilities. Jacobian matrices are 
    found using automatic differentiation. For a detailed description of
    the package, see Gelling, Schofield & Barker (2019)
    <doi:10.1111/anzs.12263>.
Implementation of the Johnson Quantile-Parameterised Distribution in R. 
  The Johnson Quantile-Parameterised Distribution (J-QPD) is a flexible distribution
  system that is parameterised by a symmetric percentile triplet of quantile values
  (typically the 10th-50th-90th) along with known support bounds for the distribution.
  The J-QPD system was developed by Hadlock and Bickel (2017) <doi:10.1287/deca.2016.0343>.
  This package implements the density, quantile, CDF and random number generator functions.
Provides functions to retrieve data and metadata from providers 
			 that disseminate data by means of SDMX web services. 
			 SDMX (Statistical Data and Metadata eXchange) is a standard that 
			 has been developed with the aim of simplifying the exchange of 
			 statistical information. 
			 More about the SDMX standard and the SDMX Web Services 
			 can be found at: <https://sdmx.org>.
Converts R object into JSON objects and vice-versa.
The 'jsoncons'
    <https://danielaparker.github.io/jsoncons/> 'C++' header-only
    library constructs representations from a 'JSON' character vector,
    and provides extensions for flexible queries and other operations
    on 'JSON' objects. This package has simple 'R' wrappers to support
    'JSONpath' and 'JMESpath' queries into 'JSON' strings or 'R'
    objects. The 'jsoncons' library is also be easily linked to other
    packages for direct access to 'C++' functionality.
This is a package that allows conversion to and from 
  data in Javascript object notation (JSON) format.
  This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code
  and allows R programmers to read and convert JSON content to R objects.
  This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON
  and was not extensible.  rjson's performance is now similar to this package, and perhaps slightly faster in some cases.
  This package uses methods and is readily extensible by defining methods for different classes, 
  vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R. 
  The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional
  options to allow customizing the generation and processing of JSON content.
  This package uses libjson rather than implementing yet another JSON parser. The aim is to support
  other general projects by building on their work, providing feedback and benefit from their ongoing development.
Creates interactive graphs with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package.
Handle 'JSON-stat' format (<https://json-stat.org>) in R.
    Not all features are supported, especially the extensive metadata
    features of 'JSON-stat'.
Create an 'R Journal' 'Rmarkdown' template article, that will
  generate html and pdf versions of your paper. Check that the paper folder 
  has all the required components needed for submission. 
  Examples of 'R Journal' publications can be found at 
  <https://journal.r-project.org>.
'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>) is the seasonal adjustment software officially recommended
  to the members of the European Statistical System and the European System of Central Banks. Seasonal adjustment models performed
  with 'JDemetra+' can be stored into workspaces. 'JWSACruncher' (<https://github.com/jdemetra/jwsacruncher/releases>) is a console tool that 
  re-estimates all the multi-processing defined in a workspace and to export the result. 'rjwsacruncher' allows to launch easily the 'JWSACruncher'.
The 'rkafkajars' package collects all the external jars required for the 'rkafka' package.
An R interface to KEA (Version 5.0).
  KEA (for Keyphrase Extraction Algorithm) allows for extracting
  keyphrases from text documents. It can be either used for free
  indexing or for indexing with a controlled vocabulary. For more
  information see <http://www.nzdl.org/Kea/>.
External jars required for package RKEA.
'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks.
    This package takes the advantages of 'KEEL' and R, allowing to use 'KEEL' algorithms in simple R code.
    The implemented R code layer between R and 'KEEL' makes easy both using 'KEEL' algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way.
    It includes more than 100 algorithms for classification, regression, preprocess, association rules and imbalance learning, which allows a more complete experimentation process.
    For more information about KEEL, see <http://www.keel.es/>.
'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and 'KEEL', for using 'KEEL' in R code. This package includes the datasets from 'KEEL' in .dat format for its use in 'RKEEL' package. For more information about 'KEEL', see <http://www.keel.es/>.
'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and 'KEEL', for using 'KEEL' in R code. This package downloads and install the .jar files necessary for 'RKEEL' algorithms execution. For more information about 'KEEL', see <http://www.keel.es/>.
Calculates the Kelly criterion (Kelly, J.L. (1956) <doi:10.1002/j.1538-7305.1956.tb03809.x>) for bets given quoted prices, model predictions and commissions.
    Additionally it contains helper functions to calculate the probabilities for wins and draws in multi-leg games.
Exchange rate for Kenya Shilling against other currencies, US DOLLAR, EURO, STERLING POUND, Tanzania Shilling, Uganda Shilling.
The 'KeOps' library lets you compute generic reductions of very 
    large arrays whose entries are given by a mathematical formula with CPU and 
    GPU computing support. It combines a tiled reduction scheme with an 
    automatic differentiation engine. It is perfectly suited to the efficient 
    computation of Kernel dot products and the associated gradients, even when 
    the full kernel matrix does not fit into the GPU memory.
Estimates the Hoeffding decomposition of an unknown function by solving ridge group sparse optimization problem based on reproducing kernel Hilbert spaces, and approximates its sensitivity indices (see Kamari, H., Huet, S. and Taupin, M.-L. (2019) <arXiv:1905.13695>).
Applies methods used to estimate animal homerange, but
    instead of geospatial coordinates, we use isotopic coordinates. The estimation
    methods include: 1) 2-dimensional bivariate normal kernel utilization density
    estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon
    estimator, all applied to stable isotope data. Additionally, functions to
    determine niche area, polygon overlap between groups and levels (confidence
    contours) and plotting capabilities.
Hybrid Mortality Modelling (HMM) provides a framework in which mortality around "the accident hump" and at very old ages can be modelled under a single model. The graphics' codes necessary for visualization of the models' output are included here. Specifically, the graphics are based on the assumption that, the mortality rates can be expressed as a function of the area under the curve between the crude mortality rates plots and the tangential transform of the force of mortality.
This is a analysis toolkit to streamline the analyses of minicircle sequence diversity in population-scale genome projects. rKOMICS is a user-friendly R package that has simple installation requirements and that is applicable to all 27 trypanosomatid genera. Once minicircle sequence alignments are generated, rKOMICS allows to examine, summarize and visualize minicircle sequence diversity within and between samples through the analyses of minicircle sequence clusters. We showcase the functionalities of the (r)KOMICS tool suite using a whole-genome sequencing dataset from a recently published study on the history of diversification of the Leishmania braziliensis species complex in Peru. Analyses of population diversity and structure highlighted differences in minicircle sequence richness and composition between Leishmania subspecies, and between subpopulations within subspecies. The rKOMICS package establishes a critical framework to manipulate, explore and extract biologically relevant information from mitochondrial minicircle assemblies in tens to hundreds of samples simultaneously and efficiently. This should facilitate research that aims to develop new molecular markers for identifying species-specific minicircles, or to study the ancestry of parasites for complementary insights into their evolutionary history. ***** !! WARNING: this package relies on dependencies from Bioconductor. For Mac users, this can generate errors when installing rKOMICS. Install Bioconductor and ComplexHeatmap at advance: install.packages("BiocManager"); BiocManager::install("ComplexHeatmap") *****.
A client package that makes the 'KorAP' web service API accessible from R.
  The corpus analysis platform 'KorAP' has been developed as a scientific tool to make
  potentially large, stratified and multiply annotated corpora, such as the 'German Reference Corpus DeReKo'
  or the 'Corpus of the Contemporary Romanian Language CoRoLa', accessible for linguists to let them verify
  hypotheses and to find interesting patterns in real language use.
  The 'RKorAPClient' package provides access to 'KorAP' and the corpora behind it for user-created R code,
  as a programmatic alternative to the 'KorAP' web user-interface.
  You can learn more about 'KorAP' and use it directly on 'DeReKo' at <https://korap.ids-mannheim.de/>.
Contains function rkt which computes the Mann-Kendall test (MK) and the Seasonal and the Regional Kendall Tests for trend (SKT and RKT) and  Theil-Sen's slope estimator.
Robust  kernel center matrix, robust  kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, 
 influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A,  Fukumizu, K., Wang  Y.-P. (2018) <doi:10.1016/j.neucom.2018.04.008>.
   Alam, M. A,  Calhoun, C. D.,  Wang  Y.-P. (2018) <doi:10.1016/j.csda.2018.03.013>.
This package provides functionality to read files
    containing observations which consist of arbitrary key/value
    pairs.
Provides functions and datasets required for the ST 370 course at North Carolina State University.
The 'LabKey' client library for R makes it easy for R users to
    load live data from a 'LabKey' Server, <https://www.labkey.com/>,
    into the R environment for analysis, provided users have permissions
    to read the data. It also enables R users to insert, update, and
    delete records stored on a 'LabKey' Server, provided they have appropriate
    permissions to do so.
Standardized methods for calculating common important derived
    physical features of lakes including water density based based on
    temperature, thermal layers, thermocline depth, lake number, Wedderburn
    number, Schmidt stability and others.
A toolbox for working with base types, core R features
  like the condition system, and core 'Tidyverse' features like tidy
  evaluation.
Read and write 'las' and 'laz' binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing <https://www.asprs.org/divisions-committees/lidar-division/laser-las-file-format-exchange-activities>. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.4 <https://laszip.org/>.
An easy-to-use tool to employ interactivity in every-day exploratory analysis. It contains 
	a collection of most commonly used types of charts (such as scatter plots, line plots, heatmaps, bar charts),
	which can be linked to each other or to other interactive elements with just few lines of code.
Robust likelihood cross validation bandwidth for uni- and multi-variate kernel densities. It is robust against fat-tailed distributions and/or outliers. Based on "Robust Likelihood Cross-Validation for Kernel Density Estimation," Wu (2019) <doi:10.1080/07350015.2018.1424633>.
Linguistic Descriptions of Complex Phenomena (LDCP) is an architecture and methodology that allows us to model complex phenomena, interpreting input data, and generating automatic text reports customized to the user needs (see <doi:10.1016/j.ins.2016.11.002> and <doi:10.1007/s00500-016-2430-5>). The proposed package contains a set of methods that facilitates the development of LDCP systems. It main goal is increasing the visibility and practical use of this research line.
Common 'base' and 'stats' methods for 'rle' objects, aiming to make it possible to treat them transparently as vectors.
Leaf angle distribution is described by a number of functions
    (e.g. ellipsoidal, Beta and rotated ellipsoidal). The parameters of leaf angle
    distributions functions are estimated through different empirical relationship.
    This package includes estimations of parameters of different leaf angle
    distribution function, plots and evaluates leaf angle distribution functions,
    calculates extinction coefficients given leaf angle distribution.
    Reference: Wang(2007)<doi:10.1016/j.agrformet.2006.12.003>.
Display spatial data with interactive maps powered by the 
  open-source JavaScript library 'Leaflet' (see <http://leafletjs.com/>). Maps can be rendered in a web browser or
  displayed in the HTML viewer pane of 'RStudio'. This package is designed to be easy to use and
  can create complex maps with vector and raster data, web served map tiles and interface elements.
Provides an interface to the C implementation of the
        random number generator with multiple independent streams
        developed by L'Ecuyer et al (2002). The main purpose of this
        package is to enable the use of this random number generator in
        parallel R applications.
Allows easy access to the LEMON Graph Library set of algorithms, written in C++.
             See the LEMON project page at <https://lemon.cs.elte.hu/trac/lemon>.
             Current LEMON version is 1.3.1.
Converts LESS to CSS. 
    It uses V8 engine, where LESS parser is run. Functions for
    LESS text, file or folder conversion are provided.
    This work was supported by a junior grant research project by 
    Czech Science Foundation 'GACR' no. 'GJ18-04150Y'.
Contains functions for simulating the linear fractional stable motion according to the algorithm developed by Mazur and Otryakhin <doi:10.32614/RJ-2020-008> based on the method from Stoev and Taqqu (2004) <doi:10.1142/S0218348X04002379>, as well as functions for estimation of parameters of these processes introduced by Mazur, Otryakhin and Podolskij (2018) <arXiv:1802.06373>, and also different related quantities.
Assists in the manipulation and processing of linear features with the help of the 'sf' package.
  Makes use of linear referencing to extract data from most shape files.
  Reference for this packages methods: Albeke, S.E. et al. (2010) <doi:10.1007/s10980-010-9528-4>.
An implementation of a number of Global Trend models for time series forecasting 
    that are Bayesian generalizations and extensions of some Exponential Smoothing models. 
    The main differences/additions include 1) nonlinear global trend, 2) Student-t error 
    distribution, and 3) a function for the error size, so heteroscedasticity. The methods 
    are particularly useful for short time series. When tested on the well-known M3 dataset,
    they are able to outperform all classical time series algorithms. The models are fitted 
    with MCMC using the 'rstan' package.
An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, 
    a C library of highly efficient parallelizable functions  for performing the ensemble empirical mode decomposition (EEMD), 
    its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). 
    Due to the possible portability issues CRAN version no longer supports OpenMP, you can install OpenMP-supported version 
    from GitHub: <https://github.com/helske/Rlibeemd/>.
Interface to 'libKriging' 'C++' library <https://github.com/libKriging> that should
    provide most standard Kriging / Gaussian process regression features
    (like in 'DiceKriging', 'kergp' or 'RobustGaSP' packages).
    'libKriging' relies on Armadillo linear algebra library (Apache 2 license) by Conrad Sanderson, 
    and 'lbfgsb_cpp' is a 'C++' wrapper by Colin Fang around 'lbfgsb' library (BSD-3 license) by 
    Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales used for hyperparameters optimization.
Set of tools for reading, processing and visualizing small set of
    LiDAR (Light Detection and Ranging) data for forest inventory applications.
    More details were published in Silva et al. (2016) <doi:10.1080/07038992.2016.1196582>.
Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.
A series of functions that allow users
    to access the 'LinkedIn' API to get information about connections,
    search for people and jobs, share updates with their network,
    and create group discussions.  For more information about using
    the API please visit <https://developer.linkedin.com/>.
Solving a system of linear equations is one of the most fundamental
    computational problems for many fields of mathematical studies, such as
    regression problems from statistics or numerical partial differential equations.
    We provide basic stationary iterative solvers such as Jacobi, Gauss-Seidel,
    Successive Over-Relaxation and SSOR methods. Nonstationary, also known as
	Krylov subspace methods are also provided. Sparse matrix computation is also supported
    in that solving large and sparse linear systems can be manageable using 'Matrix' package
    along with 'RcppArmadillo'. For a more detailed description, see a book by Saad (2003)
    <doi:10.1137/1.9780898718003>.
Provides a set of functions for data manipulation with
    list objects, including mapping, filtering, grouping, sorting,
    updating, searching, and other useful functions. Most functions
    are designed to be pipeline friendly so that data processing with
    lists can be chained.
Listings are often part of the submission of clinical trial
    data in regulatory settings.  We provide a framework for the specific
    formatting features often used when displaying large datasets in that
    context.
Data driven approach for robust regression estimation in homoscedastic and heteroscedastic context. See Wang et al. (2007), <doi:10.1198/106186007X180156> regarding homoscedastic framework.
Estimates robust rank-based fixed effects and predicts robust
    random effects in two- and three- level random effects nested models.
    The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.
R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.
A very lightweight package that writes out log messages in an opinionated way.
  Simpler and lighter than other logging packages, 'rlog' provides a compact feature set that
  focuses on getting the job done in a Unix-like way.
Processing logical operations such as AND/OR/NOT operations
    dynamically. It also handles nesting in the operations.
Rapid, simulation-based exact (restricted) likelihood ratio tests
    for testing the presence of variance components/nonparametric terms for
    models fit with nlme::lme(),lme4::lmer(), lmeTest::lmer(), gamm4::gamm4(),
    mgcv::gamm() and SemiPar::spm().
Random forest with a variety of additional features for regression, classification and survival analysis. 
             The features include: parallel computing with OpenMP, embedded model for selecting the splitting variable,
             based on Zhu, Zeng & Kosorok (2015) <doi:10.1080/01621459.2015.1036994>, subject weight, variable weight, 
             tracking subjects used in each tree, etc.
R interface to the 'LTP'-Cloud service for Natural Language Processing
    in Chinese (http://www.ltp-cloud.com/).
A collection of functions to simulate luminescence production in 
    dosimetric materials using Monte Carlo methods. Implemented are models for 
    delocalised transitions (e.g., Chen and McKeever (1997) <doi:10.1142/2781>), 
    localised transitions (e.g., Pagonis et al. (2019) <doi:10.1016/j.jlumin.2018.11.024>) 
    and tunnelling transitions (Jain et al. (2012) <doi:10.1088/0953-8984/24/38/385402> 
    and Pagonis et al. (2019) <doi:10.1016/j.jlumin.2018.11.024>). 
    Supported stimulation methods are thermal luminescence (TL), 
    continuous-wave optically stimulated luminescence (CW-OSL), 
    linearly-modulated optically stimulated luminescence (LM-OSL), 
    linearly-modulated infrared stimulated luminescence (LM-IRSL),
    and isothermal luminescence (ITL or ISO-TL).
A collection of functions to simulate luminescence signals in quartz and Al2O3 based on published models.
A collection of 'shiny' applications for the R package
    'Luminescence'. These mainly, but not exclusively, include applications for
    plotting chronometric data from e.g. luminescence or radiocarbon dating. It
    further provides access to bootstraps tooltip and popover functionality and
    contains the 'jscolor.js' library with a custom 'shiny' output binding.
R implementation of the common parsing tools 'lex' and 'yacc'.
Rasch model and extensions for survey data, using Conditional Maximum likelihood (CML). Carlo Cafiero, Sara Viviani, Mark Nord (2018) <doi:10.1016/j.measurement.2017.10.065>.
Estimation of the conditional covariance matrix using the RiskMetrics 2006 methodology of Zumbach (2007) <doi:10.2139/ssrn.1420185>.
Compute an exact CI for the population mean under a random effects model. The routines implement the algorithm described in Michael, Thronton, Xie, and Tian (2017) <https://haben-michael.github.io/research/Exact_Inference_Meta.pdf>.
Allows for production of Czekanowski's Diagrams. See K. Bartoszek, A. Vasterlund (2020) <doi:10.2478/bile-2020-0008>.
Creates and maintains a build process for complex analytic tasks in R.
  Package allows to easily generate Makefile for the (GNU) 'make' tool, which drives the build process
  by (in parallel) executing build commands in order to update results accordingly to given dependencies
  on changed data or updated source files.
An EM algorithm to fit Mallows' Models to full or partial rankings, with or without ties.
        Based on Adkins and Flinger (1998) <doi:10.1080/03610929808832223>.
An implementation of an algorithm family for continuous
    optimization called memetic algorithms with local search chains
    (MA-LS-Chains), as proposed in Molina et al. (2010) <doi:10.1162/evco.2010.18.1.18102> and Molina et al. (2011) <doi:10.1007/s00500-010-0647-2>. Rmalschains is further discussed in Bergmeir et al. (2016) <doi:10.18637/jss.v075.i04>. Memetic algorithms are hybridizations of genetic
    algorithms with local search methods. They are especially suited
    for continuous optimization.
An interface to the 'Mangal' database - a collection of ecological networks. This package includes functions to work with the 'Mangal RESTful API' methods (<https://mangal-interactions.github.io/mangal-api/>).
Edit and simplify 'geojson', 'Spatial', and 'sf'
    objects.  This is wrapper around the 'mapshaper' 'JavaScript' library
    by Matthew Bloch <https://github.com/mbloch/mapshaper/> to perform
    topologically-aware polygon simplification, as well as other
    operations such as clipping, erasing, dissolving, and converting
    'multi-part' to 'single-part' geometries.
Provides an interface to 'Mapzen'-based APIs (including 
    geocode.earth, Nextzen, and NYC GeoSearch) for geographic search 
    and geocoding, isochrone calculation, and vector data to draw map tiles. 
    See <https://www.mapzen.com/documentation/> for more information. The original 
    Mapzen has gone out of business, but 'rmapzen' can be set up to work with 
    any provider who implements the Mapzen API.
A port of the C++ routine for applying the marching cubes algorithm written by 
    Thomas Lewiner et al. (2012) <doi:10.1080/10867651.2003.10487582> into an R package. 
    The package supplies the contour3d() function, which takes a 3-dimensional array of voxel 
    data and calculates the vertices, vertex normals, and faces for a 3d mesh representing 
    the contour(s) at a given level.
Three robust marginal integration procedures for additive models based on local 
             polynomial kernel smoothers. As a preliminary estimator of the multivariate 
             function for the marginal integration procedure, a first approach uses local 
             constant M-estimators, a second one uses local polynomials of order 1 over all the
             components of covariates, and the third one uses M-estimators based on local 
             polynomials but only in the direction of interest. For this last approach, 
             estimators of the derivatives of the additive functions can be obtained. All three
             procedures can compute predictions for points outside the training set if desired. 
             See Boente and Martinez (2017) <doi:10.1007/s11749-016-0508-0> for details.
Implements a DBI-compliant interface to MariaDB
    (<https://mariadb.org/>) and MySQL (<https://www.mysql.com/>)
    databases.
Given a time series of daily temperatures, the package provides tools
    to detect extreme thermal events, including marine heat waves, and to
    calculate the exceedances above or below specified threshold values.
    It outputs the properties of all detected events and exceedances.
An interface to the software package MARK that constructs input
    files for MARK and extracts the output. MARK was developed by Gary White
    and is freely available at <http://www.phidot.org/software/mark/downloads/>
    but is not open source.
Convert R Markdown documents into a variety of formats.
Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions.
Read and write 'Matlab' MAT files from R. The 'rmatio'
    package supports reading MAT version 4, MAT version 5 and MAT
    compressed version 5. The 'rmatio' package can write version 5 MAT
    files and version 5 files with variable compression.
S3 and S4 functions are implemented for spatial multi-site
    stochastic generation of daily time series of temperature and
    precipitation. These tools make use of Vector AutoRegressive models (VARs).
    The weather generator model is then saved as an object and is calibrated by
    daily instrumental "Gaussianized" time series through the 'vars' package
    tools. Once obtained this model, it can it can be used for weather
    generations and be adapted to work with several climatic monthly time
    series.
A Bayesian credible interval is interpreted with respect to posterior probability, 
    and this interpretation is far more intuitive than that of a frequentist confidence interval. 
    However, standard highest-density intervals can be wide due to between-subjects variability and tends 
    to hide within-subjects effects, rendering its relationship with the Bayes factor less clear 
    in within-subjects (repeated-measures) designs. 
    This urgent issue can be addressed by using within-subjects intervals in within-subjects designs, 
    which integrate four methods including the Loftus-Masson (1994) <doi:10.3758/BF03210951>, 
    the Rouder-Morey-Speckman-Province (2012) <doi:10.1016/j.jmp.2012.08.001>,
    the Nathoo-Kilshaw-Masson (2018) <doi:10.1016/j.jmp.2018.07.005>, 
    and the Heck (2019) <doi:10.31234/osf.io/whp8t> interval estimates.
A robust clustering algorithm (Model-Based) similar to Expectation
    Maximization for finite mixture normal distributions is implemented, 
    its main advantage is that the estimator is resistant to outliers, 
    that means that results of parameter estimation are still correct when there
    are atypical values in the sample 
    (see Gonzalez, Maronna, Yohai and Zamar (2021) 
    <arxiv:2102.06851>).
Multiscale Curvature Classification of ground returns in 3-D LiDAR 
    point clouds, designed for forested environments. 'RMCC' is a porting to R of the 
    'MCC-lidar' method by Evans and Hudak (2007) <doi:10.1109/TGRS.2006.890412>.
MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.
Compute the repeated measures correlation, a statistical technique
    for determining the overall within-individual relationship among paired measures
    assessed on two or more occasions, first introduced by Bland and Altman (1995).
    Includes functions for diagnostics, p-value, effect size with confidence
    interval including optional bootstrapping, as well as graphing. Also includes
    several example datasets. For more details, see the web documentation 
    <https://lmarusich.github.io/rmcorr/index.html> and the 
    original paper: Bakdash and Marusich (2017) <doi:10.3389/fpsyg.2017.00456>.
Provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention).  Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making.   Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as 'high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be 'high risk' by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies.  Curves can be estimated using data from a prospective cohort.  In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available.  Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used to recommend that low-risk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided.
Estimates and simulates Kuhn-Tucker demand models with individual heterogeneity. The package implements the multiple-discrete continuous extreme value (MDCEV) model and the Kuhn-Tucker specification common in the environmental economics literature on recreation demand. Latent class and random parameters specifications can be implemented and the models are fit using maximum likelihood estimation or Bayesian estimation. All models are implemented in Stan, which is a C++ package for performing full Bayesian inference (see Stan Development Team, 2019) <https://mc-stan.org/>. The package also implements demand forecasting (Pinjari and Bhat (2011) <https://repositories.lib.utexas.edu/handle/2152/23880>) and welfare calculation (Lloyd-Smith (2018) <doi:10.1016/j.jocm.2017.12.002>) for policy simulation.
Supports concordances in 'R Markdown' 
  documents.  This currently allows the original source
  location in the '.Rmd' file of errors detected by 'HTML tidy'
  to be found more easily, and potentially allows forward
  and reverse search in 'HTML' and 'LaTeX' documents
  produced from 'R Markdown'.  The 'LaTeX' support
  has been included in the most recent development 
  version of the 'patchDVI' package.
A collection of 'Lua' filters that extend the functionality
  of R Markdown templates (e.g., count words or post-process citations).
HTML formats and templates for 'rmarkdown' documents, with some extra
    features such as automatic table of contents, lightboxed figures, dynamic
    crosstab helper.
A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.
Use 'rmarkdown' partials, also know as child documents in
		'knitr', so you can make components for HTML, PDF, and Word documents. 
		The package provides various helper functions to make certain functions easier. 
		You may want to use this package, if you want to flexibly summarise objects 
		using a combination of figures, tables, text, and HTML widgets. 
		Unlike HTML widgets, the output is Markdown and can hence be turn into other
		output formats than HTML.
Formats for R Markdown that undo modifications by
    'pandoc' and 'rmarkdown' to original 'latex' templates, such as
    smaller margins, paragraph spacing, and compact titles. In addition, 
    enhancements such as author blocks with affiliations and
    headers and footers are introduced. All of this functionality is built 
    around plugins that modify the default 'pandoc' template without relying on 
    custom templates.
'R' 'Markdown' format for 'shower' presentations, see
   <https://github.com/shower/shower>.
If you are using R Markdown documents then you have sometimes restrictions about the size of the documents, e.g. number of words, number of characters or non-whitespace characters. rmdcount() computes these counts with and without code chunks and returns the result as data frame.
A suite of tools useful to read, visualize and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. Surrogate data generation allows an estimation of pseudosynchrony that helps to estimate the effect size of the observed synchronization. Kleinbub, J. R., & Ramseyer, F. T. (2020). rMEA: An R package to assess nonverbal synchronization in motion energy analysis time-series. Psychotherapy research, 1-14. <doi:10.1080/10503307.2020.1844334>.
An 'Rcpp' interface for Eunjeon project <http://eunjeon.blogspot.com/>.
    The 'mecab-ko' and 'mecab-ko-dic' is based on a C++ library,
    and part-of-speech tagging with them is useful when the spacing of source Korean text is not correct.
    This package provides part-of-speech tagging and tokenization function for Korean text.
We provide functions to compute confidence intervals for a well-defined nonlinear function of the model parameters (e.g., product of k coefficients) in single--level and
    multilevel structural equation models. It also computes a chi-square test statistic for a function of indirect effects. 
    'Tofighi', D. and 'MacKinnon', D. P. (2011). 'RMediation' An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692--700. <doi:10.3758/s13428-011-0076-x>.
    'Tofighi', D. (2020). Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects. Frontiers in Psychology, 10, 2989. <doi:10.3389/fpsyg.2019.02989>.
Functions for simple fixed and random effects
        meta-analysis for two-sample comparisons and cumulative
        meta-analyses. Draws standard summary plots, funnel plots, and
        computes summaries and tests for association and heterogeneity.
Implementation of the metalog distribution in R.
    The metalog distribution is a modern, highly flexible, data-driven distribution. 
    Metalogs are developed by Keelin (2016) <doi:10.1287/deca.2016.0338>.
    This package provides functions to build these distributions from raw data. 
    Resulting metalog objects are then useful for exploratory and probabilistic analysis.
The provided package implements the statistical tests for the functional repeated measures analysis problem (Kurylo and Smaga, 2023, <arXiv:2306.03883>). These procedures enable us to verify the overall hypothesis regarding equality, as well as hypotheses for pairwise comparisons (i.e., post hoc analysis) of mean functions corresponding to repeated experiments.
Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.
Provides mutual information estimators based on k-nearest neighbor estimators by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138>, S. Gao, et al. (2015) <http://proceedings.mlr.press/v38/gao15.pdf> and local density estimators by W. Gao, et al. (2017) <doi:10.1109/ISIT.2017.8006749>.
A tool for multiply imputing missing data using 'MIDAS', a deep learning method based on denoising autoencoder neural networks. This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with 'Python' to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets.
Facilitates the use of data mining algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version.
Constraint optimization, or constraint programming, is the name given to identifying
  feasible solutions out of a very large set of candidates, where the problem can be modeled in terms 
  of arbitrary constraints. 'MiniZinc' is a free and open-source constraint modeling language. 
  Constraint satisfaction and discrete optimization problems can be formulated in a high-level 
  modeling language. Models are compiled into an intermediate representation that is understood by a
  wide range of solvers. 'MiniZinc' itself provides several solvers, for instance 'GeCode'. R users 
  can use the package to solve constraint programming problems without using 'MiniZinc' directly, 
  modify existing 'MiniZinc' models and also create their own models.
Perform derivative-free optimization algorithms in R using C++.
    A wrapper interface is provided to call C function of the 'bobyqa' implementation
    (See <https://github.com/emmt/Algorithms/tree/master/bobyqa>).
Provides header files of 'mio', a cross-platform C++11 header-only 
    library for memory mapped file IO <https://github.com/mandreyel/mio>.
It was developed especially for gene expression and metabolomics data analysis when the datasets are corrupted by outliers and missing values. The beta-divergence method was used to impute the missing values and modify the outliers.
Contains many functions useful for data analysis
    and utility operations.
Interface of 'MIXMOD' software for supervised, unsupervised and
    semi-supervised classification with mixture modelling <doi: 10.18637/jss.v067.i06>.
Provides an interface to many endpoints of Mixpanel's Data Export, Engage and JQL API. The R functions allow for event and profile data export as well as for segmentation, retention, funnel and addiction analysis. Results are always parsed into convenient R objects. Furthermore it is possible to load and update profiles.
Mixture Composer (Biernacki (2015) <https://inria.hal.science/hal-01253393v1>) is a project to perform clustering using mixture models with
    heterogeneous data and partially missing data. Mixture models are fitted using a SEM algorithm.
    It includes 8 models for real, categorical, counting, functional and ranking data.
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management.
    It includes models for real, categorical, counting, functional and ranking data.
    This package contains the minimal R interface of the C++ 'MixtComp' library.
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management. This package contains graphical, getter and some utility
    functions to facilitate the analysis of 'MixtComp' output.
Sundry discrete probability distributions and helper functions.
R implementation of Maximum Likelihood Principal Component Analysis
    The main idea of this package is to have an alternative way of PCA for 
    subspace modeling that considers measurement errors.
    More details can be found in Peter D. Wentzell (2009) 
    <doi:10.1016/B978-0-444-64165-6.03029-9>.
The RMM  fits Revenue Management Models using the RDE(Robust Demand Estimation) method introduced in the paper by <doi:10.2139/ssrn.3598259>, one of the customer choice-based Revenue Management Model. Furthermore, it is possible to select a multinomial model as well as a conditional logit model as a model of RDE.
Connect R with MOA (Massive Online Analysis -
    <https://moa.cms.waikato.ac.nz/>) to build classification models and
    regression models on streaming data or out-of-RAM data.
    Also streaming recommendation models are made available.
External jars required for package RMOA. RMOA is a framework to
    build data stream models on top of MOA (Massive Online Analysis -
    <https://moa.cms.waikato.ac.nz/>). The jar files are put in this R package, the modelling logic can be found in the RMOA package.
Graphical visualization of the birds' molt to facilitate the creation of molting graph for passerines having 9 (Rmolt(data,9)) or 10 primaries (Rmolt(data,10)), and also only for the 10 first primaries (Rmolt(data,"10_0")).
A monadic solution to pipeline analysis. All operations -- and the errors,
    warnings and messages they emit -- are merged into a directed graph. Infix
    binary operators mediate when values are stored, how exceptions are
    handled, and where pipelines branch and merge. The resulting structure may
    be queried for debugging or report generation. 'rmonad' complements, rather
    than competes with, non-monadic pipeline packages like 'magrittr' or
    'pipeR'. This work is funded by the NSF (award number 1546858).
The 'rmoo' package is a framework for multi- and many-objective
        optimization, which allows researchers and users versatility
        in parameter configuration, as well as tools for analysis, replication
        and visualization of results. The 'rmoo' package was built as a fork of
        the 'GA' package by Luca Scrucca(2017) <DOI:10.32614/RJ-2017-008> and
        implementing the Non-Dominated Sorting Genetic Algorithms proposed
        by K. Deb's.
Provides functions for risk management and portfolio investment of securities with practical tools for data processing and plotting. Moreover, it contains functions which perform the COS Method, an option pricing method based on the Fourier-cosine series (Fang, F. (2008) <doi:10.1137/080718061>).
This is a meta-package designed to support the
        installation of Rmosek (>= 6.0) and bring the optimization
        facilities of MOSEK (>= 6.0) to the R-language. The interface
        supports large-scale optimization of many kinds: Mixed-integer
        and continuous linear, second-order cone, exponential cone and
        power cone optimization, as well as continuous semidefinite
        optimization. Rmosek and the R-language are open-source
        projects. MOSEK is a proprietary product, but unrestricted
        trial and academic licenses are available.
Estimates Pareto-optimal solution for personnel selection
    with 3 objectives using Normal Boundary Intersection (NBI) algorithm
    introduced by Das and Dennis (1998) <doi:10.1137/S1052623496307510>.
    Takes predictor intercorrelations and predictor-objective relations as
    input and generates a series of solutions containing predictor weights
    as output. Accepts between 3 and 10 selection predictors. Maximum 2
    objectives could be adverse impact objectives. Partially modeled after
    De Corte (2006) TROFSS Fortran program
    <https://users.ugent.be/~wdecorte/trofss.pdf> and updated from
    'ParetoR' package described in Song et al. (2017)
    <doi:10.1037/apl0000240>. For details, see Study 3 of Zhang et al. (in press).
Arithmetic (via S4 classes and methods) for
  arbitrary precision floating point numbers, including transcendental
  ("special") functions.  To this end, the package interfaces to
  the 'LGPL' licensed 'MPFR' (Multiple Precision Floating-Point Reliable) Library
  which itself is based on the 'GMP' (GNU Multiple Precision) Library.
An interface (wrapper) to MPI. It also 
	     provides interactive R manager and worker environment.
Optimization of any Black-Box/Non-Convex Function on Hyper-Rectangular Parameter Space. It uses a Variation of Pattern Search Technique. Described in the paper : Das (2016) <arXiv:1604.08616> .
We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998617749561>.
Analysis of oxygen consumption data generated by Loligo (R) Systems respirometry equipment. The package includes a function for loading data output by Loligo's 'AutoResp' software (get.witrox.data()), functions for calculating metabolic rates over user-specified time intervals, extracting critical points from data using broken stick regressions based on Yeager and Ultsch (<DOI:10.1086/physzool.62.4.30157935>), and easy functions for converting between different units of barometric pressure.
Regression modeling, testing, estimation, validation,
	graphics, prediction, and typesetting by storing enhanced model design
	attributes in the fit.  'rms' is a collection of functions that
	assist with and streamline modeling.  It also contains functions for
	binary and ordinal logistic regression models, ordinal models for
  continuous Y with a variety of distribution families, and the Buckley-James
	multiple regression model for right-censored responses, and implements
	penalized maximum likelihood estimation for logistic and ordinary
	linear models.  'rms' works with almost any regression model, but it
	was especially written to work with binary or ordinal regression
	models, Cox regression, accelerated failure time models,
	ordinary linear models,	the Buckley-James model, generalized least
	squares for serially or spatially correlated observations, generalized
	linear models, and quantile regression.
A Bayesian companion to the 'rms' package, 'rmsb' provides Bayesian model fitting, post-fit estimation, and graphics.  It implements Bayesian regression models whose fit objects can be processed by 'rms' functions such as 'contrast()', 'summary()', 'Predict()', 'nomogram()', and 'latex()'.  The fitting function currently implemented in the package is 'blrm()' for Bayesian logistic binary and ordinal regression with optional clustering, censoring, and departures from the proportional odds assumption using the partial proportional odds model of Peterson and Harrell (1990) <https://www.jstor.org/stable/2347760>.
Display a randomly selected quote about Richard M. Stallman
 based on the collection in the 'GNU Octave' function 'fact()' which was
 aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site
 stallmanfacts.com (which is accessible only via <http://archive.org>).
Contains several useful navigation helper functions, including easily building
    folder paths, quick viewing dataframes in 'Excel', creating date vectors and changing the
    console prompt to reflect time.
'Rcpp' bindings to the native C++ implementation of MS Numpress, that provides two compression schemes for numeric data from mass spectrometers. The library provides implementations of 3 different algorithms, 1 designed to compress first order smooth data like retention time or M/Z arrays, and 2 for compressing non smooth data with lower requirements on precision like ion count arrays. Refer to the publication (Teleman et al., (2014) <doi:10.1074/mcp.O114.037879>) for more details.
Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust 
             multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of 
             the least trimmed square loss of the models in the ensembles under constraints  for the size of 
             the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and
             diversity of the robust ensemble are selected by cross-validation.
Assemble the panels of computerized adaptive multistage testing by the 
    bottom-up and the top-down approach, and simulate the administration of the assembled 
    panels. The full documentation and tutorials are at <https://github.com/xluo11/Rmst>.
    Reference: Luo and Kim (2018) <doi:10.1111/jedm.12174>.
Performs two-sample comparisons using the restricted mean survival time (RMST) when survival curves end at different time points between groups. This package implements a sensitivity approach that allows the threshold  timepoint tau to be specified after the longest survival time in the shorter survival group. Two kinds of between-group contrast estimators (the difference in RMST and the ratio of RMST) are computed: Uno et al(2014)<doi:10.1200/JCO.2014.55.2208>, Uno et al(2022)<https://CRAN.R-project.org/package=survRM2>, Ueno and Morita(2023)<doi:10.1007/s43441-022-00484-z>.
Contains inferential and graphical routines for comparing two treatment arms in terms of the restricted mean time in favor of treatment.
We generate random variables following general Marchenko-Pastur distribution and Tracy-Widom distribution. We compute limits and distributions of eigenvalues and generalized components of spiked covariance matrices. We give estimation of all population eigenvalues of spiked covariance matrix model. We give tests of population covariance matrix. We also perform matrix denoising for signal-plus-noise model.
An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.
Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.
Functions for working with the Tracy-Widom laws and other distributions 
    related to the eigenvalues of large Wishart matrices.
    The tables for computing the Tracy-Widom densities and distribution
    functions were computed by functions were computed by Momar Dieng's MATLAB package "RMLab". This package is part of a collaboration between Iain Johnstone, Zongming Ma, Patrick Perry, and Morteza Shahram.
Some basic features of 'MUMPS' (Multifrontal Massively Parallel
         sparse direct Solver) are wrapped in a class whose methods can be used
         for sequentially solving a sparse linear system (symmetric or not)
         with one or many right hand sides (dense or sparse).
         There is a possibility to do separately symbolic analysis,
         LU (or LDL^t) factorization and system solving.
         Third part ordering libraries are included and can be used: 'PORD', 'METIS', 'SCOTCH'.
         'MUMPS' method was first described in Amestoy et al. (2001) <doi:10.1137/S0895479899358194>
         and Amestoy et al. (2006) <doi:10.1016/j.parco.2005.07.004>.
A toolkit of functions for nonlinear regression and repeated
    measurements not to be used by itself but called by other Lindsey packages such
    as 'gnlm', 'stable', 'growth', 'repeated', and 'event' 
    (available at <https://www.commanster.eu/rcode.html>).
Mappable vector library provides convenient way to access large datasets. Use all of your data at once, with few limits. Memory mapped data can be shared between multiple R processes. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface or a fast network file system. The data is memory mapped into R and then accessed using usual R list and array subscription operators. Convenience functions are provided for merging, grouping and indexing large vectors and data.frames. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as a standalone C library. libMVL has simple C API making it easy to interchange datasets with outside programs.
A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can
    (1) effectively process large data, 
    (2) rapidly evaluate population structure, 
    (3) efficiently estimate variance components several algorithms, 
    (4) implement parallel-accelerated association tests of markers three methods, 
    (5) globally efficient design on GWAS process computing, 
    (6) enhance visualization of related information. 
    'rMVP' contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>) 
    and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX 
    (Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>, 
    R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and 
    'SUPER': Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression 
    (Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).
An integrated set of tools to allow data users to conduct 
    meteorological normalisation on air quality data. This meteorological 
    normalisation technique uses predictive random forest models to remove 
    variation of pollutant concentrations so trends and interventions can be 
    explored in a robust way. For examples, see Grange et al. (2018) 
    <doi:10.5194/acp-18-6223-2018> and Grange and Carslaw (2019) 
    <doi:10.1016/j.scitotenv.2018.10.344>.
Legacy 'DBI' interface to 'MySQL' / 'MariaDB' based on old code
    ported from S-PLUS. A modern 'MySQL' client based on 'Rcpp' is available 
    from the 'RMariaDB' package.
Allows work with 'MyTarget Statistics API v2' 
	<https://target.my.com/adv/api-marketing/doc/stat-v2> and 
	'MyTarget Statistics API v3' <https://target.my.com/adv/api-marketing/doc/stat-v2#statisticsv3>
	load data by ads, campaigns, agency clients and statistic from
	your ads account.
Reads, writes and validates 'mzQC' files. The 'mzQC' format is a 
  standardized file format for the exchange, transmission, and archiving of 
  quality metrics derived from biological mass spectrometry data, as defined 
  by the HUPO-PSI (Human Proteome Organisation - Proteomics Standards Initiative) 
  Quality Control working group. 
  See <https://hupo-psi.github.io/mzQC/> for details.
Infer log-linear Poisson Graphical Model with an auxiliary data
    set. Hot-deck multiple imputation method is used to improve the reliability
    of the inference with an auxiliary dataset. Standard log-linear Poisson 
    graphical model can also be used for the inference and the Stability 
    Approach for Regularization Selection (StARS) is implemented to drive the 
    selection of the regularization parameter. The method is fully described in
    <doi:10.1093/bioinformatics/btx819>.
Functions for semi-automated quality control of bulk RNA-seq data.
Provides function for RNA secondary structure plotting, comparison and module mining. Given a RNA secondary structure, you can obtain stem regions, hairpin loops, internal loops, bulge loops and multibranch loops of this RNA structure using this program. They are the basic modules of RNA secondary structure. For each module you get, you can use this program to label the RNA structure with a specific color. You can also use this program to compare two RNA secondary structures to get a score that represents similarity. Reference: Reuter JS, Mathews DH (2010) <doi:10.1186/1471-2105-11-129>.
Interface to access data via the United States Department of 
  Agriculture's National Agricultural Statistical Service (NASS) 'Quick Stats' 
  web API <https://quickstats.nass.usda.gov/api>. Convenience functions 
  facilitate building queries based on available parameters and valid parameter 
  values. This product uses the NASS API but is not endorsed or certified by NASS.
Facilitates mapping by making natural earth map data from <https://www.naturalearthdata.com/> more easily available to R users.
Vector map data from <http://www.naturalearthdata.com/>. Access functions are provided in the accompanying package 'rnaturalearth'.
Image data used as examples in the loon R package.
Provides a set of functions to access a data visualization web service. For more information and a tutorial on how to use it, see https://navicell.curie.fr/pages/nav_web_service.html and https://github.com/sysbio-curie/RNaviCell.
Use the <http://api.nbp.pl/> API through R. Retrieve
    currency exchange rates and gold prices data published by the
    National Bank of Poland in form of convenient R objects.
Provides the libraries of the EUtils operations for the
        RNCBI package.
Contains functions to retrieve, organize, and visualize weather data from the NCEP/NCAR Reanalysis (<http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html>) and NCEP/DOE Reanalysis II (<http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html>) datasets.  Data are queried via the Internet and may be obtained for a specified spatial and temporal extent or interpolated to a point in space and time.  We also provide functions to visualize these weather data on a map.  There are also functions to simulate flight trajectories according to specified behavior using either NCEP wind data or data specified by the user.
An interface to the Nexus Class Library which allows parsing
    of NEXUS, Newick and other phylogenetic tree file formats. It provides
    elements of the file that can be used to build phylogenetic objects
    such as ape's 'phylo' or phylobase's 'phylo4(d)'. This functionality
    is demonstrated with 'read_newick_phylo()' and 'read_nexus_phylo()'.
Extract the implied risk neutral density from options using various methods.
To incorporate neighbor genotypic identity into genome-wide association studies, the package provides a set of functions for variation partitioning and association mapping. The theoretical background of the method is described in Sato et al. (2021) <doi:10.1038/s41437-020-00401-w>.
To enable quantitative trait loci mapping of neighbor effects, this package extends a single-marker regression to interval mapping. The theoretical background of the method is described in Sato et al. (2021) <doi:10.1093/g3journal/jkab017>.
An implementation of a method based on information theory devised 
    for the identification of genes showing a significant variation of expression
    across multiple conditions. Given expression estimates from any number of 
    RNA-Seq samples and conditions it identifies genes or transcripts with a 
    significant variation of expression across all the conditions studied, 
    together with the samples in which they are over- or under-expressed. 
    Zambelli et al. (2018) <doi:10.1093/nar/gky055>.
Within this package the XML-RPC API to NEOS <https://neos-server.org/neos/> is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.
Provides functions to compute the modularity and modularity-related roles in networks. It is a wrapper around the rgraph library (Guimera & Amaral, 2005, <doi:10.1038/nature03288>).
An interface to the 'NetCDF' file formats designed by Unidata
  for efficient storage of array-oriented scientific data and descriptions.
  Most capabilities of 'NetCDF' version 4 are supported. Optional conversions
  of time units are enabled by 'UDUNITS' version 2, also from Unidata.
Interface to use and access Wilensky's 'NetLogo' (Wilensky 1999) from R using either headless (no GUI) or interactive GUI mode. Provides functions to load models, execute commands, and get values from reporters. Mostly analogous to the 'NetLogo' 'Mathematica' Link <https://github.com/NetLogo/Mathematica-Link>.
A collection of tools for measuring the similarity of text messages and tracing the flow of messages over
    time and across media.
Provides access to phyloinformatic data in 'NeXML' format.  The
    package should add new functionality to R such as the possibility to
    manipulate 'NeXML' objects in more various and refined way and compatibility
    with 'ape' objects.
Generation of univariate and multivariate data that follow the generalized 
  Poisson distribution. The details of the univariate part are explained in 
  Demirtas (2017) <doi: 10.1080/03610918.2014.968725>, and the multivariate part is 
  an extension of the correlated Poisson data generation routine that was introduced in 
  Yahav and Shmueli (2012) <doi: 10.1002/asmb.901>.
A function setVectorSeed() is provided. Its argument
        is a numeric vector of an arbitrary nonzero length, whose
        components have integer values from [0, 2^32-1]. The input
        vector is transformed using AES (Advanced Encryption Standard)
        algorithm into an initial state of Mersenne-Twister random
        number generator. The function provides a better alternative
        to the R base function set.seed(), if the input vector is
        a single integer. Initializing a stream of random numbers
        with a vector is a convenient way to obtain several streams,
        each of which is identified by several integer indices.
Provides a set of functions for working with
    Random Number Generators (RNGs). In particular, a generic
    S4 framework is defined for getting/setting the current RNG, or RNG data
    that are embedded into objects for reproducibility.
    Notably, convenient default methods greatly facilitate the way current
    RNG settings can be changed.
It is a dedicated package to WELL pseudo random generators, 
  which were introduced in Panneton et al. (2006), ``Improved Long-Period 
  Generators Based on Linear Recurrences Modulo 2'', see <doi:10.1145/1132973.1132974>. 
  But this package is not intended to be used directly, you are strongly __encouraged__ 
  to use the 'randtoolbox' package, which depends on this package.
Tools for downloading and analyzing CDC NHANES data, with a focus
    on analytical laboratory data.
Provides very fast read and write access to images stored in the
    NIfTI-1, NIfTI-2 and ANALYZE-7.5 formats, with seamless synchronisation
    of in-memory image objects between compiled C and interpreted R code. Also
    provides a simple image viewer, and a C/C++ API that can be used by other
    packages. Not to be confused with 'RNiftyReg', which performs image
    registration and applies spatial transformations.
Provides an 'R' interface to the 'NiftyReg' image registration tools
    <https://github.com/KCL-BMEIS/niftyreg>. Linear and nonlinear registration
    are supported, in two and three dimensions.
Allows the user to implement a dark/light toggle mode in 'shiny' using the 'Nightly' 'JavaScript' library. 
    The default mode is dark/light however the user can also specify other colours.
A comprehensive suite of functions to perform and visualise 
    pairwise and network meta-analysis with aggregate binary or continuous
    missing participant outcome data. The package covers core Bayesian one-stage
    models implemented in a systematic review with multiple interventions, 
    including fixed-effect and random-effects network meta-analysis, 
    meta-regression, evaluation of the consistency assumption via the 
    node-splitting approach and the unrelated mean effects model, and 
    sensitivity analysis. Missing participant outcome data are addressed in all 
    models of the package. The package also offers a rich, user-friendly 
    visualisation toolkit that aids in appraising and interpreting the results 
    thoroughly and preparing the manuscript for journal submission. 
    The visualisation tools comprise the network plot, forest plots, panel of 
    diagnostic plots, heatmaps on the extent of missing participant outcome data
    in the network, league heatmaps on estimation and prediction, rankograms, 
    Bland-Altman plot, leverage plot, deviance scatterplot, heatmap of 
    robustness, and barplot of Kullback-Leibler divergence. The package also 
    allows the user to export the results to an Excel file at the working 
    directory.
Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) <doi:10.1007/s11306-017-1178-y>.
Implementation of a Recurrent Neural Network architectures in native R, including Long Short-Term Memory (Hochreiter and Schmidhuber, <doi:10.1162/neco.1997.9.8.1735>), Gated Recurrent Unit (Chung et al., <arXiv:1412.3555>) and vanilla RNN.
Client for many 'NOAA' data sources including the 'NCDC' climate
    'API' at <https://www.ncdc.noaa.gov/cdo-web/webservices/v2>, with functions for
    each of the 'API' 'endpoints': data, data categories, data sets, data types,
    locations, location categories, and stations. In addition, we have an interface
    for 'NOAA' sea ice data, the 'NOAA' severe weather inventory, 'NOAA' Historical
    Observing 'Metadata' Repository ('HOMR') data, 'NOAA' storm data via 'IBTrACS',
    tornado data via the 'NOAA' storm prediction center, and more.
An interface to the National Oceanic and Atmospheric Administration's Operational Model Archive and Distribution System (NOMADS, see <https://nomads.ncep.noaa.gov/> for more information) that allows R users to quickly and efficiently download global and regional weather model data for processing.  rNOMADS currently supports a variety of models ranging from global weather data to an altitude of over 40 km, to high resolution regional weather models, to wave and sea ice models.  rNOMADS can retrieve binary data in grib format as well as import ascii data directly into R by interfacing with the GrADS-DODS system.
Inverse normal transformation (INT) based genetic association testing. These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not, as when the residual distribution exhibits excess skew or kurtosis. Moreover, INT-based tests dominate standard linear regression in terms of power. These tests may be classified into two types. In direct INT (D-INT), the phenotype is itself transformed. In indirect INT (I-INT), phenotypic residuals are transformed. The omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach. See McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. "Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies" <doi:10.1111/biom.13214>.
Programmatic interface to the
    Web Service methods provided by the National 'Phenology' Network
    (<https://usanpn.org/>), which includes data on various life history
    events that occur at specific times.
Apply sensitivity analysis for offline policy evaluation, as
    implemented in Jung et al. (2017) <arXiv:1702.04690> based
    on Rosenbaum and Rubin (1983) <http://www.jstor.org/stable/2345524>.
Utility functions to retrieve data from the UK National River Flow
  Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions:
  <https://nrfa.ceh.ac.uk/costs-terms-and-conditions>).
  The package contains R wrappers to the UK NRFA data temporary-API. There are
  functions to retrieve stations falling in a bounding box, to generate a map
  and extracting time series and general information. The package is fully
  described in Vitolo et al (2016) "rnrfa: An R package to Retrieve, Filter and
  Visualize Data from the UK National River Flow Archive"
  <https://journal.r-project.org/archive/2016/RJ-2016-036/RJ-2016-036.pdf>.
Routines to interact with the Numerai Machine Learning Tournament
  API <https://numer.ai>. The functionality includes the ability to automatically download the
  current tournament data, submit predictions, and to get information for your
  user.
Creates JavaScript charts with the 'nvd3' library. So far only the multibar chart, the horizontal multibar chart, the line chart and the line chart with focus are available.
This web client interfaces Unpaywall <https://unpaywall.org/products/api>, formerly
    oaDOI, a service finding free full-texts of academic papers by linking DOIs with 
    open access journals and repositories. It provides unified access to various data sources 
    for open access full-text links including Crossref and the Directory of Open Access 
    Journals (DOAJ). API usage is free and no registration is required.
Project road network development based on an existing road 
    network, target locations to be connected by roads and a cost surface. Road 
    projection methods include minimum spanning tree with least cost path 
    (Kruskal's algorithm (1956) <doi:10.2307/2033241>), least cost path 
    (Dijkstra's algorithm (1959) <doi:10.1007/BF01386390>) or snapping. 
    These road network projection methods are ideal for use with land cover
    change projection models.
A collection of methods for the robust analysis of univariate and
    multivariate functional data, possibly in high-dimensional cases, and hence
    with attention to computational efficiency and simplicity of use. See the R 
    Journal publication of Ieva et al. (2019) <doi:10.32614/RJ-2019-032> for an 
    in-depth presentation of the 'roahd' package. See Aleman-Gomez et al. (2021) 
    <arXiv:2103.08874> for details about the concept of depthgram.
Provides an interface to the OAuth 1.0 specification
        allowing users to authenticate via OAuth to the
        server of their choice.
Base S4-classes and functions for robust asymptotic statistics.
Includes 'sysdata.rda' file for packages of the 'RobASt' - family of packages; is
        currently used by package 'RobExtremes' only.
Implementation of a variety of methods to compute
    the robustness of ecological interaction networks with binary interactions 
    as described in <doi:10.1002/env.2709>. In particular, using the Stochastic 
    Block Model and its bipartite counterpart, the Latent Block Model to put a 
    parametric model on the network, allows the comparison of the robustness of 
    networks differing in species richness and number of interactions. It also
    deals with networks that are partially sampled and/or with missing values.
Conditionally unbiased bounded influence estimates as described in 
  Kuensch et al. (1989) <doi:10.1080/01621459.1989.10478791> in three special cases of 
  the generalized linear model: Bernoulli, Binomial, and Poisson distributed responses.
Methods for analysis of compositional data including robust
    methods  (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace 
    rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), 
    count zeros (<doi:10.1177/1471082X14535524>), 
    methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier
    detection for compositional data, (robust) principal component analysis for
    compositional data, (robust) factor analysis for compositional data, (robust)
    discriminant analysis for compositional data (Fisher rule), robust regression
    with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), 
    contingency (<doi:10.1080/03610926.2013.824980>) 
    and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) 
    and (robust) Anderson-Darling normality tests for
    compositional data as well as popular log-ratio transformations (addLR, cenLR,
    isomLR, and their inverse transformations). In addition, visualisation and
    diagnostic tools are implemented as well as high and low-level plot functions
    for the ternary diagram.
Robust pairwise correlations based on estimates of scale,
  particularly on "FastQn" one-step M-estimate.
Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <arXiv:1905.06201>, and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) <DOI:10.1007/978-1-4939-3076-0_12> and Dehling, Fried and Wendler (2020) <DOI:10.1093/biomet/asaa004>. Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) <DOI:10.1080/01621459.2019.1629938>, Dehling et al. (2017) <DOI:10.1017/S026646661600044X>, and Wied et al. (2014) <DOI:10.1016/j.csda.2013.03.005>.
Gene-environment (G×E) interactions have important implications to elucidate the 
    etiology of complex diseases beyond the main genetic and environmental effects. 
    Outliers and data contamination in disease phenotypes of G×E studies have been commonly 
    encountered, leading to the development of a broad spectrum of robust penalization methods. 
    Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing 
    studies. We develop a robust Bayesian variable selection method for G×E interaction 
    studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and 
    outliers in the response variable while conducting variable selection by accounting for 
    structural sparsity. In particular, the spike-and-slab priors have been imposed on both 
    individual and group levels to identify important main and interaction effects. An efficient 
    Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo 
    algorithms of the proposed and alternative methods are efficiently implemented in C++.
Locations problems, M-estimates of coefficients and scale
        in linear regression, Weights for bounded influence regression,
        Covariance matrix of the coefficient estimates, Asymptotic
        relative efficiency of regression M-estimates, Robust testing
        in linear models, High breakdown point regression, M-estimates
        of covariance matrices, M-estimates for discrete generalized
        linear models.
Optimally robust estimation for extreme value distributions using S4 classes and
            methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and
            'ROptEst').
Implementations for several robust procedures that allow for (online)
        extraction of the signal of univariate or multivariate time series by
        applying robust regression techniques to a moving time window are provided.
        Included are univariate filtering procedures based on repeated-median 
        regression as well as hybrid and trimmed filters derived from it; 
        see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive 
        online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105> 
        and the slope comparing adaptive repeated median by Borowski and Fried (2013) 
        <doi:10.1007/s11222-013-9391-7> choose the width of the moving time 
        window adaptively. Multivariate versions are also provided; see  
        Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate 
        online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393>  
        for a multivariate slope comparing adaptive repeated median. Furthermore, 
        a repeated-median based filter with automatic outlier replacement and 
        shift detection is provided; see Fried (2004) <doi:10.1080/10485250410001656444>.
Functions for implementing robust methods for functional linear regression. In the functional linear regression, we consider scalar-on-function linear regression and function-on-function linear regression. More details, see Beyaztas, U., and Shang, H. L. (2021) <arXiv:2111.01238> and Beyaztas, U., and Shang, H. L. (2022) <arXiv:2203.05065>.
Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) <DOI:10.1080/00949655.2017.1359601>.
Assesses the robustness of the community structure of a network found by one or more community detection algorithm to give indications about their reliability. It detects if the community structure found by a set of algorithms is statistically significant and compares the different selected detection algorithms on the same network. robin helps to choose among different community detection algorithms the one that better fits the network of interest. Reference in Policastro V., Righelli D., Carissimo A., Cutillo L., De Feis I. (2021) <https://journal.r-project.org/archive/2021/RJ-2021-040/index.html>.
Execute API calls to the RobinHood <https://robinhood.com> investing platform. Functionality includes accessing account data and current holdings, retrieving investment statistics and quotes, placing and canceling orders, getting market trading hours, searching investments by popular tag, and interacting with watch lists.
Client for the Ocean Biodiversity Information System (<https://obis.org>).
Implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) <arXiv:1204.3358> and Agamennoni et al. (2018) <doi:10.1109/ICRA.2011.5979605>, the innovative outlier robust filter of Ruckdeschel et al. (2014) <arXiv:1204.3358>, as well as the innovative and additive outlier robust filter of Fisch et al. (2020) <arXiv:2007.03238>.
Functions for the determination of optimally robust influence curves and
            estimators in case of normal location and/or scale.
Functions for the determination of optimally robust influence curves and
            estimators for preprocessing omics data, in particular gene expression data.
A framework for estimating ensembles of meta-analytic models
    (assuming either presence or absence of the effect, heterogeneity, and
    publication bias). The RoBMA framework uses Bayesian model-averaging to 
    combine the competing meta-analytic models into a model ensemble, weights 
    the posterior parameter distributions based on posterior model probabilities 
    and uses Bayes factors to test for the presence or absence of the
    individual components (e.g., effect vs. no effect; Bartoš et al., 2022, 
    <doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022, 
    <doi:10.1037/met0000405>). Users can define a wide range of non-informative 
    or informative prior distributions for the effect size, heterogeneity, 
    and publication bias components (including selection models and PET-PEESE). 
    The package provides convenient functions for summary, visualizations, and 
    fit diagnostics.
Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) <doi:10.18637/jss.v103.i13>.
This companion package extends the package 'robmed' (Alfons, Ates & Groenen, 2022b; <doi:10.18637/jss.v103.i13>) in various ways.  Most notably, it provides a graphical user interface for the robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>) to make the method more accessible to less proficient 'R' users, as well as functions to export the results as a table in a 'Microsoft Word' or 'Microsoft Powerpoint' document, or as a 'LaTeX' table. Furthermore, the package contains a 'shiny' app to compare various bootstrap procedures for mediation analysis on simulated data.
Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.
Finite mixture models are a popular technique for modelling unobserved heterogeneity or to approximate general distribution functions in a semi-parametric way. They are used in a lot of different areas such as astronomy, biology, economics, marketing or medicine.
             This package is the implementation of popular robust mixture regression methods based on different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation.
             The implemented algorithms includes:  CTLERob stands for Component-wise adaptive Trimming Likelihood Estimation based mixture regression; mixbi stands for mixture regression based on bi-square estimation; mixLstands for mixture regression based on Laplacian distribution; TLE stands for Trimmed Likelihood Estimation based mixture regression. For more detail of the algorithms, please refer to below references. 
             Reference: Chun Yu, Weixin Yao, Kun Chen (2017) <doi:10.1002/cjs.11310>.
             NeyKov N, Filzmoser P, Dimova R et al. (2007) <doi:10.1016/j.csda.2006.12.024>.
             Bai X, Yao W. Boyer JE (2012) <doi:10.1016/j.csda.2012.01.016>.
             Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) <arXiv:2005.11599>.
Implementations of several robust nonparametric two-sample tests
    for location or scale differences. The test statistics are based on robust
    location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators
    as described in Fried & Dehling (2011) <doi:10.1007/s10260-011-0164-1>.
    The p-values can be computed via the permutation principle, the randomization principle, or by using
    the asymptotic distributions of the test statistics under the null hypothesis, which ensures
    (approximate) distribution independence of the test decision. To test for a difference in
    scale, we apply the tests for location difference to transformed observations; see Fried (2012) <doi:10.1016/j.csda.2011.02.012>.
    Random noise on a small range can be added to the original observations in order to
    hold the significance level on data from discrete distributions.
    The location tests assume homoscedasticity and the scale tests require the
    location parameters to be zero.
An implementation of Bayesian online changepoint detection (Adams and MacKay (2007) <arXiv:0710.3742>) with an option for probability based outlier detection and removal (Wendelberger et. al. (2021) <arXiv:2112.12899>). Building on the independent multivariate constant mean model implemented in the 'R' package 'ocp', this package models multivariate data as multivariate normal about a linear trend, defined by user input covariates, with an unstructured error covariance. Changepoints are identified based on a probability threshold for windows of points.
Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). robomit the estimate i) the bias-adjusted treatment correlation or effect and ii) the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result based on the framework by Oster (2019). Additionally, robomit offers a set of sensitivity analysis and visualization functions. See: Oster, E. 2019. <doi:10.1080/07350015.2016.1227711>.
Suite of utilities for accessing and manipulating
    data from the 'KoboToolbox' API. 'KoboToolbox' is a robust
    platform designed for field data collection in various disciplines.
    This package aims to simplify the process of fetching and handling
    data from the API. Detailed documentation for the 'KoboToolbox' API
    can be found at <https://support.kobotoolbox.org/api.html>.
Provides functions to download and parse 'robots.txt' files.
        Ultimately the package makes it easy to check if bots
        (spiders, crawler, scrapers, ...) are allowed to access specific
        resources on a domain.
Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer() calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit(). Artificial light curves can be generated using the function tsgen(). For more details see the corresponding article: Thieler, Fried and Rathjens (2016), Journal of Statistical Software 69(9), 1-36, <doi:10.18637/jss.v069.i09>.
Three-step regression and inference for cellwise and casewise contamination.
We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter  estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.
Functions for the determination of optimally robust influence curves in case of
            linear regression with unknown scale and standard normal distributed errors where
            the regressor is random.
A framework for estimating ensembles of parametric survival models
    with different parametric families. The RoBSA framework uses Bayesian 
    model-averaging to combine the competing parametric survival models into 
    a model ensemble, weights the posterior parameter distributions based on 
    posterior model probabilities and uses Bayes factors to test for the 
    presence or absence of the individual predictors or preference for a 
    parametric family (Bartoš, Aust & Haaf, 2022, <doi:10.1186/s12874-022-01676-9>).
    The user can define a wide range of informative priors for all parameters 
    of interest. The package provides convenient functions for summary, visualizations, 
    fit diagnostics, and prior distribution calibration.
An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) <http://proceedings.mlr.press/v108/cisneros20a.html>.
Allows loading and displaying an Observable notebook (online JavaScript  
    notebooks powered by <https://observablehq.com>) as an HTML Widget in an R 
    session, 'shiny' application or 'rmarkdown' document.
Fast algorithms for the Theil-Sen estimator,
  Siegel's repeated median slope estimator, and Passing-Bablok regression.
  The implementation is based on algorithms by
  Dillencourt et. al (1992) <doi:10.1142/S0218195992000020> and Matousek et. al (1998)  <doi:10.1007/PL00009190>.
  The implementations are detailed in 
  Raymaekers (2023) <doi:10.32614/RJ-2023-012> and 
  Raymaekers J., Dufey F. (2022) <arXiv:2202.08060>.
  All algorithms run in quasilinear time.
Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>.  This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.
Functions to perform robust stepwise split regularized regression. The approach first 
             uses a robust stepwise algorithm to split the variables into the models of an ensemble. 
             An adaptive robust regularized estimator is then applied to each subset of predictors
             in the models of an ensemble.
Robust (outlier-resistant) estimators of finite population
    characteristics like of means, totals, ratios, regression, etc. Available
    methods are M- and GM-estimators of regression, weight reduction,
    trimming, and winsorization. The package extends the 'survey'
    <https://CRAN.R-project.org/package=survey> package.
An implementation of Bayesian model-averaged t-test that allows 
    users to draw inference about the presence vs absence of the effect, 
    heterogeneity of variances, and outliers. The 'RoBTT' packages estimates model 
    ensembles of models created as a combination of the competing hypotheses and uses 
    Bayesian model-averaging to combine the models using posterior model probabilities. 
    Users can obtain the model-averaged posterior distributions and inclusion Bayes 
    factors which account for the uncertainty in the data generating process 
    (Maier et al., 2022, <doi:10.31234/osf.io/d5zwc>).
    Users can define a wide range of informative priors for all parameters 
    of interest. The package provides convenient functions for summary, visualizations, 
    and fit diagnostics.
Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown. Also included are functions for conducting sensitivity analyses under correlated effects weighting and producing RVE-based forest plots.
Methods for robust statistics, a state of the art in the early
  2000s, notably for robust regression and robust multivariate analysis.
An implementation of easy tools for outlier robust inference in
    two-stage least squares (2SLS) models. The user specifies a reference 
    distribution against which observations are classified as outliers or not. 
    After removing the outliers, adjusted standard errors are automatically 
    provided. Furthermore, several statistical tests for the false outlier 
    detection rate can be calculated. The outlier removing algorithm can be 
    iterated a fixed number of times or until the procedure converges. The 
    algorithms and robust inference are described in more detail in Jiao (2019) 
    <https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.
R functions for the computation of the truncated maximum
	     likelihood and the robust accelerated failure time regression 
	     for gaussian and log-Weibull case.
Robust tests (RW, RPB and RGF) are provided for testing the equality of several long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW, RPB and RGF tests are robust versions of Welch's F test proposed by Welch (1951) <doi:10.2307/2332579>, parametric bootstrap test proposed by Krishnamoorthy et. al (2007) <doi:10.1016/j.csda.2006.09.039>; and generalized F test proposed by Weerahandi (1995) <doi:10.2307/2532947>;, respectively. These tests are based on the modified maximum likelihood (MML) estimators proposed by Tiku(1967, 1968) <doi:10.2307/2333859>, <doi:10.1080/01621459.1968.11009228>.
"Essential" Robust Statistics.
 Tools allowing to analyze data with robust methods.  This includes
 regression methodology including model selections and multivariate
 statistics where we strive to cover the book "Robust Statistics,
 Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
Fits the robust Bayesian Copas (RBC) selection model of Bai et al. (2020) <arXiv:2005.02930> for correcting and quantifying publication bias in univariate meta-analysis. Also fits standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) <doi:10.1093/biostatistics/kxx004>.
Robust estimators for the beta regression, useful for modeling 
  bounded continuous data. Currently, four types of robust estimators are supported. 
  They depend on a tuning constant which may be fixed or selected by a 
  data-driven algorithm also implemented in the package. Diagnostic tools 
  associated with the fitted model, such as the residuals and goodness-of-fit 
  statistics, are implemented. Robust Wald-type tests are available. More details
  about robust beta regression are described in Maluf et al. (2022) <arXiv:2209.11315>.
Robust tests (RW and RF) are provided for testing the equality of two long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW test is a robust version of Welch's two sample t test and the RF is a robust fiducial based test. The RW and RF tests are proposed using the adaptive modified maximum likelihood (AMML) estimators derived by Tiku and Surucu (2009) <doi:10.1016/j.spl.2008.12.001> and Donmez (2010) <https://open.metu.edu.tr/bitstream/handle/11511/19440/index.pdf>.
Bayesian robust fitting of linear mixed effects models through weighted likelihood equations and approximate Bayesian computation as proposed by Ruli et al. (2017) <arXiv:1706.01752>.
Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang (2018) <arXiv:1707.08215>, Mengyang Gu, Fangzheng Xie and Long Wang (2021) <arXiv:1807.03829>, Mengyang Gu, Kyle Anderson and Erika McPhillips (2021) <arXiv:1810.11664>.
Collection of methods for robust covariance and (sparse) precision matrix estimation based on Loh and Tan (2018) <doi:10.1214/18-EJS1427>.
Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 <doi:10.1016/j.patcog.2009.03.027>, allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.
Implementation of corrected two-sample tests. A corrected version of the Pearson and Kendall correlation tests, 
 the Mann-Whitney (Wilcoxon) rank sum test, the Wilcoxon signed rank test and a variance test are implemented.
 The package also proposes a test for the median and an independence test between two continuous variables of Kolmogorov-Smirnov's type. 
 All these corrected tests are asymptotically calibrated in the sense that the probability of rejection under the null hypothesis is 
 asymptotically equal to the level of the test. See <arXiv:2211.08784> for more details on the statistical tests.
Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison.
Outliers virtually exist in any datasets of any application field. 
  To avoid the impact of outliers, we need to use robust estimators. 
  Classical estimators of multivariate mean and covariance matrix are 
  the sample mean and the sample covariance matrix. Outliers will 
  affect the sample mean and the sample covariance matrix, and thus 
  they will affect the classical factor analysis which depends on 
  the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser, 
  P. and Croux, C. (2003) <doi:10.1016/S0047-259X(02)00007-6>). 
  So it is necessary to use the robust estimators of the sample 
  mean and the sample covariance matrix. There are several robust 
  estimators in the literature: Minimum Covariance Determinant estimator, 
  Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid, 
  M, S, and Stahel-Donoho. 
  The most direct way to make multivariate analysis more robust is to replace
  the sample mean and the sample covariance matrix of the classical estimators 
  to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006) 
  <doi:10.1002/0470010940>) (Todorov, V. and Filzmoser, P. (2009) 
  <doi:10.18637/jss.v032.i03>), which is our choice of robust factor
  analysis. We created an object oriented solution for robust factor 
  analysis based on new S4 classes.
Robust parameter estimation and prediction of Gaussian stochastic 
 process emulators. It allows for robust parameter estimation and prediction using 
 Gaussian stochastic process emulator. It also implements the parallel partial 
 Gaussian stochastic process emulator for computer model with massive outputs 
 See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; 
 Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.
Robust methods for high-dimensional data, in particular linear
    model selection techniques based on least angle regression and sparse
    regression.
Inference for the treatment effect with possibly invalid instrumental variables via TSHT('Guo et al.' (2016) <arXiv:1603.05224>) and SearchingSampling('Guo' (2021) <arXiv:2104.06911>), which are effective for both low- and high-dimensional covariates and instrumental variables; test of endogeneity in high dimensions ('Guo et al.' (2016) <arXiv:1609.06713>).
Provides an easy way to compute the Theil Sehn Regression method and also the Siegel Regression Method which are both robust methods base on the median of slopes between all pairs of data. In contrast with the least squared linear regression, these methods are not sensitive to outliers. Theil, H. (1992) <doi:10.1007/978-94-011-2546-8_20>, Sen, P. K. (1968) <doi:10.1080/01621459.1968.10480934>.
Computationally efficient tool for performing variable selection and obtaining robust estimates, which implements robust variable selection procedure proposed by Wang, X., Jiang, Y., Wang, S., Zhang, H. (2013) <doi:10.1080/01621459.2013.766613>. Users can enjoy the near optimal, consistent, and oracle properties of the procedures.
Implements the Robust Scoring Equations estimator to fit linear
    mixed effects models robustly.
    Robustness is achieved by modification of the scoring equations
    combined with the Design Adaptive Scale approach.
Robust inference methods for fixed-effect and random-effects models of meta-analysis are implementable. The robust methods are developed using the density power divergence that is a robust estimating criterion developed in machine learning theory, and can effectively circumvent biases and misleading results caused by influential outliers. The density power divergence is originally introduced by Basu et al. (1998) <doi:10.1093/biomet/85.3.549>, and the meta-analysis methods are developed by Noma et al. (2022) <forthcoming>.
Implements two-sample tests for paired data with missing values (Fong, Huang, Lemos and McElrath 2018, Biostatics, <doi:10.1093/biostatistics/kxx039>) and modified Wilcoxon-Mann-Whitney two sample location test, also known as the Fligner-Policello test.
Methods for aggregating ranked lists, especially lists of
    genes. It implements the Robust Rank Aggregation Kolde et al (2012)
    <doi:10.1093/bioinformatics/btr709> and some other simple algorithms 
    for the task. RRA method uses a probabilistic model for aggregation that 
    is robust to noise and also facilitates the calculation of significance
    probabilities for all the elements in the final ranking.
A collection of functions to compute the Rao-Stirling diversity index
	(Porter and Rafols, 2009) <DOI:10.1007/s11192-008-2197-2> and its extension to
	acknowledge missing data (i.e.,	uncategorized references) by calculating its
	interval of uncertainty using	mathematical optimization as proposed in Calatrava
	et al. (2016) <DOI:10.1007/s11192-016-1842-4>.
	The Rao-Stirling diversity index is a well-established bibliometric indicator
	to measure the interdisciplinarity of scientific publications. Apart from the
	obligatory dataset of publications with their respective references and	a
	taxonomy of disciplines that categorizes references as well as a measure of
	similarity between the disciplines, the Rao-Stirling diversity index requires
	a complete categorization of all references of a publication into disciplines.
	Thus, it fails for a incomplete categorization; in this case, the robust
	extension has to be used, which encodes the uncertainty caused by missing
	bibliographic data as an uncertainty interval.
	Classification / ACM - 2012: Information systems ~ Similarity measures,
	Theory of computation ~ Quadratic	programming, Applied computing ~ Digital
	libraries and archives.
Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm.
Functions for Robust Bayesian Small Area Estimation.
Data sets are often corrupted by outliers. When data are multivariate outliers can be classified as case-wise or cell-wise. The latters are particularly challenge to handle. We implement a robust estimation procedure for Seemingly Unrelated Regression Models which is able to cope well with both type of outliers. Giovanni Saraceno, Fatemah Alqallaf, Claudio Agostinelli (2021) <arXiv:2107.00975>.
Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.
Robustness -- 'eXperimental', 'eXtraneous', or 'eXtraordinary'
  Functionality for Robust Statistics.  Hence methods which are not well established,
  often related to methods in package 'robustbase'.  Amazingly, 'BACON()', originally by
  Billor, Hadi, and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>
  has become established in places.  The "barrow wheel" `rbwheel()` is from
  Stahel and Mächler (2009) <doi:10.1111/j.1467-9868.2009.00706.x>.
Helps users in quickly visualizing risk-of-bias 
    assessments performed as part of a systematic review. It allows users to 
    create weighted bar-plots of the distribution of risk-of-bias judgments 
    within each bias domain, in addition to traffic-light plots of the 
    specific domain-level judgments for each study. The resulting figures are 
    of publication quality and are formatted according the risk-of-bias 
    assessment tool use to perform the assessments. Currently, the supported 
    tools are ROB2.0 (for randomized controlled trials; Sterne et al (2019)  
    <doi:10.1136/bmj.l4898>), ROBINS-I (for non-randomised studies of 
    interventions; Sterne et al (2016) <doi:10.1136/bmj.i4919>), and QUADAS-2 
    (for diagnostic accuracy studies; Whiting et al (2011) 
    <doi:10.7326/0003-4819-155-8-201110180-00009>).
Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.
Aggregates multiple Receiver Operating Characteristic (ROC) curves 
    obtained from different sources into one global ROC. Additionally, it’s 
    also possible to calculate the aggregated precision-recall (PR) curve.
Generation of Box-Cox based ROC curves and several aspects of inferences and hypothesis testing. Can be used when inferences for one biomarker (Bantis LE, Nakas CT, Reiser B. (2018) <doi:10.1002/bimj.201700107>) are of interest or when comparisons of two correlated biomarkers (Bantis LE, Nakas CT, Reiser B. (2021) <doi:10.1002/bimj.202000128>) are of interest. Provides inferences and comparisons around the AUC, the Youden index, the sensitivity at a given specificity level (and vice versa), the optimal operating point of the ROC curve (in the Youden sense), and the Youden based cutoff.
Functions for a classification method based on receiver operating characteristics (ROC). Briefly, features are selected according to their ranked AUC value in the training set. The selected features are merged by the mean value to form a meta-gene. The samples are ranked by their meta-gene value and the meta-gene threshold that has the highest accuracy in splitting the training samples is determined. A new sample is classified by its meta-gene value relative to the threshold. In the first place, the package is aimed at two class problems in gene expression data, but might also apply to other problems.
Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data.
The algorithm provided in this package generates perfect sample for unimodal or multimodal posteriors. Read Once Coupling From The Past, with Metropolis-Multishift is used to generate a perfect sample for a given posterior density based on the two extreme starting paths, minimum and maximum of the most interest range of the posterior. It uses the monotone random operation of multishift coupler which allows to sandwich all of the state space in one point. It means both Markov Chains starting from the maximum and minimum will be coalesced. The generated sample is independent from the starting points. It is useful for mixture distributions too. The output of this function is a real value as an exact draw from the posterior distribution.
Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.
The Reproducible Open Coding Kit ('ROCK', and this package, 'rock')
  was developed to facilitate reproducible and open coding, specifically
  geared towards qualitative research methods. Although it is a
  general-purpose toolkit, three specific applications have been
  implemented, specifically an interface to the 'rENA' package that
  implements Epistemic Network Analysis ('ENA'), means to process notes
  from Cognitive Interviews ('CIs'), and means to work with decentralized
  construct taxonomies ('DCTs'). The 'ROCK' and this 'rock' package are described
  in the ROCK book <https:rockbook.org>.
A collection of functions for interpretation and presentation
    of regression analysis.  These functions are used
    to produce the statistics lectures in
    <https://pj.freefaculty.org/guides/>. Includes regression
    diagnostics, regression tables, and plots of interactions and
    "moderator" variables. The emphasis is on "mean-centered" and
    "residual-centered" predictors. The vignette 'rockchalk' offers a
    fairly comprehensive overview.  The vignette 'Rstyle' has advice
    about coding in R.  The package title 'rockchalk' refers to our
    school motto, 'Rock Chalk Jayhawk, Go K.U.'.
'R6' class interface for handling relational database connections using 'DBI' package as backend.
  The class allows handling of connections to e.g. PostgreSQL, MariaDB and SQLite.
  The purpose is having an intuitive object allowing straightforward handling of SQL databases.
A set of functions for receiver operating characteristic (ROC) curve 
    estimation and area under the curve (AUC) calculation.
    All functions are designed to work with aggregated data; 
    nevertheless, they can also handle raw samples. 
    In 'ROCket', we distinguish two types of ROC curve representations:
    1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR) 
    are functions of a parameter (the score),
    2) functions - TPR is a function of FPR.
    There are several ROC curve estimation methods available. An introduction to 
    the mathematical background of the implemented methods (and much more) can be found in 
    de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) <https://www.ine.pt/revstat/pdf/rs140101.pdf> 
    and Cai & Pepe (2004) <doi:10.1111/j.0006-341X.2004.00200.x>.
Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.
Connector to the REST API of a 'Rock' R server, to perform operations 
  on a remote R server session, or administration tasks. See 'Rock' documentation at
  <https://rockdoc.obiba.org/>.
Efficient diffusing of content across function documentations. Sections, parameters or dot parameters are extracted from function documentations and turned into valid Rd character strings, which are ready to diffuse into the 'roxygen' comments of another function by inserting inline code.
Non-inferiority test and diagnostic test are very important in clinical trails.
  This package is to get a p value from the non-inferiority test for ROC curves from diagnostic test.
A set of tools for working with Romanian personal numeric codes. 
    The core is a validation function which applies several verification
    criteria to assess the validity of numeric codes. This is accompanied by 
    functionality for extracting the different components of a personal numeric
    code. A personal numeric code is issued to all Romanian residents either at 
    birth or when they obtain a residence permit.
Estimates the pooled (unadjusted) Receiver Operating Characteristic (ROC) curve, the covariate-adjusted ROC (AROC) curve, and the covariate-specific/conditional ROC (cROC) curve by different methods, both Bayesian and frequentist. Also, it provides functions to obtain ROC-based optimal cutpoints utilizing several criteria. Based on Erkanli, A. et al. (2006) <doi:10.1002/sim.2496>; Faraggi, D. (2003) <doi:10.1111/1467-9884.00350>; Gu, J. et al. (2008) <doi:10.1002/sim.3366>; Inacio de Carvalho, V. et al. (2013) <doi:10.1214/13-BA825>; Inacio de Carvalho, V., and Rodriguez-Alvarez, M.X. (2018) <arXiv:1806.00473>; Janes, H., and Pepe, M.S. (2009) <doi:10.1093/biomet/asp002>; Pepe, M.S. (1998) <http://www.jstor.org/stable/2534001?seq=1>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1016/j.csda.2010.07.018>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1007/s11222-010-9184-1>. Please see Rodriguez-Alvarez, M.X. and Inacio, V. (20208) <arXiv:2003.13111> for more details.
Provides the robust gamma rank correlation coefficient as introduced by
	     Bodenhofer, Krone, and Klawonn (2013) <DOI:10.1016/j.ins.2012.11.026>
	     along with a permutation-based rank correlation test.
	     The rank correlation coefficient and the test are explicitly
	     designed for dealing with noisy numerical data.
Functions for (1) computing diagnostic test statistics (sensitivity, specificity, etc.) from confusion matrices with adjustment for various base rates or known prevalence based on McCaffrey et al (2003) <doi:10.1007/978-1-4615-0079-7_1>, (2) computing optimal cut-off scores with different criteria including maximizing sensitivity, maximizing specificity, and maximizing the Youden Index from Youden (1950) <https://acsjournals.onlinelibrary.wiley.com/doi/abs/10.1002/1097-0142%281950%293%3A1%3C32%3A%3AAID-CNCR2820030106%3E3.0.CO%3B2-3>, and (3) displaying and comparing classification statistics and area under the receiver operating characteristic (ROC) curves or area under the curves (AUC) across consecutive categories for ordinal variables.
ROC graphs, sensitivity/specificity curves, lift charts,
  and precision/recall plots are popular examples of trade-off
  visualizations for specific pairs of performance measures. ROCR is a
  flexible tool for creating cutoff-parameterized 2D performance curves
  by freely combining two from over 25 performance measures (new
  performance measures can be added using a standard interface).
  Curves from different cross-validation or bootstrapping runs can be
  averaged by different methods, and standard deviations, standard
  errors or box plots can be used to visualize the variability across
  the runs. The parameterization can be visualized by printing cutoff
  values at the corresponding curve positions, or by coloring the
  curve according to cutoff. All components of a performance plot can
  be quickly adjusted using a flexible parameter dispatching
  mechanism. Despite its flexibility, ROCR is easy to use, with only
  three commands and reasonable default values for all optional
  parameters.
Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. Computes eROC curve and the corresponding AUC for imperfect reference standard.
Optimal linear combination predictive signatures for maximizing the area between two Receiver Operating Characteristic (ROC) curves (treatment vs. control).
We develop the entire solution paths for ROC-SVM presented by Rakotomamonjy. The ROC-SVM solution path algorithm greatly facilitates the tuning procedure for regularization parameter, lambda in ROC-SVM by avoiding grid search algorithm which may be computationally too intensive. For more information on the ROC-SVM, see the report in the ROC Analysis in AI workshop(ROCAI-2004) : Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>.
Receiver Operating Characteristic (ROC)-guided survival trees and ensemble algorithms are implemented, providing a unified framework for tree-structured analysis with censored survival outcomes. A time-invariant partition scheme on the survivor population was considered to incorporate time-dependent covariates. Motivated by ideas of randomized tests, generalized time-dependent ROC curves were used to evaluate the performance of survival trees and establish the optimality of the target hazard/survival function. The optimality of the target hazard function motivates us to use a weighted average of the time-dependent area under the curve (AUC) on a set of time points to evaluate the prediction performance of survival trees and to guide splitting and pruning. A detailed description of the implemented methods can be found in Sun et al. (2019) <arXiv:1809.05627>.
An implementation of R's DBI interface using ODBC package as a
    back-end. This allows R to connect to any DBMS that has a ODBC driver.
A collection of functions for numerical construction of optimal discriminating designs. At the current moment T-optimal designs (which maximize the lower bound for the power of F-test for regression model discrimination), KL-optimal designs (for lognormal errors) and their robust analogues can be calculated with the package.
Show physics, math and engineering students how an ODE solver
    is made and how effective R classes can be for the construction of
    the equations that describe natural phenomena. Inspiration for this work 
    comes from the book on "Computer Simulations in Physics" 
    by Harvey Gould, Jan Tobochnik, and Wolfgang Christian. 
    Book link: <http://www.compadre.org/osp/items/detail.cfm?ID=7375>.
Provides an R6 class and several utility methods to
    facilitate the implementation of models based on ordinary
    differential equations. The heart of the package is a code generator
    that creates compiled 'Fortran' (or 'R') code which can be passed to
    a numerical solver. There is direct support for solvers contained
    in packages 'deSolve' and 'rootSolve'.
Implements the robust functional analysis of variance (RoFANOVA), described in Centofanti et al. (2021) <arXiv:2112.10643>. 
    It allows testing mean differences among groups of  functional data by being robust against the presence of outliers.
Tools for grading the coding style and documentation of R
  scripts. This is the R component of Roger the Omni Grader, an
  automated grading system for computer programming projects based on
  Unix shell scripts; see <https://gitlab.com/roger-project>. The
  package also provides an R interface to the shell scripts. Inspired by
  the lintr package.
Rogue ("wildcard") taxa are leaves with uncertain phylogenetic
  position.
  Their position may vary from tree to tree under inference methods that yield a
  tree set (e.g. bootstrapping, Bayesian tree searches, maximum parsimony).
  The presence of rogue taxa in a tree set can potentially remove all
  information from a consensus tree. The information content of a consensus
  tree - a function of its resolution and branch support values - can often be
  increased by removing rogue taxa. 
  'Rogue' provides an explicitly information-theoretic approach to rogue
  detection (Smith 2022) <doi:10.1093/sysbio/syab099>,
  and an interface to 'RogueNaRok' (Aberer et al. 2013)
  <doi:10.1093/sysbio/sys078>.
The R Optimization Infrastructure ('ROI') <doi:10.18637/jss.v094.i15>
	is a sophisticated framework for handling optimization problems in R.
	Additional information can be found on the 'ROI' homepage <https://roi.r-forge.r-project.org/>.
A collection of non-linear optimization problems with box bounds
             transformed into 'ROI' optimization problems.
             This package provides a wrapper around the 'globalOptTests'
             which provides a collection of global optimization problems.
             More information can be found in the 'README' file.
The mixed integer programming library 'MIPLIB' (see <http://miplib.zib.de/>) 
	is commonly used to compare the performance of mixed integer optimization solvers.
	This package provides functions to access 'MIPLIB' from the 
	'R' Optimization Infrastructure ('ROI'). More information about 'MIPLIB'
	can be found in the paper by Koch et al. available at
	<http://mpc.zib.de/index.php/MPC/article/viewFile/56/28>.
	The 'README.md' file illustrates how to use this package.
A collection of 'ROI' optimization problems based on the 'NETLIB-LP' collection.
  'Netlib' is a software repository, which amongst many other software for scientific computing contains a collection of linear programming problems. The purpose of this package is to make 
  this problems easily accessible from 'R' as 'ROI' optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'alabama' solver for solving nonlinear optimization problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the 'clarabel' solver for solving convex cone problems.
         More information about 'clarabel' can be found at
         <https://oxfordcontrol.github.io/ClarabelDocs/stable/>.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the 'CPLEX' commercial solver. It allows for solving mixed integer quadratically
	     constrained programming (MIQPQC) problems as well as all
	     variants/combinations of LP, QP, QCP, IP.
Enhances the R Optimization Infrastructure ('ROI') package
        	 with the 'DEoptim' and 'DEoptimR' package. 'DEoptim' is
        	 used for unconstrained optimization and 'DEoptimR' for
        	 constrained optimization.
Enhances the 'R' Optimization Infrastructure ('ROI') package
	     with the Embedded Conic Solver ('ECOS') for solving conic optimization problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package by registering
	     the free 'GLPK' solver. It allows for solving mixed integer linear programming ('MILP')
	     problems as well as all variants/combinations of 'LP', 'IP'.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the quadratic solver 'HiGHS'. More information about
         'HiGHS' can be found at <https://highs.dev>.
Enhances the 'R' Optimization Infrastructure ('ROI') package 
    by registering the 'ipop' solver from package 'kernlab'.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with the 'lp_solve' solver.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with the possibility to obtain multiple solutions for linear 
             problems with binary variables. The main function is copied 
             (with small modifications) from the relations package.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with a connection to the 'neos' server. 'ROI' optimization
             problems can be directly be sent to the 'neos' server
             and solution obtained in the typical 'ROI' style.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'NLopt' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'optimx' package.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the quadratic solver 'OSQP'. More information about
         'OSQP' can be found at <https://osqp.org>.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the quadratic solver 'qpOASES'. More information about
         'qpOASES' can be found at <https://github.com/coin-or/qpOASES>.
Enhances the R Optimization Infrastructure ('ROI') package
    by registering the 'quadprog' solver. It allows for solving quadratic programming (QP)
    problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the 'SCS' solver for solving convex cone problems.
Enhances the R Optimization Infrastructure ('ROI') package by registering
	     the 'SYMPHONY' open-source solver from the COIN-OR suite. It allows for
	     solving mixed integer linear programming (MILP) problems as well as all
	     variants/combinations of LP, IP.
Easily applying same t-tests/basic data description across several sub-groups, with the output as a nice arranged data.frame. Multiple comparison and the significance symbols are also provided.
Fast and efficient computation of rolling and expanding statistics for time-series data.
Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.
R Markdown output formats based on JavaScript libraries such as
    'Scrollama' (<https://github.com/russellgoldenberg/scrollama>) for storytelling.
A rolling version of the Latent Dirichlet Allocation, see Rieger et al. (2021) <doi:10.18653/v1/2021.findings-emnlp.201>. By a sequential approach, it enables the construction of LDA-based time series of topics that are consistent with previous states of LDA models. After an initial modeling, updates can be computed efficiently, allowing for real-time monitoring and detection of events or structural breaks.
Functions to perform propensity score matching on rolling entry interventions for which a suitable "entry" date is not observed for nonparticipants. For more details, please reference Witman et al. (2018) <https://onlinelibrary.wiley.com/doi/abs/10.1111/1475-6773.13086>.
Methods for fast rolling and expanding linear regression models. That is, series of linear regression models estimated on either an expanding window of data or a moving window of data. The methods use rank-one updates and downdates of the upper triangular matrix from a QR decomposition (see Dongarra, Moler, Bunch, and Stewart (1979) <doi:10.1137/1.9781611971811>).
Functions to convert an R colour specification to a colour name. The user can select and create different lists of colour names and different colour metrics for the conversion.
A colour list and colour metric based on the ISCC-NBS System of Color Designation for use with the 'roloc' package for converting colour specifications to colour names.
This R package connects to 'SWI'-'Prolog', <https://www.swi-prolog.org/>, so that R can send deterministic and non-deterministic queries to 'prolog' ('consult', 'query'/'submit', 'once', 'findall').
Implements the rank-ordered logit (RO-logit) model for stratified 
    analysis of continuous outcomes introduced by 
    Tan et al. (2017) <doi:10.1177/0962280217747309>. Model diagnostics based on 
    the heuristic residuals and estimates in linear scales are available from 
    the package, and outcomes with ties are supported.
Provides fast procedures for exploring all pairs of
    cutpoints of a single covariate with respect to survival and determining
    optimal cutpoints using a hierarchical method and various ordered logrank tests.
Estimates and plots as a heat map the rolling window wavelet correlation (RWWC) coefficients statistically significant (within the 95% CI) between two regular (evenly spaced) time series. 'RolWinWavCor' also plots at the same graphic the time series under study. The 'RolWinWavCor' was designed for financial time series, but this software can be used with other kinds of data (e.g., climatic, ecological, geological, etc). The functions contained in 'RolWinWavCor' are highly flexible since these contains some parameters to personalize the time series under analysis and the heat maps of the rolling window wavelet correlation coefficients. Moreover, we have also included a data set (named EU_stock_markets) that contains nine European stock market indices to exemplify the use of the functions contained in 'RolWinWavCor'. Methods derived from Polanco-Martínez et al (2018) <doi:10.1016/j.physa.2017.08.065>).
Load multiple movies, series, actors, directors etc from 'OMDB' API. More information in: <http://www.omdbapi.com/> .
Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. 'romic' takes advantage of these transformations to create interactive 'shiny' apps for exploratory data analysis such as an interactive heatmap.
R on FHIR is an easy to use wrapper around the 'HL7 FHIR' REST API (STU 3 and R4). It provides tools to easily read and search resources on a FHIR server and brings the results into the R environment. R on FHIR is based on the FhirClient of the official 'HL7 FHIR .NET API', also made by Firely.
An HTTP web server for R with a documented API to interface between R and the server. The documentation contains the Rook specification and details for building and running Rook applications. To get started, be sure and read the 'Rook' help file first.
Statistical distribution in OOP (Object Oriented Programming) way.
             This package proposes a R6 class interface to classic statistical
             distribution, and new distributions can be easily added with the
             class AbstractDist. A useful point is the generic fit() method for
             each class, which uses a maximum likelihood estimation to find the
             parameters of a dataset, see, e.g. Hastie, T. and al (2009)
             <isbn:978-0-387-84857-0>. Furthermore, the rv_histogram class
             gives a non-parametric fit, with the same accessors that for the
             classic distribution. Finally, three random generators useful to
             build synthetic data are given: a multivariate normal generator, an
             orthogonal matrix generator, and a symmetric positive definite
             matrix generator, see Mezzadri, F. (2007)
             <arXiv:math-ph/0609050>.
A set of tools to reconstruct ordered ontogenic trajectories from
    single cell RNAseq data.
Implementation of Taylor Regression Estimator (TRE), 
   Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE),
   Integration Extreme Finding Estimator (IEFE) and 
   Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve .     
   Christopoulos, DT (2019) <doi:10.13140/RG.2.2.17158.32324> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
   Christopoulos, DT (2016) <https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2014) <arXiv:1206.5478v2 [math.NA]> .
Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). 
  Includes routines that: (1) generate gradient and jacobian matrices (full and banded),
  (2) find roots of non-linear equations by the 'Newton-Raphson' method, 
  (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running,
  (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations
    by numerical differencing (using the method-of-lines approach).
  Includes fortran code.
Functions for hypothesis testing in single and double Wishart
    settings, based on Roy's largest root. This test statistic is especially
    useful in multivariate analysis. The computations are based on results by
    Chiani (2014) <DOI:10.1016/j.jmva.2014.04.002> and Chiani (2016)
    <DOI:10.1016/j.jmva.2015.10.007>. They use the fact that the CDF is related
    to the Pfaffian of a matrix that can be computed in a finite number of
    iterations. This package takes advantage of the Boost and Eigen C++ libraries
    to perform multi-precision linear algebra.
Selects one model with variable selection FDR controlled at a
    specified level. A q-value for each potential variable is also returned. The
    input, variable selection counts over many bootstraps for several levels of
    penalization, is modeled as coming from a beta-binomial mixture
    distribution.
The 'ropenblas' package (<https://prdm0.github.io/ropenblas/>) is useful for users of any 'GNU/Linux' distribution. It will be possible to download, compile and link the 'OpenBLAS' library (<https://www.openblas.net/>) with the R language, always by the same procedure, regardless of the 'GNU/Linux' distribution used. With the 'ropenblas' package it is possible to download, compile and link the latest version of the 'OpenBLAS' library even the repositories of the 'GNU/Linux' distribution used do not include the latest versions of 'OpenBLAS'. If of interest, older versions of the 'OpenBLAS' library may be considered. Linking R with an optimized version of 'BLAS' (<https://netlib.org/blas/>) may improve the computational performance of R code. The 'OpenBLAS' library is an optimized implementation of 'BLAS' that can be easily linked to R with the 'ropenblas' package.
Installs 'OpenCV' for use by other packages. 'OpenCV' <https://opencv.org/> 
    is library of programming functions mainly aimed at real-time computer 
    vision. This 'Lite' version installs the stable base version of 'OpenCV' and 
    some of its experimental externally contributed modules. It does not provide 
    R bindings directly.
'Rapid7' collects 'cybersecurity' data and makes it available via
    their 'Open Data' <http://opendata.rapid7.com> portal which has an API. Tools are
    provided to assist in querying for available data sets and downloading any
    data set authorized to a free, registered account.
Provides a client for the API of OpenDota. OpenDota is a web service which is provide DOTA2 real time data. Data is collected through the Steam WebAPI. With ROpenDota you can easily grab the latest DOTA2 statistics in R programming such as latest match on official international competition, analyzing your or enemy performance to learn their strategies,etc. Please see <https://github.com/rosdyana/ROpenDota> for more information.
Provide a simple interface to Bloomberg's OpenFIGI API. Please
    see <https://openfigi.com> for API details and registration. You may be
    eligible to have an API key to accelerate your loading process.
An R package for the OpenSecrets.org web services API.
OpenWeatherMap (OWM) <http://openweathermap.org/api> is a service providing weather related data.
           This package can be used to access current weather data for one location or several locations.
           It can also be used to forecast weather for 5 days with data for every 3 hours.
Provides string arithmetic, reassignment operators, logical operators
  that handle missing values, and extra logical operators such as floating point
  equality and all or nothing. The intent is to allow R users to write code that
  is easier to read, write, and maintain while providing a friendlier experience
  to new R users from other language backgrounds (such as 'Python') who are used
  to concepts such as x += 1 and 'foo' + 'bar'.
  Includes operators for not in, easy floating point comparisons, === equivalent, and SQL-like 
  like operations (), etc. 
  We also added in some extra helper functions, such as OS checks, pasting
  in Oxford comma format, and functions to get the first, last, nth, or most common 
  element of a vector or word in a string.
Reproducible, programmatic retrieval of datasets from the
    Roper Center data archive.  The Roper Center for Public Opinion
    Research <https://ropercenter.cornell.edu> maintains the largest 
    archive of public opinion data in existence, but researchers using
    these datasets are caught in a bind.  The Center's terms and conditions
    bar redistribution of downloaded datasets, but to ensure that one's 
    work can be reproduced, assessed, and built upon by others, one must
    provide access to the raw data one employed.  The `ropercenter`
    package cuts this knot by providing registered users with programmatic,
    reproducible access to Roper Center datasets from within R.
Read the data from Origin(R) project files ('*.opj')
	<https://www.originlab.com/doc/User-Guide/Origin-File-Types>.
	No write support is planned.
A suite of classes and methods which provide low-level support for modeling provenance between in-memory R objects. This is an infrastructure package and is not intended to be used directly by end-users.
Optimally robust estimation in general smoothly parameterized models using S4
            classes and methods.
Optimally robust estimation using S4 classes and methods. Old version still
            needed for current versions of ROptRegTS and RobRex.
Perform general purpose optimization in R using C++. A unified 
    wrapper interface is provided to call C functions of the five optimization 
    algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying 
    optim().
A general-purpose optimisation engine that supports 
	i) Monte Carlo optimisation with Metropolis criterion [Metropolis et al. (1953) <doi:10.1063/1.1699114>, Hastings (1970) <doi:10.1093/biomet/57.1.97>]
    and Acceptance Ratio Simulated Annealing [Kirkpatrick et al. (1983) <doi:10.1126/science.220.4598.671>, Černý (1985) <doi:10.1007/BF00940812>] 
	on multiple cores, and ii) Acceptance Ratio Replica Exchange Monte 
	Carlo Optimisation. In each case, the system pseudo-temperature is 
	dynamically adjusted such that the observed acceptance ratio is kept 
	near to the desired (fixed or changing) acceptance ratio.
Collection of tools to develop options strategies, value option contracts using the Black-Scholes-Merten option pricing model and calculate the option Greeks. Hull, John C. "Options, Futures, and Other Derivatives" (1997, ISBN:0-13-601589-1). Fischer Black, Myron Scholes (1973) "The Pricing of Options and Corporate Liabilities" <doi:10.1086/260062>.
Optimally robust estimation for regression-type models using S4 classes and
            methods.
Matrix reconstruction, also known as matrix completion, is the task of inferring missing entries of a partially observed matrix. This package provides a method called OptSpace, which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009) <doi:10.1109/ISIT.2009.5205567> for a case under low-rank assumption.
Oracle Database interface (DBI) driver for R.
   This is a DBI-compliant Oracle driver based on the OCI.
Client for the 'Orcid.org' API (<https://orcid.org/>).
    Functions included for searching for people, searching by 'DOI',
    and searching by 'Orcid' 'ID'.
Predicts morphological parameters of rorquals (e.g. body mass, 
    flipper length, maximum engulfment capacity) from body length using 
    allometric equations from Kahane-Rapport and Goldbogen (2018) 
    <doi:10.1002/jmor.20846>.
Functions to deal with binary classification
  problems in the presence of imbalanced classes. Synthetic balanced samples are  
  generated according to ROSE (Menardi and Torelli, 2013).  
  Functions that implement more traditional remedies to the class imbalance
  are also provided, as well as different metrics to evaluate a learner accuracy.
  These are estimated by holdout, bootstrap or cross-validation methods.
New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested
    and their performance accurately assessed. This requires densities
    that offer challenging properties to the novel sampling algorithms.
    One such popular problem is the Rosenbrock function. However, while its
    shape lends itself well to a benchmark problem, no codified multivariate expansion
    of the density exists. We have developed an extension to this class of distributions
    and supplied densities and direct sampler functions to assess the performance
    of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock 
    Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) <arXiv:1903.09556>.
When teaching statistics, it can often be desirable to
  uncouple the content from specific software packages. To ease such
  efforts, the Rosetta Stats website (<https://rosettastats.com>) allows
  comparing analyses in different packages. This package is the companion
  to the Rosetta Stats website, aiming to provide functions that produce
  output that is similar to output from other statistical packages, thereby
  facilitating 'software-agnostic' teaching of statistics.
'Rosette' is an API for multilingual text analysis and information
    extraction. More information can be found at <https://developer.rosette.com>.
Download and plot Open Street Map <https://www.openstreetmap.org/>,
    Bing Maps <https://www.bing.com/maps> and other tiled map sources. Use to create 
    basemaps quickly and add hillshade to vector-based maps.
Implementation of robust sparse PCA using the ROSPCA algorithm 
             of Hubert et al. (2016) <DOI:10.1080/00401706.2015.1093962>.
Implementation of the tests for rotational symmetry on the
    hypersphere proposed in García-Portugués, Paindaveine and Verdebout (2020)
    <doi:10.1080/01621459.2019.1665527>. The package also implements the
    proposed distributions on the hypersphere, based on the tangent-normal
    decomposition, and allows for the replication of the data application
    considered in the paper.
Fit and deploy rotation forest models ("Rodriguez, J.J., Kuncheva,
    L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans.
    Pattern Anal. Mach. Intell. 28, 1619-1630 <doi:10.1109/TPAMI.2006.211>") for binary classification.
    Rotation forest is an ensemble method where each base classifier (tree) is
    fit on the principal components of the variables of random partitions of
    the feature set.
Tools for working with rotational data, including
    simulation from the most commonly used distributions on SO(3),
    methods for different Bayes, mean and median type estimators for
    the central orientation of a sample, confidence/credible
    regions for the central orientation based on those estimators and
    a novel visualization technique for rotation data.  Most recently,
    functions to identify potentially discordant (outlying) values
    have been added.  References: Bingham, Melissa A. and Nordman, Dan J. and Vardeman, Steve B. (2009),
    Bingham, Melissa A and Vardeman, Stephen B and Nordman, Daniel J (2009),
    Bingham, Melissa A and Nordman, Daniel J and Vardeman, Stephen B (2010),
    Leon, C.A. and Masse, J.C. and Rivest, L.P. (2006),
    Hartley, R and Aftab, K and Trumpf, J. (2011),
    Stanfill, Bryan and Genschel, Ulrike and Hofmann, Heike (2013),
    Maonton, Jonathan (2004), 
    Mardia, KV and Jupp, PE (2000, ISBN:9780471953333), 
    Rancourt, D. and Rivest, L.P. and Asselin, J. (2000),
    Chang, Ted and Rivest, Louis-Paul (2001), 
    Fisher, Nicholas I. (1996, ISBN:0521568900).
A rotatogram is a method of displaying an association which is axis non-dominant. This is achieved in two ways: First, the method of estimating the slope and intercept uses the least-products method rather than more typical least squared error for the "dependent" variable. The least products method has no "dependent" variable and is scale independent. Second, the plot is rotated such that the resulting regression line is vertical, reducing the suggestion that the vertical axis is the dominant one. The slope can be read relative to either axis equally.
An interface to the 'Open Tree of Life' API to retrieve
    phylogenetic trees, information about studies used to assemble the
    synthetic tree, and utilities to match taxonomic names to 'Open Tree
    identifiers'. The 'Open Tree of Life' aims at assembling a
    comprehensive phylogenetic tree for all named species.
Conditionally rotate or back-up files based on
    their size or the date of the last backup; inspired by the 'Linux'
    utility 'logrotate'.
Visualize networks using the 'javascript' library 'roughjs'. This allows to draw sketchy, hand-drawn-like networks.
Implementations of algorithms for data analysis based on the
    rough set theory (RST) and the fuzzy rough set theory (FRST). We not only
    provide implementations for the basic concepts of RST and FRST but also
    popular algorithms that derive from those theories. The methods included in the
    package can be divided into several categories based on their functionality:
    discretization, feature selection, instance selection, rule induction and
    classification based on nearest neighbors. RST was introduced by Zdzisław
    Pawlak in 1982 as a sophisticated mathematical tool to model and process
    imprecise or incomplete information. By using the indiscernibility relation for
    objects/instances, RST does not require additional parameters to analyze the
    data. FRST is an extension of RST. The FRST combines concepts of vagueness and
    indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in
    1965) and RST.
Draw maps using the 'javascript' library 'roughjs'. This allows to draw sketchy, hand-drawn-like maps.
Decimal rounding is non-trivial in binary arithmetic.  ISO
  standard round to even is more rare than typically assumed as most decimal fractions
  are not exactly representable in binary.  Our roundX() versions explore differences
  between current and potential future versions of round() in R.
  Further, provides (some partly related) C99 math lib functions not in base R.
Visualize the objects in orbits in 2D and 3D. The packages is under developing to plot the orbits of objects in polar coordinate system. See the examples in demo.
R functions for generating and/or displaying random Chuck Norris 
  facts. Based on data from the 'Internet Chuck Norris database' ('ICNDb').
A simple rounding function. The default round() function in R
    uses the IEC 60559 standard and therefore it rounds 0.5 to 0 and rounds
    -1.5 to -2.  The roundx() function accounts for this and helps
    to round 0.5 up to 1.
Detecting outliers using robust methods, 
 	i.e. the Median Absolute Deviation (MAD) for univariate outliers; Leys, Ley, Klein, Bernard, & Licata (2013) <doi:10.1016/j.jesp.2013.03.013>
        and the Mahalanobis-Minimum Covariance Determinant (MMCD) for multivariate outliers; Leys, C., Klein, O., Dominicy, Y. & Ley, C. (2018) <doi:10.1016/j.jesp.2017.09.011>.
        There is also the more known but less robust Mahalanobis distance method, only for comparison purposes.
In order to make sure that web request ends up in the correct 
    handler function a router is often used. 'routr' is a package implementing a
    simple but powerful routing functionality for R based servers. It is a fully
    functional 'fiery' plugin, but can also be used with other 'httpuv' based
    servers.
Much as 'roxygen2' allows one to document functions in the same file as the function itself, 'roxut'  allows one to write the unit tests in the same file as the function.  Once processed, the unit tests are moved to the appropriate directory.  Currently supports 'testthat' and 'tinytest' frameworks. The 'roxygen2' package provides much of the infrastructure.
Generate your Rd documentation, 'NAMESPACE' file, and
    collation field using specially formatted comments. Writing
    documentation in-line with code makes it easier to keep your
    documentation up-to-date as your requirements change. 'roxygen2' is
    inspired by the 'Doxygen' system for C++.
Converts elements of 'roxygen' documentation to
    'markdown'.
Various tests as 'roxygen2' roclets: e.g. 'testthat' and 'tinytest' tests. 
  Also other static analysis tools as checking parameter documentation consistency and others.
Analysis of corneal data obtained from a Placido disk corneal topographer with calculation of irregularity indices. This package performs analyses of corneal data obtained from a Placido disk corneal topographer, with the calculation of the Placido irregularity indices and the posterior analysis. The package is intended to be easy to use by a practitioner, providing a simple interface and yielding easily interpretable results. A corneal topographer is an ophthalmic clinical device that obtains measurements in the cornea (the anterior part of the eye). A Placido disk corneal topographer makes use of the Placido disk [Rowsey et al. (1981)]<doi:10.1001/archopht.1981.03930011093022>, which produce a circular pattern of measurement nodes. The raw information measured by such a topographer is used by practitioners to analyze curvatures, to study optical aberrations, or to diagnose specific conditions of the eye (e.g. keratoconus, an important corneal disease). The rPACI package allows the calculation of the corneal irregularity indices described in [Castro-Luna et al. (2020)]<doi:10.1016%2Fj.clae.2019.12.006>, [Ramos-Lopez et al. (2013)]<doi:10.1097%2FOPX.0b013e3182843f2a>, and [Ramos-Lopez et al. (2011)]<doi:10.1097/opx.0b013e3182279ff8>. It provides a simple interface to read corneal topography data files as exported by a typical Placido disk topographer, to compute the irregularity indices mentioned before, and to display summary plots that are easy to interpret for a clinician.
Packed bar charts are a variation of treemaps for visualizing skewed data.  The concept was introduced by Xan Gregg at 'JMP'.
Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016) <doi:10.1007/978-3-319-32562-0>. This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.
'PADRINO' houses textual representations of
    Integral Projection Models which can be converted from their 
    table format into full kernels to reproduce or extend an 
    already published analysis. 'Rpadrino' is an R interface to this database. For
    more information on Integral Projection Models, see Easterling et al. (2000) 
  <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) 
  <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>,
  and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. See Levin et al. (2021)
  for more information on 'ipmr', the engine that powers model reconstruction
  <doi:10.1111/2041-210X.13683>.
A part of precision agriculture is linked to the spectral image obtained from the cameras. With the image information of the agricultural experiment, the included functions facilitate the collection of spectral data associated with the experimental units. Some designs generated in R are linked to the images, which allows the use of the information of each pixel of the image in the experimental unit and the treatment. Tables and images are generated for the analysis of the precision agriculture experiment during the entire vegetative period of the crop.
'PaleoClim' <http://www.paleoclim.org> (Brown et al. 2019, 
  <doi:10.1038/sdata.2018.254>) is a set of free, high resolution paleoclimate 
  surfaces covering the whole globe. It includes data on surface temperature,
  precipitation and the standard bioclimatic variables commonly used in 
  ecological modelling, derived from the 'HadCM3' general circulation model and 
  downscaled to a spatial resolution of up to 2.5 minutes. Simulations are
  available for key time periods from the Late Holocene to mid-Pliocene. Data on
  current and Last Glacial Maximum climate is derived from 'CHELSA'  (Karger et 
  al. 2017, <doi:10.1038/sdata.2017.122>) and reprocessed by 'PaleoClim' to 
  match their format; it is available at up to 30 seconds resolution. This 
  package provides a simple interface for downloading 'PaleoClim' data in R, 
  with support for caching and filtering retrieved data by period, resolution,
  and geographic extent.
A set of functions to build simple 
        GUI controls for R functions.  These are built on the 'tcltk' package. 
        Uses could include changing a parameter on a graph by animating it 
        with a slider or a "doublebutton", up to more sophisticated control 
        panels.
        Some functions for specific graphical tasks, referred to as 'cartoons',
        are provided.
Download and merge labeled single-cell RNA-seq data from the PanglaoDB <https://panglaodb.se/> into a Seurat object.
Recursive partitioning for classification, 
  regression and survival trees.  An implementation of most of the 
  functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Recursive partitioning for least absolute deviation regression trees. Another algorithm from the 1984 book by 
             Breiman, Friedman, Olshen and Stone in addition to the 'rpart' package (Breiman, Friedman, Olshen, Stone (1984, ISBN:9780412048418).
Plot 'rpart' models. Extends plot.rpart() and text.rpart()
             in the 'rpart' package.
Recursive partitioning methods to build
        classification trees for ordinal responses within the CART
        framework. Trees are grown using the Generalized Gini
        impurity function, where the misclassification costs are given
        by the absolute or squared differences in scores assigned to
        the categories of the response. Pruning is based on the total
        misclassification rate or on the total misclassification cost.
Fit and simulate any kind of
    physiologically-based kinetic ('PBK') models whatever the number of compartments.
    Moreover, it allows to account for any link between pairs of compartments, as
    well as any link of each of the compartments with the external medium. Such
    generic PBK models have today applications in pharmacology (PBPK models) to
    describe drug effects, in toxicology and ecotoxicology (PBTK models) to describe
    chemical substance effects. In case of exposure to a parent compound (drug or
    chemical) the 'rPBK' package allows to consider metabolites, whatever their number
    and their phase (I, II, ...). Last but not least, package 'rPBK' can also be used for
    dynamic flux balance analysis (dFBA) to deal with metabolic networks. See also
    Charles et al. (2022) <doi:10.1101/2022.04.29.490045>.
Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.
Monthly Pacific Decadal Oscillation (PDO) index
    values from January 1900 to September 2018. 
    Superseded by 'rsoi' package which includes the historical and 
    most recent monthly PDO index values together with related climate indices.
The quantitative measurement and detection of molecules in HPLC should be carried out by an accurate description of chromatographic peaks. In this package non-linear fitting using a modified Gaussian model with a parabolic variance (PVMG) has been implemented to obtain the retention time and height at the peak maximum. This package also includes the traditional Van Deemter approach and two alternatives approaches to characterize chromatographic column.
Implements the fast iterative shrinkage-thresholding algorithm
             (FISTA) algorithm to fit a Gamma distribution with an elastic net 
             penalty as described in Chen, Arakvin and Martin (2018) 
             <arxiv:1804.07780>. An implementation for the case of the 
             exponential distribution is also available, with details available 
             in Chen and Martin (2018) <https://papers.ssrn.com/abstract_id=3085672>.
Computes the influence functions time series of the returns for the risk and 
             performance measures as mentioned in Chen and Martin (2018) 
             <https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019)
             <https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence
             functions at a set of parameter values and plots them to display the shapes of 
             the influence functions.
Implements the methodology of "Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959--1035". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.
Estimates of standard errors of popular risk and performance 
             measures for asset or portfolio returns using methods as described in 
             Chen and Martin (2021) <doi:10.21314/JOR.2020.446>.
This reduced piecewise exponential survival software implements the likelihood ratio test and backward elimination procedure in Han, Schell, and Kim (2012 <doi:10.1080/19466315.2012.698945>, 2014 <doi:10.1002/sim.5915>), and Han et al. (2016 <doi:10.1111/biom.12590>). Inputs to the program can be either times when events/censoring occur or the vectors of total time on test and the number of events. Outputs of the programs are times and the corresponding p-values in the backward elimination. Details about the model and implementation are given in Han et al. 2014. This program can run in R version 3.2.2 and above.
The purpose of this package is to factor out logic
    and math common to Item Factor Analysis fitting, diagnostics, and
    analysis. It is envisioned as core support code suitable for more
    specialized IRT packages to build upon. Complete access to optimized C
    functions are made available with R_RegisterCCallable().
    This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.
An enhanced version of the semi-empirical, spatially distributed
    emission and transport model PhosFate implemented in 'R' and 'C++'. It
    currently supports suspended solids (SS) and particulate phosphorus (PP). A
    major feature is the allocation of substance loads entering surface waters
    to their sources of origin, which is a basic requirement for the
    identification of critical source areas and in consequence a cost-effective
    implementation of mitigation measures. References: Hepp et al. (2022)
    <doi:10.1016/j.jenvman.2022.114514>; Hepp and Zessner (2019)
    <doi:10.3390/w11102161>; Kovacs (2013)
    <http://hdl.handle.net/20.500.12708/9468>.
Tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data (Goolsby et al. 2017). Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.
Work with the PhyloPic Web Service (<http://api-docs.phylopic.org/v2/>) 
    to fetch silhouette images of organisms. Includes functions for adding 
    silhouettes to both base R plots and ggplot2 plots.
Get information (boards, pins and
    users) from the Pinterest <http://www.pinterest.com>
    API.
Build powerful pivot tables (aka Pivot Grid, Pivot Chart, Cross-Tab)
    and dynamically slice & dice / drag 'n' drop your data. 'rpivotTable' is a
    wrapper of 'pivottable', a powerful open-source Pivot Table library implemented
    in 'JavaScript' by Nicolas Kruchten. Aligned to 'pivottable' v2.19.0.
Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary:
  (1) The graph parameters file, which name is passed to the function 'rplotengine()'.
      The user can specify the titles, choose the type of the graph, graph output formats
      (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend,
      whether to show or not a grid at the background, etc.
  (2) The data to be plotted, which name is specified as a parameter ('data_filename')
      in the previous file. This data file has a tabulated format, with a single character
      (e.g. tab) between each column. Optionally, the file could include data columns for
      showing confidence intervals.
A robust Partial Least-Squares (PLS) method is implemented that is robust to outliers in the residuals as well as to leverage points. A specific weighting scheme is applied which avoids iterations, and leads to a highly efficient robust PLS estimator.
An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for 210Pb-dated deposits using prior information. It can combine 210Pb, radiocarbon, and other dates in the chronologies. See Aquino et al. (2018) <doi:10.1007/s13253-018-0328-7>. Note that parts of the code underlying 'rplum' are derived from the 'rbacon' package by the same authors, and there remains a degree of overlap between the two packages.
Statistical estimation of revealed preference models from data collected on bipartite matchings. The models are for matchings within a bipartite population where individuals have utility for people based on known and unknown characteristics. People can form a partnership or remain unpartnered. The model represents both the availability of potential partners of different types and preferences of individuals for such people. The software estimates preference parameters based on sample survey data on partnerships and population composition. The simulation of matchings and goodness-of-fit are considered.  See Goyal, Handcock, Jackson, Rendall and Yeung (2022) <doi:10.1093/jrsssa/qnad031>.
Really Poor Man's Graphical User Interface, used to create interactive R analysis sessions with simple R commands.
Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures.  
    This is a model-based clustering algorithm that returns a hierarchy
    of classes, similar to hierarchical clustering, but also similar to
    finite mixture models.
Implements the P-model 
  (Stocker et al., 2020 <doi:10.5194/gmd-13-1545-2020>),
  predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis,
  assimilation, and dark respiration rates as a function of the environment
  (temperature, CO2, vapour pressure deficit, light, atmospheric pressure).
Functions to allow users to build and analyze design consistent 
    tree and random forest models using survey data from a complex sample 
    design.  The tree model algorithm can fit a linear model to survey data 
    in each node obtained by recursively partitioning the data.  The splitting 
    variables and selected splits are obtained using a randomized permutation 
    test procedure which adjusted for complex sample design features used to 
    obtain the data. Likewise the model fitting algorithm produces 
    design-consistent coefficients to any specified least squares linear model 
    between the dependent and independent variables used in the end nodes.
    The main functions return the resulting binary tree or random forest as 
    an object of "rpms" or "rpms_forest" type. The package also provides methods
    modeling a "boosted" tree or forest model and a tree model for zero-inflated
    data as well as a number of functions and methods available for use with 
    these object types.
A set of functions to analyze and print the development of a
    commodity using the Point and Figure (P&F) approach. A P&F processor can be used
    to calculate daily statistics for the time series. These statistics can be used
    for deeper investigations as well as to create plots. Plots can be generated as
    well known X/O Plots in plain text format, and additionally in a more graphical
    format.
Wrapper for the 'PoetryDB' API <http://poetrydb.org> that allows for interaction and data extraction from the 
  database in an R interface. The 'PoetryDB' API is a database of poetry and poets implemented with 'MongoDB' to 
  enable developers and poets to easily access one of the most comprehensive poetry databases currently available.
A polyhedra database scraped from various sources as R6 objects and 'rgl' visualizing capabilities.
PDF tools based on the Poppler PDF rendering library.
  See <http://poppler.freedesktop.org/> for more information on Poppler.
Collection of tools to calculate portfolio performance metrics. Portfolio performance is a key measure for investors. These metrics are important to analyse how effectively their money has been invested. This package uses portfolio theories to give investor tools to evaluate their portfolio performance. For more information see,  Markowitz, H.M. (1952), <doi:10.2307/2975974>. Analysis of Investments & Management of Portfolios [2012, ISBN:978-8131518748].
Provides an interface between R and 'PostGIS'-enabled
    'PostgreSQL' databases to transparently transfer spatial
    data. Both vector (points, lines, polygons) and raster data are
    supported in read and write modes. Also provides convenience
    functions to execute common procedures in 'PostgreSQL/PostGIS'.
Fully DBI-compliant Rcpp-backed interface to PostgreSQL
    <https://www.postgresql.org/>, an open-source relational database.
Database interface and 'PostgreSQL' driver for 'R'.
 This package provides a Database Interface 'DBI' compliant 
 driver for 'R' to access 'PostgreSQL' database systems.  
 In order to build and install this package from source, 'PostgreSQL' 
 itself must be present your system to provide 'PostgreSQL' functionality 
 via its libraries and header files. These files are provided as
 'postgresql-devel' package under some Linux distributions.
 On 'macOS' and 'Microsoft Windows' system the attached 'libpq' library source will be used.
The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.
Using some association schemes to obtain a new series of resolvable partially pairwise balanced designs (RPPBD) and space-filling designs.
Reads in sample description and slide description files and
        annotates the expression values taken from GenePix results files
	(text file format used by many microarray scanner and software providers). 
	After normalization data can be visualized as boxplot, heatmap or dotplot.
Provides tools for the analysis of reverse-phase protein arrays (RPPAs), which are also known as "tissue lysate arrays" or simply "lysate arrays". The package's primary purpose is to input a set of quantification files representing dilution series of samples and control points taken from scanned RPPA slides and determine a relative log concentration value for each valid dilution series present in each slide and provide graphical visualization of the input and output data and their relationships. Other optional features include generation of quality control scores for judging the quality of the input data, spatial adjustment of sample points based on controls added to the slides, and various types of normalization of calculated values across a set of slides. The package was derived from a previous package named SuperCurve. For a detailed description of data inputs and outputs, usage  information, and a list of related papers describing methods used in the package please review the vignette "Guide_to_RPPASPACE". Hu (2007) <doi:10.1093/bioinformatics/btm283>.
Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then augmented to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly.
Read, write and manipulate 'Praat' TextGrid, PitchTier, Pitch, IntensityTier, Formant, Sound, and Collection files <https://www.fon.hum.uva.nl/praat/>.
Wrapper to retrieve market data, explore available markets, and plot historical price data from the 'PredictIt' public API (<https://www.predictit.org/api/marketdata/all/>).
    The package comes with a demo 'shiny' application for illustrating example use cases.
    License to use data made available via the API is for non-commercial use and 'PredictIt' is the sole source of such data.
Routines to select and visualize the maxima for a given strict
    partial order. This especially includes the computation of the Pareto
    frontier, also known as (Top-k) Skyline operator (see Börzsönyi, et al. 
    (2001) <doi:10.1109/ICDE.2001.914855>), and some generalizations 
    known as database preferences (see Kießling (2002) 
    <doi:10.1016/B978-155860869-6/50035-4>).
Implements a 'DBI' compliant interface to Presto. Presto is
    an open source distributed SQL query engine for running interactive
    analytic queries against data sources of all sizes ranging from
    gigabytes to petabytes: <https://prestodb.io/>.
Estimates disease prevalence for a given index date using existing
    registry data extended with Monte Carlo simulations following the method of Crouch et al (2014) <doi: 10.1016/j.canep.2014.02.005>.
'Eprime' is a set of programs for administering
    psychological experiments by computer. This package provides functions
    for loading, parsing, filtering and exporting data in the text files
    produced by 'Eprime' experiments.
Provides a set of functions to facilitate building formatted strings
    under various replacement rules: C-style formatting, variable-based formatting,
    and number-based formatting. C-style formatting is basically identical to built-in
    function 'sprintf'. Variable-based formatting allows users to put variable names
    in a formatted string which will be replaced by variable values. Number-based
    formatting allows users to use index numbers to represent the corresponding
    argument value to appear in the string.
Bayes estimation of probit choice models, both in the cross-sectional and 
    panel setting. The package can analyze binary, multivariate, ordered, and 
    ranked choices, and places a special focus on modeling heterogeneity of 
    choice behavior among deciders. The main functionality includes model 
    fitting via Markov chain Monte Carlo methods, tools for convergence 
    diagnostic, choice data simulation, in-sample and out-of-sample choice 
    prediction, and model selection using information criteria and Bayes 
    factors. The latent class model extension facilitates preference-based 
    decider classification, where the number of latent classes can be inferred 
    via the Dirichlet process or a weight-based updating scheme. This allows for 
    flexible modeling of choice behavior without the need to impose structural 
    constraints. For a reference on the method see Oelschlaeger and Bauer (2021) 
    <https://trid.trb.org/view/1759753>.
The A() function calculates the A statistic, a nonparametric
    measure of effect size for two independent groups that’s also known as
    the probability of superiority (Ruscio, 2008), along with its standard
    error and a confidence interval constructed using bootstrap methods 
    (Ruscio & Mullen, 2012). Optional arguments can be specified to 
    calculate variants of the A statistic developed for other research 
    designs (e.g., related samples, more than two independent groups or 
    related samples; Ruscio & Gera, 2013). 
    <DOI: 10.1037/1082-989X.13.1.19>.
    <DOI: 10.1080/00273171.2012.658329>.
    <DOI: 10.1080/00273171.2012.738184>.
Performs all steps in the credit scoring process. This package allows the user to follow all the necessary steps for building an effective scorecard. It provides the user functions for coarse binning of variables, Weights of Evidence (WOE) transformation, variable clustering, custom binning, visualization, and scaling of logistic regression coefficients. The results will generate a scorecard that can be used as an effective credit scoring tool to evaluate risk. For complete details on the credit scoring process, see Siddiqi (2005, ISBN:047175451X).
Robust, reliable and flexible paths to files below
    a project root. The 'root' of a project is defined as a directory that
    matches a certain criterion, e.g., it contains a certain regular file.
Use JSON templates to create folders and files structure for data science projects. Includes customized templates and accepts your own as JSON files.
Protocol Buffers are a way of encoding structured data in an
 efficient yet extensible format. Google uses Protocol Buffers for almost all
 of its internal 'RPC' protocols and file formats.  Additional documentation
 is available in two included vignettes one of which corresponds to our 'JSS'
 paper (2016, <doi:10.18637/jss.v071.i02>. A sufficiently recent version of
 'Protocol Buffers' library is required; currently version 3.3.0 from 2017
 is the stated minimum.
Implementation of the relative placement algorithm widely used in the scoring of Lindy Hop and West Coast Swing dance contests.
Implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.
Performs goodness of fits tests for both high and low-dimensional linear models.
    It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity.
    In addition one can test the significance of potentially large groups of variables, and also
    produce p-values for the significance of individual variables in high-dimensional linear
    regression.
Estimating repeatability (intra-class
    correlation) from Gaussian, binary, proportion and Poisson data.
Client for accessing data journalism APIs from ProPublica <http://www.propublica.org/>.
An R interface to the Pushbullet messaging service which
 provides fast and efficient notifications (and file transfer) between
 computers, phones and tablets.  An account has to be registered at the 
 site <https://www.pushbullet.com> site to obtain a (free) API key.
Provides portable access from 'R' to biomedical image processing toolbox 
    'ANTs' by Avants et al. (2009) <doi:10.54294/uvnhin> via seamless
    integration with the 'Python' implementation 'ANTsPy'. Allows biomedical 
    images to be processed in 'Python' and analyzed in 'R', and vice versa 
    via shared memory. See 'citation("rpyANTs")' for more reference information.
Provides access to ArcGIS geoprocessing tools by building an 
             interface between R and the ArcPy Python side-package via the 
             reticulate package.
Aims to create a single isolated 'Miniconda' 
    and 'Python' environment for reproducible pipeline scripts. 
    The package provides utilities to run system command within the 'conda'
    environment, making it easy to install, launch, manage, and stop 
    'Jupyter-lab'.
Constructs various robust quality control charts based on the median or Hodges-Lehmann estimator (location) and the median absolute deviation (MAD) or Shamos estimator (scale). The estimators used for the robust control charts are all unbiased with a sample of finite size. For more details, see Park, Kim and Wang (2022) <doi:10.1080/03610918.2019.1699114>. In addition, using this R package, the conventional quality control charts such as X-bar, S, R, p, np, u, c, g, h, and t charts are also easily constructed. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C1091319).
Implements the 'rquery' piped Codd-style query algebra using 'data.table'.  This allows
   for a high-speed in memory implementation of Codd-style data manipulation tools.
It computes the Schmidt decomposition of bipartite quantum systems, discrete or continuous,
    and their respective entanglement metrics. See Artur Ekert, Peter L. Knight (1995) <doi:10.1119/1.17904> 
    for more details.
Performs penalized quantile regression with LASSO, elastic net, SCAD and MCP penalty functions including group penalties. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC. Below URL provides a link to a work in progress vignette.
Hypocenter estimation and analysis of seismic data collected continuously, or in trigger mode. The functions organize other functions from RSEIS and GEOmap to help researchers pick, locate, and store hypocenters for detailed seismic investigation.
The 'RQuantLib' package makes parts of 'QuantLib' accessible from R
 The 'QuantLib' project aims to provide a comprehensive software framework
 for quantitative finance. The goal is to provide a standard open source library
 for quantitative analysis, modeling, trading, and risk management of financial
 assets.
An implementation of the QUEFTS (Quantitative Evaluation of the Native Fertility of Tropical Soils) model. The model (1) estimates native nutrient (N, P, K) supply of soils from a few soil chemical properties; and (2) computes crop yield given that supply, crop parameters, fertilizer application, and crop attainable yield. See Janssen et al. (1990) <doi:10.1016/0016-7061(90)90021-Z> for the technical details and Sattari et al. (2014) <doi:10.1016/j.fcr.2013.12.005> for a recent evaluation and improvements.
A piped query generator based on Edgar F. Codd's relational
    algebra, and on production experience using 'SQL' and 'dplyr' at big data
    scale.  The design represents an attempt to make 'SQL' more teachable by
    denoting composition by a sequential pipeline notation instead of nested
    queries or functions.   The implementation delivers reliable high 
    performance data processing on large data systems such as 'Spark',
    databases, and 'data.table'. Package features include: data processing trees
    or pipelines as observable objects (able to report both columns
    produced and columns used), optimized 'SQL' generation as an explicit
    user visible table modeling step, plus explicit query reasoning and checking.
Enables researchers to conduct multivariate statistical analyses
    of survey data with randomized response technique items from several designs,
    including mirrored question, forced question, and unrelated question. This
    includes regression with the randomized response as the outcome and logistic
    regression with the randomized response item as a predictor. In addition,
    tools for conducting power analysis for designing randomized response items
    are included. The package implements methods described in Blair, Imai, and Zhou
    (2015) ''Design and Analysis of the Randomized Response Technique,'' Journal
    of the American Statistical Association 
    <https://graemeblair.com/papers/randresp.pdf>.
Three methods to calculate R2 for models with correlated errors, 
    including Phylogenetic GLS, Phylogenetic Logistic Regression, Linear Mixed 
    Models (LMMs), and Generalized Linear Mixed Models (GLMMs). See details in 
    Ives 2018 <doi:10.1093/sysbio/syy060>.
Analyzes and predicts from matrix population models (Caswell 2006) <doi:10.1002/9781118445112.stat07481>.
A single method implementing multiple approaches to generate pseudo-random vectors whose components sum up to one (see, e.g., Maziero (2015) <doi:10.1007/s13538-015-0337-8>). The components of such vectors can for example be used for weighting objectives when reducing multi-objective optimisation problems to a single-objective problem in the socalled weighted sum scalarisation approach.
An implementation of the Real-time Adaptive Penalization (RAP) algorithm through which to iteratively update a regularization parameter in a streaming context.
The minimal 'rrapply'-package contains a single function rrapply(), providing an extended implementation of 'R'-base rapply() by allowing to recursively apply a function to elements of a nested list based on a general condition function and including the possibility to prune or aggregate nested list elements from the result. In addition, special arguments can be supplied to access the name, location, parents and siblings in the nested list of the element under evaluation. The rrapply() function builds upon rapply()'s native 'C' implementation and requires no other package dependencies.
Implementation of Robust Regression tailored to deal with Asymmetric noise Distribution, which was originally proposed by Takeuchi & Bengio & Kanamori (2002) <doi:10.1162/08997660260293300>. In addition, this implementation is extended as introducing potential feature regularization by LASSO etc.
Replication Rate (RR) is the probability of replicating a statistically significant association in genome-wide association studies. This R-package provide the estimation method for replication rate which makes use of the summary statistics from the primary study. We can use the estimated RR to determine the sample size of the replication study, and to check the consistency between the results of the primary study and those of the replication study.
A tool to exchange data between R and 'Raven' sound analysis software (Cornell Lab of Ornithology). Functions work on data formats compatible with the R package 'warbleR'.
Software for genomic prediction with the RR-BLUP mixed model (Endelman 2011, <doi:10.3835/plantgenome2011.08.0024>). One application is to estimate marker effects by ridge regression; alternatively, BLUPs can be calculated based on an additive relationship matrix or a Gaussian kernel.
An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065> (to appear in Computational Statistics and Data Science). It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <doi:10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <doi:10.1016/j.csda.2007.11.006>).
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point:
        principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>),
        linear and quadratic discriminant analysis (Todorov and Pires (2007)),
        multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>),
        outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).
        See also Todorov and Filzmoser (2009) <urn:isbn:978-3838108148>,
        Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and
        Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.
Provides methods for multiway data analysis by means of Parafac
    and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions
    for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (2018) <doi:10.1080/02664763.2017.1381669>).
    Several optimization methods alternative to ALS are available 
    (Simonacci and Gallo (2019) <doi:10.1016/j.chemolab.2019.103822>, Simonacci and Gallo (2020) <doi:10.1007/s00500-019-04320-9>).
Robust multivariate methods for high dimensional data including
        outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), 
        robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), 
        robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), 
        and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point for Incomplete
        Data (missing values) (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).
Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.
'IUCN' Red List (<http://apiv3.iucnredlist.org/api/v3/docs>) client.
    The 'IUCN' Red List is a global list of threatened and endangered species.
    Functions cover all of the Red List 'API' routes. An 'API' key is required.
Superclasses 'PostgreSQL' connection to help enable full 'dplyr' functionality on 'Redshift'.
'OpenRefine' (formerly 'Google Refine') is a popular, open source data cleaning software. This package enables users to programmatically trigger data transfer between R and 'OpenRefine'. Available functionality includes project import, export and deletion.
Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.
An R and Repast integration tool for running individual-based
    (IbM) simulation models developed using 'Repast Simphony' Agent-Based framework
    directly from R code supporting multicore execution. This package 
    integrates 'Repast Simphony' models within R environment, making easier 
    the tasks of running and analyzing model output data for 
    automated parameter calibration and for carrying out uncertainty and
    sensitivity analysis using the power of R environment.
Functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.
Feature Selection with Regularized Random Forest. This
    package is based on the 'randomForest' package by Andy Liaw.
    The key difference is the RRF() function that builds a
    regularized random forest. Fortran original by Leo Breiman 
    and Adele Cutler, R port by Andy Liaw and Matthew Wiener, 
    Regularized random forest for classification by Houtao Deng, 
    Regularized random forest for regression by Xin Guan.
    Reference: Houtao Deng (2013) <arXiv:1306.0237>.
Testing and inference for regression models using residual randomization methods. The basis of inference is an invariance assumption on the regression errors, e.g., clustered errors, or doubly-clustered errors.
Collection of functions for fitting distributions to given data or
    by known quantiles. Two main functions fit.perc() and fit.cont() provide
    users a GUI that allows to choose a most appropriate distribution without
    any knowledge of the R syntax. Note, this package is a part of the 'rrisk'
    project.
We implement full-ranked, rank-penalized, and adaptive nuclear norm penalized estimation methods using multivariate mixture models proposed by Kang, Chen, and Yao (2022+).
Fit the reduced-rank multinomial logistic regression model for Markov
    chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio
    (2021)<doi:10.1002/sim.8923> in R. It combines the ideas of multinomial
    logistic regression in Markov chains and reduced-rank. It is very useful in 
    a study where multi-states model is assumed and each transition among the 
    states is controlled by a series of covariates. The key advantage is to 
    reduce the number of parameters to be estimated. The final coefficients for 
    all the covariates and the p-values for the interested covariates will be 
    reported. The p-values for the whole coefficient matrix can be calculated by 
    two bootstrap methods.
Functions for creating and manipulating RNA secondary structure plots.
Calculate RNNI distance between and manipulate with ranked trees. RNNI stands for Ranked Nearest Neighbour Interchange and is an extension of the classical NNI space (space of trees created by the NNI moves) to ranked trees, where internal nodes are ordered according to their heights (usually assumed to be times). The RNNI distance takes the tree topology into account, as standard NNI does, but also penalizes changes in the order of internal nodes, i.e. changes in the order of times of evolutionary events. For more information about the RNNI space see: Gavryushkin et al. (2018) <doi:10.1007/s00285-017-1167-9>, Collienne & Gavryushkin (2021) <doi:10.1007/s00285-021-01567-5>, Collienne et al. (2021) <doi:10.1007/s00285-021-01685-0>, and Collienne (2021) <http://hdl.handle.net/10523/12606>.
Computation of the International Roughness Index (IRI) given a
    longitudinal road profile. The IRI can be calculated for a single road segment
    or for a sequence of segments with a fixed length (e. g. 100m). For the latter,
    an overlap of the segments can be selected. The IRI and likewise the algorithms
    for its determination are defined in Sayers, Michael W; Gillespie, Thomas D;
    Queiroz, Cesar A.V. 1986. The International Road Roughness Experiment (IRRE) :
    establishing correlation and a calibration standard for measurements. World
    Bank technical paper; no. WTP 45. Washington, DC : The World Bank. (ISBN
    0-8213-0589-1) available from <http://documents.worldbank.org/curated/en/326081468740204115>.
Multivariate regression methodologies including
    classical reduced-rank regression (RRR)
    studied by Anderson (1951) <doi:10.1214/aoms/1177729580> and
    Reinsel and Velu (1998) <doi:10.1007/978-1-4757-2853-8>,
    reduced-rank regression via adaptive nuclear norm penalization
    proposed by Chen et al. (2013) <doi:10.1093/biomet/ast036> and
    Mukherjee et al. (2015) <doi:10.1093/biomet/asx080>,
    robust reduced-rank regression (R4) proposed by
    She and Chen (2017) <doi:10.1093/biomet/asx032>,
    generalized/mixed-response reduced-rank regression (mRRR) proposed by
    Luo et al. (2018) <doi:10.1016/j.jmva.2018.04.011>,
    row-sparse reduced-rank regression (SRRR) proposed by
    Chen and Huang (2012) <doi:10.1080/01621459.2012.734178>,
    reduced-rank regression with a sparse singular value decomposition (RSSVD)
    proposed by Chen et al. (2012) <doi:10.1111/j.1467-9868.2011.01002.x>
    and sparse and orthogonal factor regression (SOFAR) proposed by
    Uematsu et al. (2019) <doi:10.1109/TIT.2019.2909889>.
Functions for phylogenetic analysis (Castiglione et al., 2018 <doi:10.1111/2041-210X.12954>). The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, the computation of ontogenetic shape vectors and test for morphological convergence.
Linear model calculations are made for many random versions of data.  
    Using residual randomization in a permutation procedure, sums of squares are 
    calculated over many permutations to generate empirical probability distributions 
    for evaluating model effects.  This packaged is described by 
    Collyer & Adams (2018).  Additionally, coefficients, statistics, fitted values, and residuals generated over many 
    permutations can be used for various procedures including pairwise tests, prediction, classification, and
    model comparison.  This package should provide most tools one could need for the analysis of
    high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.
Reduced-rank regression, diagnostics and graphics.
Univariate and multivariate methods to analyze randomized response 
    (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A 
    survey technique for eliminating evasive answer bias. Journal of the 
    American Statistical Association, 60, 63–69, <doi:10.2307/2283137>). 
    Besides univariate estimates of true proportions, RR variables can be used 
    for correlations, as dependent variable in a logistic regression (with or 
    without random effects), or as predictors in a linear regression
    (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and 
    regression analyses of randomized response data. Journal of Statistical 
    Software, 85(2), 1–29, <doi:10.18637/jss.v085.i02>). For simulations and 
    the estimation of statistical power, RR data can be generated according to 
    several models. The implemented methods also allow to test the link between 
    continuous covariates and dishonesty in cheating paradigms such as the 
    coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). 
    The statistical analysis of cheating paradigms. Behavior Research Methods, 
    49, 724–732, <doi:10.3758/s13428-016-0729-x>).
Methods for estimating online robust reduced-rank regression. 
    The Gaussian maximum likelihood estimation method is described in Johansen, S. (1991) <doi:10.2307/2938278>.
    The majorisation-minimisation estimation method is partly described in Zhao, Z., & Palomar, D. P. (2017) <doi:10.1109/GlobalSIP.2017.8309093>.
    The description of the generic stochastic successive upper-bound minimisation method
    and the sample average approximation can be found in Razaviyayn, M., Sanjabi, M., & Luo, Z. Q. (2016) <doi:10.1007/s10107-016-1021-7>.
Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2020.1741379>.
Makes documents containing plots and tables from a table of R codes. 
    Can make "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents with or without R code.
    In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications.
Point and interval estimation of linear parameters with data
    obtained from complex surveys (including stratified and clustered samples)
    when randomization techniques are used. The randomized response technique
    was developed to obtain estimates that are more valid when studying
    sensitive topics. Estimators and variances for 14 randomized response
    methods for qualitative variables and 7 randomized response methods for
    quantitative variables are also implemented. In addition, some data sets
    from surveys with these randomization methods are included in the package.
Implementation of Gibbs sampling algorithm for Bayesian Estimation
    of the Reduced Reparameterized Unified Model ('rrum'), described by 
    Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.
Advanced response surface analysis. The main function RSA computes
    and compares several nested polynomial regression models (full second- or 
    third-order polynomial, shifted and rotated squared difference model, 
    rising ridge surfaces, basic squared difference model, asymmetric or 
    level-dependent congruence effect models). The package provides plotting 
    functions for 3d wireframe surfaces, interactive 3d plots, and contour plots. 
    Calculates many surface parameters (a1 to a5, principal axes, stationary point,
    eigenvalues) and provides standard, robust, or bootstrapped standard errors
    and confidence intervals for them.
The package contains all the data sets related to the book
        written by the maintainer of the package.
Empirical best linear unbiased prediction (EBLUP) and
    robust prediction of the area-level means under the basic unit-level
    model. The model can be fitted by maximum likelihood or a (robust)
    M-estimator. Mean square prediction error is computed by a parametric
    bootstrap.
Provides a model agnostic tool for white-box model trained on features extracted from a black-box model. For more information see: Gosiewska et al. (2020) <doi:10.1016/j.dss.2021.113556>.
Provides access to geocomputing and terrain analysis functions
    of the geographical information system (GIS) 'SAGA' (System for Automated
    Geoscientific Analyses) from within R by running the command line version of
    SAGA. This package furthermore provides several R functions for handling ASCII
    grids, including a flexible framework for applying local functions (including
    predict methods of fitted models) and focal functions to multiple grids. SAGA
    GIS is available under GPL-2 / LGPL-2 licences from 
    <https://sourceforge.net/projects/saga-gis/>.
Provides an R scripting interface to the open-source 'SAGA-GIS' 
    (System for Automated Geoscientific Analyses Geographical Information
    System) software. 'Rsagacmd' dynamically generates R functions for every
    'SAGA-GIS' geoprocessing tool based on the user's currently installed
    'SAGA-GIS' version. These functions are contained within an S3 object
    and are accessed as a named list of libraries and tools. This structure
    facilitates an easier scripting experience by organizing the large number
    of 'SAGA-GIS' geoprocessing tools (>700) by their respective library.
    Interactive scripting can fully take advantage of code autocompletion tools
    (e.g. in 'Rstudio'), allowing for each tools syntax to be quickly
    recognized. Furthermore, the most common types of spatial data (via the
    'terra', 'sp', and 'sf' packages) along with non-spatial data are
    automatically passed from R to the 'SAGA-GIS' command line tool for
    geoprocessing operations, and the results are loaded as the appropriate R
    object. Outputs from individual 'SAGA-GIS' tools can also be chained using
    pipes from the 'magrittr' and 'dplyr' packages to combine complex
    geoprocessing operations together in a single statement. 'SAGA-GIS' is
    available under a GPLv2 / LGPLv2 licence from
    <https://sourceforge.net/projects/saga-gis/> including Windows x86/x64
    and macOS binaries. SAGA-GIS is also included in Debian/Ubuntu default software
    repositories. Rsagacmd has currently been tested on 'SAGA-GIS' versions
    from 2.3.1 to 9.0.1 on Windows, Linux and macOS.
Assists in processing reflectance data, developing empirical models using stepwise regression and a generalized linear modeling approach, cross-
    validation, and analysis of trends in water quality conditions (specifically chl-a) and climate conditions using the Theil-Sen estimator.
Classes and functions to create and summarize different types
    of resampling objects (e.g. bootstrap, cross-validation).
Resampling Stats (http://www.resample.com) is an add-in for
    running randomization tests in Excel worksheets. The workflow is (1) to define
    a statistic of interest that can be calculated from a data table, (2) to
    randomize rows ad/or columns of a data table to simulate a null hypothesis
    and (3) and to score the value of the statistic from many randomizations. The
    relative frequency distribution of the statistic in the simulations is then
    used to infer the probability of the observed value be generated by the null
    process (probability of Type I error). This package intends to translate this
    logic for R for teaching purposes. Keeping the original workflow is favored over
    performance.
Implements the Gibbs sampling algorithm to randomly sample association rules with one pre-chosen item as the consequent from a transaction dataset. The Gibbs sampling algorithm was proposed in G. Qian, C.R. Rao, X. Sun and Y. Wu (2016) <DOI:10.1073/pnas.1604553113>.
'SaTScan'(TM) <https://www.satscan.org> is software for finding regions in 
    Time, Space, or Time-Space that have excess risk, based on scan statistics, and 
	uses Monte Carlo hypothesis testing to generate P-values for these regions.  The 
	'rsatscan' package provides functions for writing R data frames in 
	'SaTScan'-readable formats, for setting 'SaTScan' parameters, for running 'SaTScan' in 
	the OS, and for reading the files that 'SaTScan' creates.
Retrieve, update, delete job information from <https://saucelabs.com/>. Poll the 'SauceLabs' services
    current status and access supported platforms. Send and retrieve files from 'SauceLabs' and manage tunnels associated
    with 'SauceConnect'.
Package to Handle R Requests from R Service Bus Applications with JSON Payloads in a generic way.
    The incoming request is encoded as a string (character vector of length one) containing 
    the JSON file passed through by the client.
Provides an R interface to the Registry of Standard Biological Parts API maintained by the iGEM Foundation: <https://igem.org/Main_Page>. Facilitates retrieval of
    the part number, authorship, date of entry, url, short description, type, and sequence following the guidelines set forth at <http://parts.igem.org/Registry_API/Guidelines>. 
    All Registry content falls under Creative Commons Attribution-ShareAlike: <https://creativecommons.org/licenses/by-sa/4.0/>.
Performs robust and sparse correlation matrix estimation. Robustness is achieved based on a simple robust pairwise correlation estimator, while sparsity is obtained based on thresholding. The optimal thresholding is tuned via cross-validation. See Serra, Coretto, Fratello and Tagliaferri (2018) <doi:10.1093/bioinformatics/btx642>.
A statistical tool for multivariate modeling and clustering using stepwise cluster analysis. The modeling output of rSCA is constructed as a cluster tree to represent the complicated relationships between multiple dependent and independent variables. A free tool (named rSCA Tree Generator) for visualizing the cluster tree from rSCA is also released and it can be downloaded at <https://rscatree.weebly.com/>.
'Scala' <https://www.scala-lang.org/> is embedded in 'R' and callbacks from 'Scala' to 'R' are available. Support is provided to write 'R' packages that access 'Scala'. After installation, please run 'rscala::scalaConfig()'.  The vignette provides an update of the original paper <doi:10.18637/jss.v092.i04>.
As an advanced approach to computerized adaptive testing (CAT), 
  shadow testing (van der Linden(2005) <doi:10.1007/0-387-29054-0>) dynamically 
  assembles entire shadow tests as a part of 
  selecting items throughout the testing process.
  Selecting items from shadow tests guarantees the compliance of all content 
  constraints defined by the blueprint. 'RSCAT' is an R package for the 
  shadow-test approach to CAT. The objective of 
  'RSCAT' is twofold: 1) Enhancing the effectiveness of shadow-test CAT simulation;
  2) Contributing to the academic and scientific community for CAT research.
  RSCAT is currently designed for dichotomous items based on the three-parameter logistic (3PL) model.
Evaluates R source codes by variable and/or functions names. Similar source codes should deliver similarity coefficients near one. Since neither the frequency nor the order of the used names is considered, a manual inspection of the R source code is required to check for similarity. Possible use cases include detection of code clones for improving
    software quality and of plagiarism amongst students' assignments.
Scelestial infers a lineage tree from single-cell DNA mutation matrix. 
	It generates a tree with approximately maximum parsimony through 
	a Steiner tree approximation algorithm.
Client for Rserve, allowing to connect to Rserve instances and issue commands.
Provides a tool for mass deployment of shiny apps to 'RStudio Connect' or 'Shiny Server'. Multiple user accounts and servers can be configured for deployment.
Programmatic deployment interface for 'RPubs', 'shinyapps.io', and
    'Posit Connect'. Supported content types include R Markdown documents,
    Shiny applications, Plumber APIs, plots, and static web content.
Provides a generic implementation of the 'RStudio' connection contract to 
    make it easier for database connections, and other type of connections, opened 
    via R packages integrate with the connections pane inside the 'RStudio' interactive
    development environment (IDE).
Uses Elsevier 'Scopus' API
    <https://dev.elsevier.com/sc_apis.html> to download 
    information about authors and their citations.
A method to download Department of Education College
     Scorecard data using the public API
     <https://collegescorecard.ed.gov/data/documentation/>. It is based on
     the 'dplyr' model of piped commands to select and filter data in a
     single chained function call.  An API key from the U.S. Department of
     Education is required.
Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for  a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.
Ray Shooting Depth functions are provided for bivariate analysis. This mainly includes functions for computing the bivariate depth as well as RS median. Drawing functions for depth bags are also provided.
This is a sudoku game package with a shiny application for playing .
Set of classes and methods to read data and metadata documents
  exchanged through the Statistical Data and Metadata Exchange (SDMX) framework,
  currently focusing on the SDMX XML standard format (SDMX-ML).
Response surface designs with neighbour effects are suitable for experimental situations 
            where it is expected that the treatment combination administered to one experimental unit 
            may affect the response on neighboring units as well as the response on the unit to which it is applied. 
            Integrating these effects in the response surface model improves the experiment's precision 
            (Jaggi, S., Sarika and Sharma, V.K. (2010)<http://krishi.icar.gov.in/jspui/handle/123456789/4364>; 
            Verma A., Jaggi S., Varghese, E.,Varghese, C.,Bhowmik, A., Datta, A. and Hemavathi M. (2021)<DOI: 10.1080/03610918.2021.1890123>). 
            This package includes sym(), asym1(), asym2() functions that generates response surface designs which are rotatable under a 
            polynomial model of a given order without interaction term incorporating neighbour effects.
A Bayesian-weighted estimator and two unweighted estimators are
 developed to estimate the number of newly found rare species in additional
 ecological samples. Among these methods, the Bayesian-weighted estimator
 and an unweighted (Chao-derived) estimator are of high accuracy and
 recommended for practical applications.
 Technical details of the proposed estimators have been well described
 in the following paper: Shen TJ, Chen YH (2018) A Bayesian
 weighted approach to predicting the number of newly discovered
 rare species. Conservation Biology, In press.
SEA performs simultaneous feature-set testing for (gen)omics data. It tests the unified null hypothesis controls the family-wise error rate for all possible pathways. The unified null hypothesis is defined as: "The proportion of true features in the set is less than or equal to the threshold c", where c is selected by the user. Family-wise error rate control is provided through use of closed testing with Simes test. For more information on closed testing with Simes see Goeman et al. (2019) <doi:10.1093/biomet/asz041> and for more information about the properties and performance of SEA procedure see Ebrahimpoor et al. (2019) <doi:10.1093/bib/bbz074>.
Estimate the percentage of seeds in a seedlot that contain stacks
    of genetically modified traits.  Estimates are calculated using a
    multinomial group testing model with maximum likelihood estimation of the
    parameters.
Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms.  Includes general time-series tools, plotting, filtering, interactive display.
Provides a set of R bindings for the 'Selenium 2.0 WebDriver'
    (see <https://www.selenium.dev/documentation/>
    for more information) using the 'JsonWireProtocol' (see
    <https://github.com/SeleniumHQ/selenium/wiki/JsonWireProtocol> for more
    information). 'Selenium 2.0 WebDriver' allows driving a web browser
    natively as a user would either locally or on a remote machine using
    the Selenium server it marks a leap forward in terms of web browser
    automation. Selenium automates web browsers (commonly referred to as
    browsers). Using RSelenium you can automate browsers locally or
    remotely.
A robust procedure is implemented to estimate means and covariance matrix of multiple variables with missing data using Huber weight and then to estimate a structural equation model.
Analyses sentiment of a sentence in English and assigns score to it. It can classify sentences to the following categories of sentiments:- Positive, Negative, very Positive, very negative, 
              Neutral. For a vector of sentences, it counts the number of sentences in each
              category of sentiment.In calculating the score, negation and various degrees
              of adjectives are taken into consideration. It deals only with English sentences.
Rserve acts as a socket server (TCP/IP or local sockets) 
	     which allows binary requests to be sent to R. Every
	     connection has a separate workspace and working
	     directory. Client-side implementations are available
	     for popular languages such as C/C++ and Java, allowing
	     any application to use facilities of R without the need of
	     linking to R code. Rserve supports remote connection,
	     user authentication and file transfer. A simple R client
	     is included in this package as well.
An R implementation for the Strain Elevation and
    Tension embedding algorithm from Bourne (2020)
    <doi:10.1007/s41109-020-00329-4>. The package embeds graphs and
    networks using the Strain Elevation and Tension embedding (SETSe)
    algorithm. SETSe represents the network as a physical system, where
    edges are elastic, and nodes exert a force either up or down based on
    node features. SETSe positions the nodes vertically such that the
    tension in the edges of a node is equal and opposite to the force it
    exerts for all nodes in the network. The resultant structure can then
    be analysed by looking at the node elevation and the edge strain and
    tension. This algorithm works on weighted and unweighted networks as
    well as networks with or without explicit node features.  Edge
    elasticity can be created from existing edge weights or kept as a
    constant.
A report of statistical findings (RSF) project template is
    generated using a 'bookdown' format. 'YAML' fields can be further
    customized. Additional helper functions provide extra features to the
    RSF.
Slow Feature Analysis (SFA), ported to R based on
    'matlab' implementations of SFA: 'SFA toolkit' 1.0 by Pietro Berkes and 'SFA toolkit'
    2.8 by Wolfgang Konen.
This is a collection of functions designed for simulating, estimating and forecasting seasonal functional autoregressive time series of order one. These methods are addressed in the manuscript: <https://www.monash.edu/business/ebs/research/publications/ebs/wp16-2019.pdf>.
Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: <http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: <http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: <http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: <https://cmc.leeds.ac.uk/>.
Contains a variety of functions, based around
    regime shift analysis of paleoecological data.
    Citations:
    Rodionov() from Rodionov (2004) <doi:10.1029/2004GL019448>
    Lanzante() from Lanzante (1996) <doi:10.1002/(SICI)1097-0088(199611)16:11%3C1197::AID-JOC89%3E3.0.CO;2-L>  
    Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680)
    rolling_autoc from Liu, Gao & Wang (2018) <doi:10.1016/j.scitotenv.2018.06.276>
    Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) <doi:10.1126/science.1090795>.
The main purpose of this package is to perform simulation-based
   estimation of stochastic actor-oriented models for longitudinal network
   data collected as panel data. Dependent variables can be single or
   multivariate networks, which can be directed, non-directed, or two-mode;
   and associated actor variables.
   There are also functions for testing parameters and checking goodness of fit.
   An overview of these models is given in Tom A.B. Snijders (2017), Stochastic
   Actor-Oriented Models for Network Dynamics, Annual Review of Statistics and
   Its Application, 4, 343-363 <doi: 10.1146/annurev-statistics-060116-054035>.
   An extensive manual, scripts, and much further information is at the Siena
   website <http://www.stats.ox.ac.uk/~snijders/siena/>.
Provides a way to transform an image into a mosaic composed from a set of smaller images (tiles). It also contains a simple function for creating the tiles from a folder of images directly through R, without the need of any external code. At this moment only the JPEG format is supported, either as input (image and tiles) or output (mosaic transformed image).
Summarise results from simulation studies and compute Monte Carlo
  standard errors of commonly used summary statistics. This package is modelled 
  on the 'simsum' user-written command in 'Stata' (White I.R., 2010 
  <https://www.stata-journal.com/article.html?article=st0200>), further extending
  it with additional performance measures and functionality.
Easy-to-use functions for downloading air quality data from the 
    Mexican National Air Quality Information System (SINAICA).  Allows you to 
    query pollution and meteorological parameters from more than a hundred
    monitoring stations located throughout Mexico. See <https://sinaica.inecc.gob.mx> 
    for more information.
Makes  operations with raster images, such as map
    viewing in time series, export values in time series for specific, total or limited
    within a polygon locations. Makes data processing of remote sensing of climatic variables distributed in the space (maps 2D) and the time (time series).
Functions for interacting with the Adobe Analytics API V1.4
    (<https://api.omniture.com/admin/1.4/rest/>).
Provides functions and examples for testing hypothesis about the population mean and variance on samples drawn by r-size biased sampling schemes.
This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.
Create custom keyboard shortcuts to examine code selected in the 'Rstudio' editor.
             F3 can for example yield 'str(selection)' and F7 open the source
             code of CRAN and base package functions on 'github'.
A toolbox for sleep data processing, visualization and analysis. Tools for state of the art automatic sleep stages scoring.
Implements the "Stemming Algorithm for the Portuguese Language" <DOI:10.1109/SPIRE.2001.10024>.
Functions that simplify submitting R scripts to a 'Slurm' 
    workload manager, in part by automating the division of embarrassingly
    parallel calculations across cluster nodes.
Provides functions to generate response-surface designs, 
    fit first- and second-order response-surface models, 
    make surface plots, obtain the path of steepest ascent, 
    and do canonical analysis. A good reference on these methods 
    is Chapter 10 of Wu, C-F J and Hamada, M (2009) 
    "Experiments: Planning, Analysis, and Parameter Design Optimization"
    ISBN 978-0-471-69946-0. An early version of the package is
    documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.
We provide functions to perform an empirical small telescopes
    analysis. This package contains 2 functions, SmallTelescopes() and 
    EstimatePower(). Users only need to call SmallTelescopes() to conduct 
    the analysis. For more information on small telescopes analysis see
    Uri Simonsohn (2015) <doi:10.1177/0956797614567341>.
Aims at loading Facebook and Instagram advertising data from
    'Smartly.io' into R. 'Smartly.io' is an online advertising service that enables
    advertisers to display commercial ads on social media networks (see <http://www.smartly.io/> for more information).
    The package offers an interface to query the 'Smartly.io' API and loads data directly into R for further data processing and data analysis.
A small package for calculating the matrices in Shiller (1991, <doi:10.1016/S1051-1377(05)80028-2>) that serve as the foundation for many repeat-sales price indexes.
Provides methods for model building and model evaluation of mixed effects models using 
    'Monolix' <https://monolix.lixoft.com>. 'Monolix' is a software tool for nonlinear mixed effects 
    modeling that must have been installed in order to use 'Rsmlx'. 
    Among other tasks, 'Rsmlx' provides a powerful tool for automatic PK model building, performs 
    statistical tests for model assessment, bootstrap simulation and likelihood profiling for 
    computing confidence intervals. 'Rsmlx' also proposes several automatic covariate search methods for 
    mixed effects models.
The Snell scoring procedure, implemented in R. This procedure was first described by E.J Snell (1964) <doi:10.2307/2528498> and was later used by Tong et al (1977) <doi:10.4141/cjas77-001> in dairy.
The Stuttgart Neural Network Simulator (SNNS) is a library
    containing many standard implementations of neural networks. This
    package wraps the SNNS functionality to make it available from
    within R. Using the 'RSNNS' low-level interface, all of the
    algorithmic functionality and flexibility of SNNS can be accessed.
    Furthermore, the package contains a convenient high-level
    interface, so that the most common neural network topologies and
    learning algorithms integrate seamlessly into R.
A programmatic interface to various 'SNP' 'datasets'
    on the web: 'OpenSNP' (<https://opensnp.org>), and 'NBCIs' 'dbSNP' database
    (<https://www.ncbi.nlm.nih.gov/projects/SNP/>). Functions
    are included for searching for 'NCBI'. For 'OpenSNP', functions are included 
    for getting 'SNPs', and data for 'genotypes', 'phenotypes', annotations, 
    and bulk downloads of data by user.
Provides easier interaction with
    'Socrata' open data portals <https://dev.socrata.com>.
    Users can provide a 'Socrata' data set resource URL,
    or a 'Socrata' Open Data API (SoDA) web query,
    or a 'Socrata' "human-friendly" URL,
    returns an R data frame. Converts dates to 'POSIX'
    format and manages throttling by 'Socrata'.
    Users can upload data to 'Socrata' portals directly
    from R.
Downloads Southern Oscillation Index, Oceanic Nino
    Index, North Pacific Gyre Oscillation data, North Atlantic Oscillation
    and Arctic Oscillation. Data sources are described in the help files for each function.
General Non-linear Optimization Using Augmented Lagrange Multiplier Method.
A comprehensive R API for querying Apache Solr databases.
             A Solr core is represented as a data frame or list that
             supports Solr-side filtering, sorting,
             transformation and aggregation, all through the familiar
             base R API. Queries are processed
             lazily, i.e., a query is only sent to the database when
             the data are required.
Somoclu is a massively parallel implementation of self-organizing maps.  It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . Details refer to (Peter Wittek, et al (2017)) <doi:10.18637/jss.v078.i09>.
Toolbox with 'shiny' applications for widely used psychometric
    methods. Those methods include following analysis: Item analysis, item response theory calibration, principal component analysis,
    confirmatory factor analysis - structural equation modeling, generating simulated data.
    References:
    Chalmers (2012, <doi:10.18637/jss.v048.i06>);
    Revelle (2022, <https://CRAN.R-project.org/package=psych Version = 2.2.9.>);
    Rosseel (2012, <doi:10.18637/jss.v048.i02>);
    Magis & Raiche (2012, <doi:10.18637/jss.v048.i08>);
    Magis & Barrada (2017, <doi:10.18637/jss.v076.c01>).
Minimally adjust the values of numerical records in a data.frame, such
    that each record satisfies a predefined set of equality and/or inequality
    constraints. The constraints can be defined using the 'validate' package. 
    The core algorithms have recently been moved to the 'lintools' package,
    refer to 'lintools' for a more basic interface and access to a version
    of the algorithm that works with sparse matrices.
An extension package for 'sparklyr' that provides an R interface to
    H2O Sparkling Water machine learning library (see <https://github.com/h2oai/sparkling-water> for more information).
Implements many algorithms for statistical learning on 
  sparse matrices - matrix factorizations, matrix completion, 
  elastic net regressions, factorization machines. 
  Also 'rsparse' enhances 'Matrix' package by providing methods for 
  multithreaded <sparse, dense> matrix products and native slicing of 
  the sparse matrices in Compressed Sparse Row (CSR) format.
  List of the algorithms for regression problems:
  1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) 
  Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>)
  2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>)
  List of algorithms for matrix factorization and matrix completion:
  1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least 
  Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>)
  2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro 
  (2005, <doi:10.1145/1102351.1102441>)
  3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, 
  Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder 
  et al. (2014, <arXiv:1410.2596>)
  4) Linear-Flow matrix factorization, from 'Practical linear models for 
  large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al 
  (2016, ISBN:978-1-57735-770-4)
  5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, 
  Socher, Manning (2014, <https://aclanthology.org/D14-1162/>)
  Package is reasonably fast and memory efficient - it allows to work with large
  datasets - millions of rows and millions of columns. This is particularly useful 
  for practitioners working on recommender systems.
Implementation of Nelson rules for control charts in 'R'. The 'Rspc' implements some Statistical Process Control methods, namely Levey-Jennings type of I (individuals) chart, Shewhart C (count) chart and Nelson rules (as described in Montgomery, D. C. (2013) Introduction to statistical quality control. Hoboken, NJ: Wiley.). Typical workflow is taking the time series, specify the control limits, and list of Nelson rules you want to evaluate. There are several options how to modify the rules (one sided limits, numerical parameters of rules, etc.). Package is also capable of calculating the control limits from the data (so far only for i-chart and c-chart are implemented).
Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.48550/arXiv.2209.04670> for the covariance-based method and in Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.
R interface to the 'Spectra' library
    <https://spectralib.org/> for large-scale eigenvalue and SVD
    problems. It is typically used to compute a few
    eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues,
    which is usually more efficient than eigen() if k << n. This package
    provides the 'eigs()' function that does the similar job as in 'Matlab',
    'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function
    to calculate the largest k singular values and corresponding
    singular vectors of a real matrix. The matrix to be computed on can be
    dense, sparse, or in the form of an operator defined by the user.
Implements the network clustering algorithm described in 
            Newman (2006) <doi:10.1103/PhysRevE.74.036104>. The complete 
            iterative algorithm comprises of two steps. In the first step, the 
            network is expressed in terms of its leading eigenvalue and 
            eigenvector and recursively partition into two communities. 
            Partitioning occurs if the maximum positive 
            eigenvalue is greater than the tolerance (10e-5) for the current 
            partition, and if it results in a positive contribution to the 
            Modularity.
            Given an initial separation using the leading eigen step, 'rSpectral' 
            then continues to maximise for the change in Modularity using a 
            fine-tuning step - or variate thereof. The first stage here is to 
            find the node which, when moved from one community to another, 
            gives the maximum change in Modularity. This node’s community is 
            then fixed and we repeat the process until all nodes have been moved. 
            The whole process is repeated from this new state until the change 
            in the Modularity, between the new and old state, is less than the 
            predefined tolerance.
            A slight variant of the fine-tuning step, which can improve speed 
            of the calculation, is also provided. Instead of moving each node 
            into each community in turn, we only consider moves of neighbouring 
            nodes, found in different communities, to the community of the 
            current node of interest. The two steps process is repeatedly 
            applied to each new community found, subdivided each community 
            into two new communities, until we are unable to find any division 
            that results in a positive change in Modularity.
Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors.
    Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx).
    Fully vectorized code, with warnings/errors for Euler angles (singularity, out of range, invalid angle order), 
    DCM (orthogonality, not proper, exceeded tolerance to unity determinant) and Euler vectors(not unity).
    Also quaternion and other useful functions.
    Based on SpinCalc by John Fuller and SpinConv by Paolo de Leva.
Implementation of various spirometry equations 
    in R, currently the GLI-2012 (Global Lung Initiative; 
    Quanjer et al. 2012 <doi:10.1183/09031936.00080312>) and the 
    NHANES3 (National Health and Nutrition Examination Survey; 
    Hankinson et al. 1999 <doi:10.1164/ajrccm.159.1.9712108>) 
    equations. 
    Contains user-friendly functions to calculate predicted 
    and LLN (Lower Limit of Normal) values for different 
    spirometric parameters such as FEV1 (Forced Expiratory 
    Volume in 1 second), FVC (Forced Vital Capacity), etc, 
    and to convert absolute spirometry measurements 
    to percent (%) predicted and z-scores.
Enables binary package installations on Linux distributions.
    Provides access to 'RStudio' public repositories at
    <https://packagemanager.rstudio.com>, and transparent management of
    system requirements without administrative privileges. Currently supported
    distributions are 'CentOS' / 'RHEL' 7-9, and several 'RHEL' derivatives
    ('Rocky Linux', 'AlmaLinux', 'Oracle Linux', and 'Amazon Linux' 2),
    'openSUSE' / 'SLES' 15.3-4, and 'Ubuntu' 18.04, 20.04 and 22.04.
Provides an interface to the Spotify API <https://developer.spotify.com/documentation/web-api/>.
An implementation of functionalities to transform directed graphs that are bound to a set of
  known forbidden paths. There are several transformations, following the rules provided by Villeneuve 
  and Desaulniers (2005) <doi: 10.1016/j.ejor.2004.01.032>, and Hsu et al. (2009) <doi: 10.1007/978-3-642-03095-6_60>. 
  The resulting graph is generated in a data-frame format. See rsppfp website for more information, 
  documentation an examples.
The SPRITE algorithm creates possible distributions of discrete responses
    based on reported sample parameters, such as mean, standard deviation and range 
    (Heathers et al., 2018, <doi:10.7287/peerj.preprints.26968v1>). This package implements it, 
    drawing heavily on the code for Nick Brown's 'rSPRITE' Shiny app <https://shiny.ieis.tue.nl/sprite/>. 
    In addition, it supports the modeling of distributions based on multi-item (Likert-type) 
    scales and the use of restrictions on the frequency of particular responses.
Calculate generalized R-squared, partial R-squared, and partial correlation coefficients for generalized linear (mixed) models (including quasi models with well defined variance functions).
Allows the user to generate and execute select, insert, update and delete 'SQL' queries the underlying database without having to explicitly write 'SQL' code.
Embeds the SQLite database engine in R and provides an
    interface compliant with the DBI package. The source for the SQLite
    engine and for various extensions in a recent version is included.
    System libraries will never be consulted because this package relies
    on static linking for the plugins it includes; this also ensures a
    consistent experience across all installations.
A comprehensive tool written in R Shiny to explore, manage and update SQLite Databases.
Parser for 'SQL' statements. Currently, it supports parsing of only 'SELECT' statements.
We provide an implementation for Sum of Ranking Differences (SRD), 
    a novel statistical test introduced by Héberger (2010) 
    <doi:10.1016/j.trac.2009.09.009>. The test allows the comparison of 
    different solutions through a reference by first performing a rank 
    transformation on the input, then calculating and comparing the distances 
    between the solutions and the reference - the latter is measured in the 
    L1 norm. The reference can be an external benchmark (e.g. an established 
    gold standard) or can be aggregated from the data. The calculated distances, 
    called SRD scores, are validated in two ways, see Héberger and Kollár-Hunek 
    (2011) <doi:10.1002/cem.1320>. A randomization test (also called permutation 
    test) compares the SRD scores of the solutions to the SRD scores of randomly 
    generated rankings. The second validation option is cross-validation that 
    checks whether the rankings generated from the solutions come from the same 
    distribution or not. For a detailed analysis about the cross-validation 
    process see Sziklai, Baranyi and Héberger (2021) <arXiv:2105.11939>. The 
    package offers a wide array of features related to SRD including the computation 
    of the SRD scores, validation options, input preprocessing and plotting tools.
Methods and tools for Singular Spectrum Analysis including decomposition,
             forecasting and gap-filling for univariate and multivariate time series.
             General description of the methods with many examples can be found in the book
             Golyandina (2018, <doi:10.1007/978-3-662-57380-8>).
             See 'citation("Rssa")' for details.
Ranked set sampling (RSS) is introduced as an advanced method for data collection which is substantial for the statistical and methodological analysis in scientific studies by McIntyre (1952) (reprinted in 2005) <doi:10.1198/000313005X54180>. This package introduces the first package  that implements the RSS and its modified versions for sampling. With 'RSSampling', the researchers can sample with basic RSS and the modified versions, namely, Median RSS, Extreme RSS, Percentile RSS, Balanced groups RSS, Double RSS, L-RSS, Truncation-based RSS, Robust extreme RSS. The 'RSSampling' also allows imperfect ranking using an auxiliary variable (concomitant) which is widely used in the real life applications. Applicants can also use this package for parametric and nonparametric inference such as mean, median and variance estimation, regression analysis and some distribution-free tests where the the samples are obtained via basic RSS.
Provides useful tools which supplement the use of 'Simulx' software and 'R' connectors ('Monolix Suite'). 'Simulx' is an easy, efficient and flexible application for clinical trial simulations. You need 'Simulx' software to be installed in order to use 'RsSimulx' package. Among others tasks, 'RsSimulx' provides the same functions as package 'mlxR' does with a compatibility with 'Simulx' software.
A collection of implementations of semi-supervised classifiers
    and methods to evaluate their performance. The package includes implementations
    of, among others, Implicitly Constrained Learning, Moment Constrained Learning,
    the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic
    Likelihood estimation, S4VM and WellSVM.
Reservoir Systems Standard Operation Policy. A system for simulation of supply reservoirs. It proposes functionalities for plotting and evaluation of supply reservoirs systems.
Testing the equality of two means using Ranked Set Sampling
             and Median Ranked Set Sampling are provided under normal distribution. Data generation functions are also given RSS and MRSS. 
             Also, data generation functions are given under imperfect ranking data for Ranked Set Sampling and Median Ranked Set Sampling.
             Ozdemir Y.A., Ebegil M., & Gokpinar F. (2019), <doi:10.1007/s40995-018-0558-0>
             Ozdemir Y.A., Ebegil M., & Gokpinar F. (2017), <doi:10.1080/03610918.2016.1263736>.
Provides functions to access, search and download spacetime earth
    observation data via SpatioTemporal Asset Catalog (STAC). This package 
    supports the version 1.0.0 (and older) of the STAC specification
    (<https://github.com/radiantearth/stac-spec>). 
    For further details see Simoes et al. (2021) <doi:10.1109/IGARSS47720.2021.9553518>.
An extremely simple stack data type, implemented with 'R6'
    classes. The size of the stack increases as needed, and the amortized
    time complexity is O(1). The stack may contain arbitrary objects.
Provides fast, persistent (side-effect-free) stack, queue and
    deque (double-ended-queue) data structures. While deques include a superset
    of functionality provided by queues, in these implementations queues are
    more efficient in some specialized situations. See the documentation for
    rstack, rdeque, and rpqueue for details.
User-facing R functions are provided to parse, compile, test,
    estimate, and analyze Stan models by accessing the header-only Stan library
    provided by the 'StanHeaders' package. The Stan project develops a probabilistic
    programming language that implements full Bayesian statistical inference
    via Markov Chain Monte Carlo, rough Bayesian inference via 'variational'
    approximation, and (optionally penalized) maximum likelihood estimation via
    optimization. In all three cases, automatic differentiation is used to quickly
    and accurately evaluate gradients without burdening the user with the need to
    derive the partial derivatives.
Estimates previously compiled regression models using the 'rstan'
    package, which provides the R interface to the Stan C++ library for Bayesian
    estimation. Users specify models via the customary R syntax with a formula and
    data.frame plus some additional arguments for priors.
Perform sigmoidal Emax model fit using 'Stan' in a formula notation, without writing 'Stan' model code.
Provides various tools for developers of R packages interfacing
    with 'Stan' <https://mc-stan.org>, including functions to set up the required
    package structure, S3 generics and default methods to unify function naming
    across 'Stan'-based R packages, and vignettes with recommendations for
    developers.
A simple R -> Stata interface allowing the user to
    execute Stata commands (both inline and from a .do file)
    from R.
Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, 
    for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. 
    The output of each test is automatically transformed into a tidy data frame to facilitate visualization. 
    Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix.  
    Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs 
    (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. 
    It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and 
    'Cramer V' for the association between categorical variables. 
    The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.
R interface for china national data <http://data.stats.gov.cn/>, 
    some convenient functions for accessing the national data are provided.
'STG' is a method for feature selection in neural network. The procedure is based on probabilistic relaxation of the l0 norm of features, or the count of the number of selected features. The framework simultaneously learns either a nonlinear regression or classification function while selecting a small subset of features. Read more: Yamada et al. (2020) <https://proceedings.mlr.press/v119/yamada20a.html>.
Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate  Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" <doi:10.1198/jcgs.2009.07177>. The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" <doi:10.1007/s10107-012-0584-1>.
While streaming processing provides opportunities to deal with extremely large and ever growing data sets in (near) real time, the development of streaming algorithms for complex models is often cumbersome: the software packages that facilitate streaming processing in production environments do not provide statisticians with the simulation, estimation, and plotting tools they are used to. Developers of streaming algorithms would thus benefit from the flexibility of [R] to create, plot and compute data while developing streaming algorithms. Package RStorm implements a streaming architecture modeled on Storm for easy development and testing of streaming algorithms in [R]. RStorm is not intended as a production package, but rather a development tool for streaming algorithms.
R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>, and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.
Functions to access data from the 'Strava v3 API' <https://developers.strava.com/>.
Unified object oriented interface for multiple independent streams of random numbers from different sources.
A convenience interface for communicating with the Stripe payment processor to accept payments online. See <https://stripe.com> for more information.
As of 'RStudio' v1.3, the preferences in the Global Options
    dialog (and a number of other preferences that aren’t) are now saved
    in simple, plain-text JSON files. This package provides an interface
    for working with these 'RStudio' JSON preference files to easily make
    modifications without using the point-and-click option menus. This is
    particularly helpful when working on teams to ensure a unified
    experience across machines and utilizing settings for best practices.
Access the RStudio API (if available) and provide informative error
    messages when it's not.
A collection of efficient and effective tools and
	algorithms for subgroup discovery and analytics. The package
	integrates an R interface to the org.vikamine.kernel library
	of the VIKAMINE system <http://www.vikamine.org> implementing
	subgroup discovery, pattern mining and analytics in Java.
Produces tables with the level of replication (number of replicates) and the
    experimental uncoded values of the quantitative factors to be used for rotatable Central
    Composite Design (CCD) experimentation and a 2-D contour plot of the corresponding
    variance of the predicted response according to
    Mead et al. (2012) <doi:10.1017/CBO9781139020879> design_ccd(), and analyzes
    CCD data with response surface methodology ccd_analysis(). A rotatable CCD
    provides values of the variance of the predicted response that are concentrically
    distributed around the average treatment combination used in the experimentation, which
    with uniform precision (implied by the use of several replicates at the average
    treatment combination) improves greatly the search and finding of an optimum response.
    These properties of a rotatable CCD represent undeniable advantages over the classical
    factorial design, as discussed by Panneton et al. (1999) <doi:10.13031/2013.13267> and
    Mead et al. (2012) <doi:10.1017/CBO9781139020879.018> among others.
Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.
A range of functions for the design and
    analysis of disease surveillance activities. These functions were
    originally developed for animal health surveillance activities but can be
    equally applied to aquatic animal, wildlife, plant and human health
    surveillance activities. Utilities are included for sample size calculation
    and analysis of representative surveys for disease freedom, risk-based
    studies for disease freedom and for prevalence estimation.
    This package is based on Cameron A., Conraths F., Frohlich A., Schauer B.,
    Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of 
    functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision 
    making tools for implementing risk-based surveillance, Grant Number 
    no. 310806, RISKSUR (<https://www.fp7-risksur.eu/sites/default/files/documents/Deliverables/RISKSUR_%28310806%29_D6.24.pdf>). 
    Many of the 'RSurveillance' functions are incorporated into the 'epitools'
    website: Sergeant, ESG, 2019. Epitools epidemiological calculators. 
    Ausvet Pty Ltd. Available at: <http://epitools.ausvet.com.au>.
'SurveyCTO' is a platform for mobile data collection in offline settings.
  The 'rsurveycto' R package uses the 'SurveyCTO' REST API
  <https://docs.surveycto.com/05-exporting-and-publishing-data/05-api-access/01.api-access.html>
  to read datasets and forms from a 'SurveyCTO' server into R as 'data.table's
  and to download file attachments. The package also has limited support to
  write datasets to a server.
Low-rank matrix decompositions are fundamental tools and widely used for data
  analysis, dimension reduction, and data compression. Classically, highly accurate 
  deterministic matrix algorithms are used for this task. However, the emergence of 
  large-scale data has severely challenged our computational ability to analyze big data. 
  The concept of randomness has been demonstrated as an effective strategy to quickly produce
  approximate answers to familiar problems such as the singular value decomposition (SVD). 
  The rsvd package provides several randomized matrix algorithms such as the randomized 
  singular value decomposition (rsvd), randomized principal component analysis (rpca), 
  randomized robust principal component analysis (rrpca), randomized interpolative 
  decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot 
  functions are provided.
Computing singular value decomposition with robustness is a challenging task. 
    This package provides an implementation of computing robust SVD using density power 
    divergence (<arXiv:2109.10680>). It combines the idea of robustness and efficiency in estimation
    based on a tuning parameter. It also provides utility functions to simulate various
    scenarios to compare performances of different algorithms.
Renders vector-based svg images into high-quality custom-size
    bitmap arrays using 'librsvg2'. The resulting bitmap can be written to
    e.g. png, jpeg or webp format. In addition, the package can convert
    images directly to various formats such as pdf or postscript.
Interface to 'SWI'-'Prolog', <https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package 'rolog' instead. The purpose of this package is to provide the 'Prolog' runtime on systems that do not have a software installation of 'SWI'-'Prolog'.
An R interface to the SYMPHONY solver for mixed-integer linear programs.
'SyncroSim' is a generalized framework for managing scenario-based 
    datasets (<https://syncrosim.com/>). 'rsyncrosim' provides an interface to 
    'SyncroSim'. Simulation models can be added to 'SyncroSim' in order to 
    transform these datasets, taking advantage of general features such as 
    defining scenarios of model inputs, running Monte Carlo simulations, and 
    summarizing model outputs. 'rsyncrosim' requires 'SyncroSim' 2.3.5 or higher 
    (API documentation: <https://docs.syncrosim.com/>).
Various functions for querying and reshaping dependency trees, 
    as for instance created with the 'spacyr' or 'udpipe' packages.
    This enables the automatic extraction of useful semantic relations from texts,
    such as quotes (who said what) and clauses (who did what). Method proposed in 
    Van Atteveldt et al. (2017) <doi:10.1017/pan.2016.12>.
Functions to write messages to the 'syslog' system logger API,
  available on all 'POSIX'-compatible operating systems. Features include
  tagging messages with a priority level and application type, as well as
  masking (hiding) messages below a given priority level.
Provides a programmatic interface to the 'Request Tracker' (RT)
  HTTP API <https://rt-wiki.bestpractical.com/wiki/REST>. 'RT' is a popular
  ticket tracking system.
Performs one-sample t-test based on robustified statistics using median/MAD (TA) and Hodges-Lehmann/Shamos (TB). For more details, see Park and Wang (2018)<arXiv:1807.02215>. This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).
Play the classic game of tic-tac-toe (naughts and crosses).
Contains functions for random generation of R x C and 2 x 2 x K contingency tables. In addition to the generation of contingency tables over predetermined intraclass-correlated clusters, it is possible to generate contingency tables without intraclass correlations under product multinomial, multinomial, and Poisson sampling plans. It also consists of a function for generation of random data from a given discrete probability distribution function. See Demirhan (2016) <https://journal.r-project.org/archive/2016-1/demirhan.pdf> for more information.
Reporting tables often have structure that goes beyond simple
    rectangular data. The 'rtables' package provides a framework for
    declaring complex multi-level tabulations and then applying them to
    data. This framework models both tabulation and the resulting tables
    as hierarchical, tree-like objects which support sibling sub-tables,
    arbitrary splitting or grouping of data in row and column dimensions,
    cells containing multiple values, and the concept of contextual
    summary computations. A convenient pipe-able interface is provided for
    declaring table layouts and the corresponding computations, and then
    applying them to data.
Applies a given global-fit method to random partial tanglegrams of a fixed size
             to identify the associations, terminals, and nodes that maximize phylogenetic
             (in)congruence. It also includes functions to compute more easily the confidence
             intervals of classification metrics and plot results, reducing computational time.
             See Llaberia-Robledillo et al. (2022, <doi:10.1101/2022.05.17.492291>).
Storing huge data in RData format causes problems because
        of the necessity to load the whole file to the memory in order
        to access and manipulate objects inside such file; rtape is a
        simple solution to this problem. The package contains several
        wrappers of R built-in serialize/unserialize mechanism allowing
        user to quickly append objects to a tape-like file and later
        iterate over them requiring only one copy of each stored object
        to reside in memory a time.
Discretize AR(1) process following Tauchen (1986) <http://www.sciencedirect.com/science/article/pii/0165176586901680>. A discrete Markov chain that approximates in the sense of weak convergence a continuous-valued univariate Autoregressive process of first order is generated. It is a popular method used in economics and in finance.
We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.
The Randomized Trait Community Clustering method (Triado-Margarit et al., 2019,
    <doi:10.1038/s41396-019-0454-4>) is a statistical approach which allows to determine whether
    if an observed trait clustering pattern is related to an increasing environmental constrain.
    The method 1) determines whether exists or not a trait clustering on the sampled communities
    and 2) assess if the observed clustering signal is related or not to an increasing environmental
    constrain along an environmental gradient. Also, when the effect of the environmental gradient
    is not linear, allows to determine consistent thresholds on the community assembly based on trait-values.
The 'TRUST4' or 'MiXCR' is used to identify the clonotypes.
    The goal of 'rTCRBCRr' is to process the results from these clonotyping tools,
    and analyze the clonotype repertoire metrics based on chain names and IGH isotypes.
    The manuscript is still under preparation for publication for now.
    The references describing the methods in this package will be added later.
Upload R data.frame to Arm Treasure Data, see <https://www.treasuredata.com/>. 
  You can execute database or table handling for resources on Arm Treasure Data.
Create interactive visualisations of Rt estimates using 'D3.js' 
  (Gibbs et al. (2020) <doi:10.5281/zenodo.4011842>). Developed primarily targeting Rt estimates 
  generated by the 'EpiNow2' package, 'RtD3' aims to make simple, beautiful 
  visualisations that help researchers explore their results and share them with others.
Robust tail dependence estimation for bivariate models. This package is based on two papers by the authors:'Robust and bias-corrected estimation of the coefficient of tail dependence' and 'Robust and bias-corrected estimation of probabilities of extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).
Provides response time distributions (density/PDF,
       distribution function/CDF, quantile function, and random
       generation): (a) Ratcliff diffusion model (Ratcliff &
       McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) based on C
       code by Andreas and Jochen Voss and (b) linear ballistic
       accumulator (LBA; Brown & Heathcote, 2008,
       <doi:10.1016/j.cogpsych.2007.12.002>) with different
       distributions underlying the drift rate.
Exploit controlled vocabularies organized on tematres servers.
A collection of R Markdown templates for nicely structured, reproducible 
    data analyses in R. The templates have embedded examples on how to write 
    citations, footnotes, equations and use colored message/info boxes, how to 
    cross-reference different parts/sections in the report, provide a nice 
    table of contents (toc) with a References section and proper R session 
    information as well as examples using DT tables and ggplot2 graphs. 
    The bookdown Lite template theme supports code folding.
A set of tools for creation, manipulation, and modeling
    of tensors with arbitrary number of modes. A tensor in the context of data
    analysis is a multidimensional array. rTensor does this by providing a S4
    class 'Tensor' that wraps around the base 'array' class. rTensor
    provides common tensor operations as methods, including matrix unfolding,
    summing/averaging across modes, calculating the Frobenius norm, and taking
    the inner product between two tensors. Familiar array operations are
    overloaded, such as index subsetting via '[' and element-wise operations.
    rTensor also implements various tensor decomposition, including CP, GLRAM,
    MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements
    transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some
    auxiliary functions include the Khatri-Rao product, Kronecker product, and
    the Hadamard product for a list of matrices.
A set of tools for basic tensor operators.  A tensor in the context of data analysis in a multidimensional array. The tools in this package rely on using any discrete transformation (e.g. Fast Fourier Transform (FFT)).  Standard tools included are the Eigenvalue decomposition of a tensor, the QR decomposition and LU decomposition.  Other functionality includes the inverse of a tensor and the transpose of a symmetric tensor. Functionality in the package is outlined in Kernfeld et al. (2015) <https://www.sciencedirect.com/science/article/pii/S0024379515004358>.
Models and displays tephra transport through custom (windy, turbulent, heterogeneous) atmosphere over custom topography. Includes a Lagrangian (particle-tracking) tephra transport model and a function to save snapshots of model as png files.
A small language extension for succinct conditional assignment using `?` and `:`, emulating the conditional ternary operator syntax using in C, Java, JavaScript and other languages.
A machine learning package for automatic text classification 
	that makes it simple for novice users to get started with machine 
	learning, while allowing experienced users to easily experiment 
	with different settings and algorithm combinations. The package 
	includes eight algorithms for ensemble classification (svm, slda, 
	boosting, bagging, random forests, glmnet, decision trees, neural 
	networks), comprehensive analytics, and thorough documentation.
A set of R functions to output Rich Text Format (RTF) files with high resolution tables and graphics that may be edited with a standard word processor such as Microsoft Word.
Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order tensor time series, and have wide applications in economics, finance and medical imaging. We propose an one-step projection estimator by minimizing the least-square loss function, and further propose a robust estimator with an iterative weighted projection technique by utilizing the Huber loss function. The methods are discussed in Barigozzi et al. (2022) <arXiv:2206.09800>, and Barigozzi et al. (2023) <arXiv:2303.18163>.
Methods for comparing different regression algorithms for 
    describing the temporal dynamics of secondary tree growth (xylem and 
    phloem). Users can compare the accuracy of the most common fitting methods 
    usually used to analyse xylem and phloem data, i.e., Gompertz function and 
    General Additive Models (GAMs); and an algorithm newly introduced to the 
    field, i.e., Bayesian Regularised Neural Networks (brnn). The core function
    of the package is XPSgrowth(), while the results can be interpreted using 
    implemented generic S3 methods, such as plot() and summary().
Allow function for using 'TGStat Stat API' and
    'TGStat Search API', for more details see <https://api.tgstat.ru/docs/ru/start/intro.html>.
    'TGStat' provide telegram channel analytics data.
The goal of 'Rthingsboard' is to provide interaction with the API of 'ThingsBoard' (<https://thingsboard.io/>), an open-source IoT platform for device management, data collection, processing and visualization.
A suite of custom R Markdown formats and templates for
    authoring journal articles and conference submissions.
A tool for processing Articulate
    Assistant Advanced™ (AAA) export files and plot tongue contour data from any
    system.
'TiddlyWiki' is a unique non-linear notebook for capturing, organising and sharing complex information. 'rtiddlywiki' is a R interface of 'TiddlyWiki' <https://tiddlywiki.com> to create new tiddler from Rmarkdown file, and then put into a local 'TiddlyWiki' node.js server if it is available.
Calculates tide heights based on tide station harmonics.  It
    includes the harmonics data for 637 US stations.  The harmonics data
    was converted from
    <https://github.com/poissonconsulting/rtide/blob/master/data-raw/harmonics-dwf-20151227-free.tar.bz2>,
    NOAA web site data processed by David Flater for 'XTide'.  The code to
    calculate tide heights from the harmonics is based on 'XTide'.
The name of this package grew out of our research on temporal interpersonal 
    emotion systems (TIES), hence 'rties'. It provides tools for using a set of models to 
    investigate temporal processes in bivariate (e.g., dyadic) systems. The general approach
    is to model, one dyad at a time, the dynamics of a variable that is assessed repeatedly 
    from both partners, extract the parameter estimates for each dyad, and then use those 
    parameter estimates as input to a latent profile analysis to extract groups of dyads with
    qualitatively distinct dynamics. Finally, the profile memberships can be used to either 
    predict, or be predicted by, another variable of interest. Currently, 2 models are supported:
    1) inertia-coordination, and 2) a coupled-oscillator. Extended documentation is provided 
    in vignettes. Theoretical background can be found in Butler (2011) <doi:10.1177/1088868311411164> 
    and Butler & Barnard (2019) <doi:10.1097/PSY.0000000000000703>.
Our method integrates information from all sequenced samples, thus avoiding loss of alleles due to low coverage. Moreover, it increases the statistical power to uncover sequencing or alignment errors <doi:10.1093/plphys/kiad191>.
Extract text or metadata from over a thousand file types, using Apache Tika <https://tika.apache.org/>. Get either plain text or structured XHTML content.
R tools to measure and compare inequality, welfare and poverty using the EU statistics on income and living conditions surveys.
Color palettes from famous artists and paintings.
Rarefy data, calculate diversity and plot the results.
'STK++' <http://www.stkpp.org> is a collection of C++ classes
    for statistics, clustering, linear algebra, arrays (with an 'Eigen'-like API),
    regression, dimension reduction, etc. The integration of the library to 'R' is
    using 'Rcpp'. The 'rtkore' package includes the header files from the 'STK++'
    core library. All files contain only template classes and/or inline functions.
    'STK++' is licensed under the GNU LGPL version 2 or later. 'rtkore' (the 'stkpp'
    integration into 'R') is licensed under the GNU GPL version 2 or later. See file
    LICENSE.note for details.
A toolkit for Commodities 'analytics', risk management and
    trading professionals. Includes functions for API calls to
    <https://commodities.morningstar.com/#/>, <https://developer.genscape.com/>,
    and <https://www.bankofcanada.ca/valet/docs>.
Convenience functions to make some common tasks with right-to-left string
    printing easier, more convenient and with no need to remember long Unicode
    characters. Specifically helpful for right-to-left languages such as Arabic,
    Persian and Hebrew.
Terrestrial laser scanning (TLS) data processing and post-hurricane damage severity classification at the individual tree level using deep Learning. Further details were published in Klauberg et al. (2023) <doi:10.3390/rs15041165>.
Fit response-time extended multinomial processing tree (RT-MPT) models by Klauer and
			Kellen (2018) <doi:10.1016/j.jmp.2017.12.003>. The RT-MPT class not only incorporate
			frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it
			to estimate process completion times and encoding plus motor execution times next to the process probabilities
			of traditional MPTs. 'rtmpt' is a Bayesian framework and posterior samples are sampled using a Metropolis-Gibbs
			sampler like the one described in the Klauer and Kellen (2018), but with some modifications. Other than in
			the original C++ program we use the free and open source GNU Scientific Library (GSL). There is also the
			possibility to suppress single process completion times.
Quickly imports, processes, analyzes, and visualizes mass-spectrometric 
	data.  Includes functions for easily extracting specific data and measurements
	from large (multi-gigabyte) raw Bruker data files, as well as a set of S3 object
	classes for manipulating and measuring mass spectrometric peaks and plotting
	peaks and spectra using the 'ggplot2' package.
Truncated Newton function minimization with bounds constraints
	based on the 'Matlab'/'Octave' codes of Stephen Nash.
Allows you to interact with the API of the "Todoist" platform.
    'Todoist' <https://todoist.com/> provides an online task manager service for teams.
Aimed at seismic tomography, the package
  plots tomographic images, and allows one to interact and query
  three-dimensional tomographic models.
  Vertical cross-sectional cuts can be extracted by mouse click.
  Geographic information can be added easily.
An implementation of calls designed to collect and organize Mastodon data via its Application Program Interfaces (API), which can be found at the following URL: <https://docs.joinmastodon.org/>.
Data with irregular spatial support, such as runoff related data or data from administrative units, can with 'rtop' be interpolated to locations without observations with the top-kriging method. A description of the package is given by Skøien et al (2014) <doi:10.1016/j.cageo.2014.02.009>.
'R' implementation and interface of the Machine Learning platform 
    'PyTorch' <https://pytorch.org/> developed in 'Python'. It requires a 'conda'
    environment with 'torch' and 'torchvision' Python packages to provide 
    'PyTorch' functions, methods and classes. The key object in 'PyTorch' is the 
    tensor which is in essence a multidimensional array. These tensors are fairly 
    flexible in performing calculations in CPUs as well as 'GPUs' to accelerate 
    tensor operations.
A toolkit for the analysis of paths from spatial tracking experiments (such as the Morris water maze) and calculation of goal-finding strategies. 
    This package is centered on an approach using machine learning for path classification.
Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) <doi:10.1515/snde-2012-0044> and Dimpfl and Peter (2014) <doi:10.1016/j.intfin.2014.03.004> for theory and applications to financial time series. Additional references can be found in the theory part of the vignette.
Provides datasets related to the Star Trek fictional universe and functions for working with the data.
    The package also provides access to real world datasets based on the televised series and other related licensed media productions.
    It interfaces with the Star Trek API (STAPI) (<http://stapi.co/>), 
    Memory Alpha (<https://memory-alpha.fandom.com/wiki/Portal:Main>), and Memory Beta (<https://memory-beta.fandom.com/wiki/Main_Page>) 
    to retrieve data, metadata and other information relating to Star Trek.
    It also contains several local datasets covering a variety of topics. 
    The package also provides functions for working with data from other Star Trek-related 
    R data packages containing larger datasets not stored in 'rtrek'.
The traditional linear regression trend, Modified Mann-Kendall (MK)
    non-parameter trend and bootstrap trend are included in this package. Linear 
    regression trend is rewritten by '.lm.fit'. MK trend is rewritten by 'Rcpp'.
    Finally, those functions are about 10 times faster than previous version 
    in R.
    Reference:
    Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for 
    autocorrelated data. Journal of hydrology, 204(1-4), 182-196. 
    <doi:10.1016/S0022-1694(97)00125-X>.
Analyze download logs from the CRAN RStudio mirror 
            (<http://cran.rstudio.com/>). 
            This CRAN mirror is the default one used in RStudio.
            The available data is the result of parsed and anonymised raw log data from
            that CRAN mirror.
This is a port of Jonathan Shewchuk's Triangle library to
    R. From his description: "Triangle generates exact Delaunay
    triangulations, constrained Delaunay triangulations, conforming
    Delaunay triangulations, Voronoi diagrams, and high-quality
    triangular meshes. The latter can be generated with no small or
    large angles, and are thus suitable for finite element analysis."
The TRIM model is widely used for estimating growth and decline of
    animal populations based on (possibly sparsely available) count data. The
    current package is a reimplementation of the original TRIM software developed
    at Statistics Netherlands by Jeroen Pannekoek. See
    <https://www.cbs.nl/en-gb/society/nature-and-environment/indices-and-trends%2d%2dtrim%2d%2d>
    for more information about TRIM.
A carbon-water coupled model (TRIPLEX-CW-Flux) is based on two well-established models, TRIPLEX-Flux model and Penman–Monteith model, integrates soil water and water vapor pressure deficits into the stomata conductance submodule to estimate net ecosystem production and evapotranspiration in forest ecosystems.<https://github.com/ShulanSun/rTRIPLEX_CW_Flux>.
Embeds sources and headers from Tina's Random
    Number Generator ('TRNG') C++ library. Exposes some functionality for
    easier access, testing and benchmarking into R. Provides examples of
    how to use parallel RNG with 'RcppParallel'. The methods and
    techniques behind 'TRNG' are illustrated in the package vignettes and
    examples. Full documentation is available in Bauke (2021)
    <https://github.com/rabauke/trng4/blob/v4.23.1/doc/trng.pdf>.
Process phylogenetic trees with tropical support vector machine and principal component analysis defined with tropical geometry. Details about tropical support vector machine are available in : Tang, X., Wang, H. & Yoshida, R. (2020) <arXiv:2003.00677>. Details about tropical principle component analysis are available in : Page, R., Yoshida, R. & Zhang L. (2020) <doi:10.1093/bioinformatics/btaa564> and Yoshida, R., Zhang, L. & Zhang, X. (2019) <doi:10.1007/s11538-018-0493-4>.
Designed to support the application of plant trait data providing easy applicable functions
    for the basic steps of data preprocessing, e.g. data import, data exploration, selection of columns
    and rows, excluding trait data according to different attributes, geocoding, long- to wide-table
    transformation, and data export. 'rtry' was initially developed as part of the TRY R project to
    preprocess trait data received via the TRY database.
This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).
Supports modelling real-time case data to facilitate the real-time
    surveillance of infectious disease. A simple grid class structure is provided to generate a computational grid over
    an area of interest with methods to map covariates between geographies. An approximate log-Gaussian Cox Process
    model is fit using 'rstan' or 'cmdstanr' and provides output and analysis as 'sf' objects for simple visualisation.
    'cmdstanr' can be downloaded at <https://mc-stan.org/cmdstanr/>. Log-Gaussian Cox Processes are described by
    Diggle et al. (2013) <doi:10.1214/13-STS441> and we provide both the low-rank approximation  for Gaussian processes
    described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol et al (2020) <arXiv:2004.11408> and the
    nearest neighbour Gaussian process described by Datta et al (2016) <doi:10.1080/01621459.2015.1044091>.
Frequentist meta-analysis and sequential meta-analysis based on 
    'Trial Sequential Analysis' (TSA) by Copenhagen Trial Unit (CTU). Primary 
    usage is the calculation of group sequential designs for meta-analysis to be
    used for planning and analysis of both prospective and retrospective 
    sequential meta-analyses to preserve type-I-error control under sequential 
    testing. 'RTSA' includes tools for sample and trial size calculation for 
    meta-analysis and core meta-analyses methods such as fixed-effect and 
    random-effects models and forest plots. TSA is described in Wetterslev et. 
    al (2008) <doi:10.1016/j.jclinepi.2007.03.013>. The methods for deriving the
    group sequential designs are based on Jennison and Turnbull (1999,
    ISBN:9780849303166).
A tool that allows to download and save historical time series data 
	for future use offline. The intelligent updating functionality will only download 
	the new available information; thus, saving you time and Internet bandwidth. 
	It will only re-download the full data-set if any inconsistencies are detected. 
	This package supports following data provides: 'Yahoo' (<https://finance.yahoo.com>), 
	'FRED' (<https://fred.stlouisfed.org>), 'Quandl' (<https://data.nasdaq.com>), 
	'AlphaVantage' (<https://www.alphavantage.co>), 'Tiingo' (<https://www.tiingo.com>).
An R wrapper around the fast T-distributed Stochastic
    Neighbor Embedding implementation by Van der Maaten  (see <https://github.com/lvdmaaten/bhtsne/> for more information on the original implementation).
TSON, short for Typed JSON, is a binary-encoded serialization of
    JSON like document that support JavaScript typed data (https://github.com/tercen/TSON).
A fast and elegant time series visualization package. In addition to the 
	standard R plot types, this package supports candle sticks, open-high-low-close, and
	volume plots. Useful for visualizing any time series data, e.g., stock prices and 
	technical indicators.
Contains the program 'ttf2pt1', for use with the
    'extrafont' package. This product includes software developed by the 'TTF2PT1'
    Project and its contributors.
R Web Client to 'TickTrader' platform. Provides you access to 'TickTrader' platform through Web API <https://ttlivewebapi.fxopen.net:8443/api/doc/index>.
An implementation of calls designed to collect 'Tumblr' data via its Application Program Interfaces (API), which can be found at the following URL: <https://www.tumblr.com/docs/en/api/v2>.
The 't-walk' is a general-purpose MCMC sampler for
      arbitrary continuous distributions that requires no tuning.
An implementation of calls designed to collect and organize
    Twitter data via Twitter's REST and stream Application Program
    Interfaces (API), which can be found at the following URL:
    <https://developer.twitter.com/en/docs>.
A strong type system for R which supports
    symbol declaration and assignment with type checking
    and condition checking.
An R interface to the 'typeform'
    <https://typeform.com> application program interface.  Also provides
    functions for downloading your results.
Implements Bayesian inference for the conditional genetic 
    stock identification model.  It allows inference of mixed fisheries and also 
    simulation of mixtures to predict accuracy.  A full description of the underlying
    methods is available in a recently published article in the
    Canadian Journal of Fisheries and Aquatic Sciences: <doi:10.1139/cjfas-2018-0016>.
Unobserved Components Models (introduced in Harvey, A. (1989),
    Forecasting, structural time series models and the Kalman filter, Cambridge
    New York: Cambridge University Press) decomposes a time series into
    components such as trend, seasonal, cycle, and the regression effects due
    to predictor series which captures the salient features of the series to
    predict its behavior.
R bindings for functions from the UCR Suite by Rakthanmanon et al. (2012) <DOI:10.1145/2339530.2339576>, which enables ultrafast subsequence
      search for a best match under Dynamic Time Warping and Euclidean Distance.
ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
The real-time quantitative polymerase chain reaction
    ('PCR') technical data sets by 'Ruijter et al.' (2013)
    <doi:10.1016/j.ymeth.2012.08.011>: (i) the four-point 10-fold dilution
    series; (ii) 380 replicates; and (iii) the 'competimer' data set. These
    three data sets can be used to benchmark 'qPCR' methods. Original data set
    is available at
    <https://medischebiologie.nl/wp-content/uploads/2019/02/qpcrdatamethods.zip>.
    This package fixes incorrect annotations in the original data sets.
Wraps the 'StarSpace' library <https://github.com/facebookresearch/StarSpace> 
    allowing users to calculate word, sentence, article, document, webpage, link and entity 'embeddings'. 
    By using the 'embeddings', you can perform text based multi-label classification, 
    find similarities between texts and categories, do collaborative-filtering based recommendation 
    as well as content-based recommendation, find out relations between entities, calculate 
    graph 'embeddings' as well as perform semi-supervised learning and multi-task learning on plain text. 
    The techniques are explained in detail in the paper: 'StarSpace: Embed All The Things!' by Wu et al. (2017), available at <arXiv:1709.03856>.
A (not yet exhaustive) collection of common models of risk
    processes in actuarial science, represented as formal S4 classes. Each class
    (risk model) has a simulator of its path, and a plotting function. Further, 
    a Monte-Carlo estimator of a ruin probability for a finite time is
    implemented, using a parallel computation. Currently, the package extends
    two classical risk models Cramer-Lundberg and Sparre Andersen models by
    including capital injections, that are positive jumps (see Breuer L. and
    Badescu A.L. (2014) <doi:10.1080/03461238.2011.636969>). The intent of the
    package is to provide a user-friendly interface for ruin processes'
    simulators, as well as a solid and extensible structure for future
    extensions.
Tools for creating data validation pipelines and
    tidy reports. This package offers a framework for exploring and
    validating data frame like objects using 'dplyr' grammar of data
    manipulation.
Bindings for additional models for use with the 'parsnip'
    package.  Models include prediction rule ensembles (Friedman and
    Popescu, 2008) <doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992
    ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013)
    <doi:10.1007/978-1-4614-6849-3>.
This holds some r markdown and quarto templates and a template to 
    create a research project in "R Studio".
Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. 'rumidas' also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.
Plots multiple run charts, finds successive signals of 
    improvement, and revises medians when each signal occurs. Finds runs
    above, below, or on both sides of the median, and returns a plot and
    a data.table summarising original medians and any revisions, for all
    groups within the supplied data.
Convert a string of text characters to Elder Futhark Runes <https://en.wikipedia.org/wiki/Elder_Futhark>.
Implements two methods of estimating runs scored in a softball 
    scenario: (1) theoretical expectation using discrete Markov chains and (2) empirical
    distribution using multinomial random simulation.  Scores are based on player-specific input 
    probabilities (out, single, double, triple, walk, and homerun).  Optional inputs include probability
    of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether
    a player is "fast" (e.g. the player could stretch home).  These probabilities may be 
    calculated from common player statistics that are publicly available on team's webpages. 
    Scores are evaluated based on a nine-player lineup and may be used to compare lineups, 
    evaluate base scenarios, and compare the offensive potential of individual players.  
    Manuscript forthcoming.  See Bukiet & Harold (1997) <doi:10.1287/opre.45.1.14> for 
    implementation of discrete Markov chains.
R functions implementing a standard Unit Testing
        framework, with additional code inspection and report
        generation tools.
User-friendly interface utilities for MCMC models via
    Just Another Gibbs Sampler (JAGS), facilitating the use of parallel
    (or distributed) processors for multiple chains, automated control
    of convergence and sample length diagnostics, and evaluation of the
    performance of a model using drop-k validation or against simulated
    data. Template model specifications can be generated using a standard
    lme4-style formula interface to assist users less familiar with the
    BUGS syntax.  A JAGS extension module provides additional distributions
    including the Pareto family of distributions, the DuMouchel prior and
    the half-Cauchy prior.
The function runMCMC_btadjust() returns a mcmc.list object which is the output of a 
  Markov Chain Monte Carlo obtained - from either 'JAGS', 'nimble' or 'greta' - after adjusting burn-in and thinning parameters to meet 
  pre-specified criteria in terms of convergence & effective sample size.
Lightweight library for rolling windows operations. Package enables
  full control over the window length, window lag and a time indices. With a runner 
  one can apply any R function on a rolling windows. The package eases work with 
  equally and unequally spaced time series.
Package 'runonce' helps automating the saving of long-running code
    to help running the same code multiple times. If you run some long-running 
    code once, it saves the result in a file on disk. Then, if the result 
    already exists, i.e. if the code has already been run and its output has 
    already been saved, it just reads the result from the stored file instead 
    of running the code again.
Provides methods for fast computation of running sample 
    statistics for time series. These include: (1) mean, (2) 
    standard deviation, and (3) variance over a fixed-length window 
    of time-series, (4) correlation, (5) covariance, and (6) 
    Euclidean distance (L2 norm) between short-time pattern and 
    time-series. Implemented methods utilize Convolution Theorem to 
    compute convolutions via Fast Fourier Transform (FFT).
Interface to the 'UNU.RAN' library for Universal Non-Uniform RANdom variate generators. 
	     Thus it allows to build non-uniform random number generators from quite arbitrary
	     distributions. In particular, it provides an algorithm for fast numerical inversion
	     for distribution with given density function.
	     In addition, the package contains densities, distribution functions and quantiles
	     from a couple of distributions.
An interface to the web service methods provided by the United States Department of Agriculture (USDA). The Agricultural Research Service (ARS) provides a large set of databases. The current version of the package holds interfaces to the Systematic Mycology and Microbiology Laboratory (SMML), which consists of four databases: Fungus-Host Distributions, Specimens, Literature and the Nomenclature database. It provides functions for querying these databases. The main function is \code{associations}, which allows searching for fungus-host combinations.
By placing on a circle 10 points numbered from 1 to 10, and connecting them by a straight line to the point corresponding to its multiplication by 2. (1 must be connected to 1 * 2 = 2, point 2 must be set to 2 * 2 = 4, point 3 to 3 * 2 = 6 and so on). You will obtain an amazing geometric figure that complicates and beautifies itself by varying the number of points and the multiplication table you use.
Uses the generalized ratio-of-uniforms (RU) method to simulate
    from univariate and (low-dimensional) multivariate continuous distributions.
    The user specifies the log-density, up to an additive constant. The RU
    algorithm is applied after relocation of mode of the density to zero, and
    the user can choose a tuning parameter r. For details see Wakefield, Gelfand
    and Smith (1991) <DOI:10.1007/BF01889987>, Efficient generation of random
    variates via the ratio-of-uniforms method, Statistics and Computing (1991)
    1, 129-133.  A Box-Cox variable transformation can be used to make the input
    density suitable for the RU method and to improve efficiency.  In the
    multivariate case rotation of axes can also be used to improve efficiency.
    From version 1.2.0 the 'Rcpp' package 
    <https://cran.r-project.org/package=Rcpp> can be used to improve efficiency.
Implementation of several unsupervised neural networks,
    from building their architecture to their training and evaluation. Available
    networks are auto-encoders including their main variants: sparse, contractive,
    denoising, robust and variational, as described in Charte et al. (2018)
    <doi:10.1016/j.inffus.2017.12.007>.
A RUT (Rol Unico Tributario) is an unique and personal identification number implemented in Chile to identify citizens and taxpayers. Rutifier allows to validate if a RUT exist or not and change between the different formats a RUT can have.
Real-time quantitative polymerase chain reaction (qPCR) data
    by Rutledge et al. (2004) <doi:10.1093/nar/gnh177> in tidy format. The
    data comprises a six-point, ten-fold dilution series, repeated in five
    independent runs, for two different amplicons. In each run, each standard
    concentration is replicated four times. Original raw data file:
    <https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/nar/32/22/10.1093_nar_gnh177/1/gnh177_Supplementary_Data.zip?Expires=1680370010&Signature=xxk4VxX0C4yr3UWzxgh7ieCt7QQmpFMRauvsVEwtGXYGCcyQY7uhNCE-M8zx9kpyDPoS8NR7fjBuMx2Xz2ANFwF1VqnjQ4AfO37klnJ3CHRIJ7bj01n2mycHDnvJ3XawHdWT8TqJxTxVC9CpYEkH2xGeJBnnpcnXLbc94A8KB8FCtg2WR3O~ULkaOQQ8uJAiVdJhnBHH~XfBRkfoKHSuyJgX7n7M2~gwXnZH9n3vUyo~CHrpIax7Hi0xUSCBbQM571hxA7JIHkhZ0HBm2aXFuAru2yJ~o8jMEnnguOJg8T7mGqTDzUBtW0hJhmQDksdJoyeAFzU84QRUIZj9q3-tXg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA>.
Implements the 'RUV' (Remove Unwanted Variation) algorithms.  These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data.  The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well.  These algorithms were proposed in Gagnon-Bartsch and Speed (2012) <doi:10.1093/nar/gkz433>, Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) <doi:10.1093/nar/gkz433>.  The algorithms require the user to specify a set of negative control variables, as described in the references.  The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms.
Variations of Remove Unwanted Variation-III (RUV-III) known as RUV-III-C (RUV-III Complete). RUV-III performs normalisation using negative control variables and replication. RUV-III-C extends this method to cases where the data contains missing values, by applying RUV-III to complete subsets of the data. Originally designed for SWATH-MS proteomics datasets. 
    Poulos et al. (2020) <doi:10.1038/s41467-020-17641-3>.
Implements a simulation-based random variable class and a suite of
  methods for extracting parts of random vectors, calculating extremes of random
  vectors, and generating random vectors under a variety of distributions 
  following Kerman and Gelman (2007) <doi:10.1007/s11222-007-9020-4>.
Automate downstream visualization & pathway analysis in RNAseq analysis. 'RVA' is a collection of functions that efficiently visualize RNAseq differential expression analysis result from summary statistics tables. It also utilize the Fisher's exact test to evaluate gene set or pathway enrichment in a convenient and efficient manner.
Contains miscellaneous functions useful in biostatistics, mostly univariate and multivariate testing procedures with a special emphasis on permutation tests. Many functions intend to simplify user's life by shortening existing procedures or by implementing plotting functions that can be used with as many methods from different packages as possible.
A collection of functions for computing "r-values" from various
    kinds of user input such as MCMC output or a list of effect size estimates
    and associated standard errors. Given a large collection of measurement units,
    the r-value, r, of a particular unit is a reported percentile that may be
    interpreted as the smallest percentile at which the unit should be placed in the
    top r-fraction of units.
Operations on triangular meshes based on 'VCGLIB'. This package
    integrates nicely with the R-package 'rgl' to render the meshes processed by
    'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is
    an open source portable C++ templated library for manipulation, processing
    and displaying with OpenGL of triangle and tetrahedral meshes. The library,
    composed by more than 100k lines of code, is released under the GPL license,
    and it is the base of most of the software tools of the Visual Computing Lab of
    the Italian National Research Council Institute ISTI <http://vcg.isti.cnr.it>,
    like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk
    <https://github.com/cnr-isti-vclab/vcglib> and patched to work with options
    determined by the configure script as well as to work with the header files
    included by 'RcppEigen'.
Check latest release version of R and R package (both in 'CRAN', 'Bioconductor' or 'Github').
A framework with tools to compare two random variables via stochastic dominance. See the README.md at <https://github.com/EtorArza/RVCompare> for a quick start guide. It can compute the Cp and Cd of two probability distributions and the Cumulative Difference Plot as explained in E. Arza (2022) <doi:10.1080/10618600.2022.2084405>. Uses bootstrap or DKW-bounds to compute the confidence bands of the cumulative distributions. These two methods are described in B. Efron. (1979) <doi:10.1214/aos/1176344552> and P. Massart (1990) <doi:10.1214/aop/1176990746>.
Simple and fast tool for transforming phytosociological 
    vegetation data into digital form for the following analysis.
    Danihelka, Chrtek, and Kaplan (2012, ISSN:00327786).
    Hennekens, and Schaminée (2001) <doi:10.2307/3237010>.
    Tichý (2002) <doi:10.1111/j.1654-1103.2002.tb02069.x>.
    Wickham, François, Henry, Müller (2022) <https://CRAN.R-project.org/package=dplyr>.
Set operations for many sets. The base functions for set operations
    in R can be used for only two sets. This package uses 'purr' to find the
    union, intersection and difference of three or more sets. This package also
    provides functions for pairwise set operations among several sets. Further, 
    based on 'ggplot2' and 'ggforce', a Venn diagram can be drawn for two or 
    three sets. For bigger data sets, a clustered heatmap showing presence/absence 
    of the elements of the sets can be drawn based on the 'pheatmap' package. 
    Finally, enrichment test can be applied to two sets whether an overlap is 
    statistically significant or not.
Build regular expressions using grammar and functionality inspired 
    by <https://github.com/VerbalExpressions>. Usage of the %>% is encouraged to
    build expressions in a chain-like fashion.
Query the main 'R' 'SVN' repository to find the
    versions 'r-release' and 'r-oldrel' refer to, and also all previous
    'R' versions and their release dates.
Retrieve, map and summarize data from the 'VertNet.org' 
    archives (<http://vertnet.org/>).  Functions allow searching by many 
    parameters, including 'taxonomic' names, places, and dates. In addition, 
    there is an interface for conducting spatially delimited searches, and 
    another for requesting large 'datasets' via email.
Wrappers around the 'xml2' and 'httr' packages to make it
    easy to download, then manipulate, HTML and XML.
Vector Graphics devices for 'Microsoft PowerPoint' and 
 'Microsoft Excel'. Functions extending package 'officer' are provided to 
 embed 'DrawingML' graphics into 'Microsoft PowerPoint' presentations and 
 'Microsoft Excel' workbooks.
To detecting rare variants for binary traits using general pedigrees, the pedigree disequilibrium tests are proposed by collapsing rare haplotypes/variants with/without weights. To run the test, MERLIN is needed in Linux for haplotyping.
'ViennaCL' is a free open-source linear algebra library 
  for computations on many-core architectures (GPUs, MIC) and 
  multi-core CPUs. The library is written in C++ and supports 'CUDA', 
  'OpenCL', and 'OpenMP' (including switches at runtime). 
  I have placed these libraries in this package as a more efficient 
  distribution system for CRAN. The idea is that you can write a package 
  that depends on the 'ViennaCL' library and yet you do not need to 
  distribute a copy of this code with your package.
Provides 'Java' graphical user interfaces 
    for viewing, manipulating and plotting graphs. 
    Graphs may be directed or undirected.
The detection of troubling approximate collinearity in a multiple linear regression model is a classical problem in Econometrics. The objective of this package is to detect it using the variance inflation factor redefined and the scatterplot between the variance inflation factor and the coefficient of variation. For more details see Salmerón R., García C.B. and García J. (2018) <doi:10.1080/00949655.2018.1463376>, Salmerón, R., Rodríguez, A. and García C. (2020) <doi:10.1007/s00180-019-00922-x>, Salmerón, R., García, C.B, Rodríguez, A. and García, C. "Limitations in Detecting Multicollinearity due to Scaling Issues in the mcvis Package" (2022, to appear in The R Journal) and Salmerón, R., García, C.B, García J. (2023, working paper) <arXiv:2005.02245>.
Provides an interface to 'vinecopulib', a C++ library for vine 
 copula modeling. The 'rvinecopulib' package implements the core features of the
 popular 'VineCopula' package, in particular inference algorithms for both vine 
 copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker 
 and more modern API, improved performances, especially in high dimensions, 
 nonparametric and multi-parameter families, and the ability to model discrete 
 variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only 
 C++ library (currently version 0.6.2). Thus users do not need to install 
 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial 
 releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' 
 is licensed under the GNU GPL version 3.
The Regional Vulnerability Index (RVI), a statistical measure of brain structural abnormality, quantifies an individual's similarity to the expected pattern (effect size) of deficits in schizophrenia (Kochunov P, Fan F, Ryan MC, et al. (2020) <doi:10.1002/hbm.25045>).
Load data from vk.com api about your communiti users and views,
    ads performance, post on user wall and etc.	For more information 
    see API Documentation <https://vk.com/dev/first_guide>.
Has various functions designed to implement the 
    Hermite-Gaussian Radial Velocity (HGRV) estimation approach of 
    Holzer et al. (2020) <arXiv:2005.14083>, which is a particular application of the radial 
    velocity method for detecting exoplanets. The overall approach consists 
    of four sequential steps, each of which has a function in this package: 
    (1) estimate the template spectrum with the function estimate_template(), 
    (2) find absorption features in the estimated template with the function 
    findabsorptionfeatures(), (3) fit Gaussians to the absorption features 
    with the function Gaussfit(), (4) apply the HGRV with simple linear 
    regression by calling the function hgrv(). This package is meant to be 
    open source. But please cite the paper Holzer et al. (2020) <arXiv:2005.14083> when 
    publishing results that use this package.
Generates pseudo-random vectors that follow an arbitrary von Mises-Fisher 
  distribution on a sphere. This method is fast and efficient when generating a large number of 
  pseudo-random vectors. Functions to generate random variates and compute density 
  for the distribution of an inner product between von Mises-Fisher random vector and 
  its mean direction are also provided.
Variable metric nonlinear function minimization with bounds constraints.
The 'Vowpal Wabbit' project is a fast out-of-core learning
 system sponsored by Microsoft Research (having started at Yahoo! Research)
 and written by John Langford along with a number of contributors. This R
 package does not include the distributed computing implementation of the
 cluster/ directory of the upstream sources. Use of the software as a network
 service is also not directly supported as the aim is a simpler direct call
 from R for validation and comparison. Note that this package contains an
 embedded older version of 'Vowpal Wabbit'. The package 'rvw' at the GitHub
 repo <https://github.com/rvw-org/rvw-legacy> can provide an alternative using 
 an external 'Vowpal Wabbit' library installation.
Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described 
    in Tonidandel & LeBreton (2015) <DOI:10.1007/s10869-014-9351-z>, with its original roots in Johnson (2000) <DOI:10.1207/S15327906MBR3501_1>. In essence, RWA decomposes
    the total variance predicted in a regression model into weights that accurately reflect the proportional 
    contribution of the predictor variables, which addresses the issue of multi-collinearity. In typical scenarios,
    RWA returns similar results to Shapley regression, but with a significant advantage on computational performance.
Provides API to Melbourne pedestrian and weather data
    <https://data.melbourne.vic.gov.au> in tidy data form.
A port of Ruby Warrior.
    Teaches R programming in a fun and interactive way.
A set of R functions which provide an
        environment for the Time-Frequency analysis of 1-D signals (and
        especially for the wavelet and Gabor transforms of noisy
        signals). It was originally written for Splus by Rene Carmona,
        Bruno Torresani, and Wen L. Hwang, first at the University of
        California at Irvine and then at Princeton University.  Credit
        should also be given to Andrea Wang whose functions on the
        dyadic wavelet transform are included. Rwave is based on the
        book: "Practical Time-Frequency Analysis: Gabor and Wavelet
        Transforms with an Implementation in S", by Rene Carmona, Wen
        L. Hwang and Bruno Torresani (1998, eBook ISBN:978008053942), Academic Press.
Perform wavelet analysis (orthogonal,translation invariant, tensorial, 1-2-3d transforms, thresholding, block thresholding, linear,...) with applications to data compression or denoising/regression. The core of the code is a port of 'MATLAB' Wavelab toolbox written by D. Donoho, A. Maleki and M. Shahram (<https://statweb.stanford.edu/~wavelab/>).
Code to facilitate simulation and inference when connectivity is defined by underlying random walks. Methods for spatially-correlated pairwise distance data are especially considered. This provides core code to conduct analyses similar to that in Hanks and Hooten (2013) <doi:10.1080/01621459.2012.724647>.
Implements the random walk clustering algorithm for weighted graphs as found in Harel and Koren (2001) <https://link.springer.com/chapter/10.1007/3-540-45294-X_3>.
A companion to the World Checklist of 
    Vascular Plants (WCVP). It includes functions to generate maps and species lists, as 
    well as match names to the WCVP. For more details and to cite the package, see: 
    Brown M.J.M., Walker B.E., Black N., Govaerts R., Ondo I., Turner R., Nic Lughadha E. (in press). 
    "rWCVP: A companion R package to the World Checklist of Vascular Plants". New Phytologist.
A tool to read and manipulate data generated from 'RiverWare'(TM) 
    <http://www.riverware.org/> simulations. 'RiverWare' and 'RiverSMART' 
    generate data in "rdf", "csv", and "nc" format. This package provides an 
    interface to read, aggregate, and summarize data from one or more 
    simulations in a 'dplyr' pipeline.
Weave and tangle drivers for Sweave extending the
  standard drivers with additional code chunk options. Currently,
  these are only options to completely ignore, or skip, code chunks on
  weaving, tangling, or both. Chunks ignored on weaving are not parsed
  and are written out verbatim on tangling. Chunks ignored on tangling
  are processed as usual on weaving, but completely left out of the
  tangled scripts.
Access the Webstat API, download data and metadata from more than 35000 time series
    from the Banque de France statistics web portal. Access requires a free client ID easily available
    from the API portal <https://developer.webstat.banque-france.fr/>.
An R interface to Weka (Version 3.9.3).
   Weka is a collection of machine learning algorithms for data mining
   tasks written in Java, containing tools for data pre-processing,
   classification, regression, clustering, association rules, and
   visualization.  Package 'RWeka' contains the interface code, the
   Weka jar is in a separate package 'RWekajars'.  For more information
   on Weka see <https://www.cs.waikato.ac.nz/ml/weka/>.
External jars required for package 'RWeka'.
Communications simulation package supporting forward error correction.
A straightforward, easy-to-use and robust parsing package which aims to
    digest history files from the popular messenger service 'WhatsApp' in all locales
    and from all devices.
Provides functions to simulate and analyze data for a regression model with an interval censored covariate, as described in Morrison et al. (2021) <doi:10.1111/biom.13472>.
Provides Wiener process distribution functions,
  namely the Wiener first passage time density, CDF, quantile and random
  functions. Additionally supplies a modelling function (wdm) and further
  methods for the resulting object.
Provides an interface with the 'Wildbook' mark-recapture ecological database framework. It 
    helps users to pull data from the 'Wildbook' framework and format data for further analysis
    with mark-recapture applications like 'Program MARK' (which can be accessed via the 'RMark' package in 'R').
    Further information on the 'Wildbook' framework is available at: <http://www.wildbook.org/doku.php>.
Tools for download and manage surface wind and sea currents data from the Global Forecasting System <https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast> and to compute connectivity between locations.
A plug in for using 'WinEdt' as an editor for R.
An expansion of R's 'stats' random wishart matrix generation. 
  This package allows the user to generate singular, Uhlig and Harald (1994) 
  <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) 
  <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate 
  wishart matrices with fractional degrees of freedom, Adhikari (2008) 
  <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility 
  modeling. Users can also use this package to create random covariance matrices.
Implementation of the Integrated Simple Weighted Sum Product Method (WISP), a multiple criteria sorting method create by Dragisa Stanujkic (2021) <doi:10.1109/TEM.2021.3075783>.
Contains convenience functions for working with spatial data across
    multiple UTM zones, raster-vector operations common in the analysis of 
    conflict data, and converting degrees, minutes, and seconds latitude and
    longitude coordinates to decimal degrees.
An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.
Continuous integration for R packages. 
    Automates testing, documentation website building,
    and containerised deployment.
Enables mapping of country level and gridded user datasets.
High resolution vector country boundaries derived from
        Natural Earth data, can be plotted in rworldmap.
Search by keywords in R packages, task views, CRAN, the web and display the results in the console or in txt, html or pdf files. Download the package documentation (html index, README, NEWS, pdf manual, vignettes, source code, binaries) with a single instruction. Visualize the package dependencies and CRAN checks. Compare the package versions, unload and install the packages and their dependencies in a safe order. Explore CRAN archives. Use the above functions for task view maintenance. Access web search engines from the console thanks to 80+ bookmarks. All functions accept standard and non-standard evaluation.
It contains Chinese character frequency data based on 
    news data from 2017 to 2019. Source of these news include Sina, China daily and Tencent.
Allows remote access to satellite image time 
    series provided by the web time series service (WTSS) available 
    at servers such as <https://brazildatacube.dpi.inpe.br/wtss/>. 
    The functions include listing the data sets available in WTSS servers, 
    describing the contents of a data set, and retrieving a time series 
    based on spatial location and temporal filters.
Implements various tests, visualizations, and metrics
    for diagnosing convergence of MCMC chains in phylogenetics.  It implements
    and automates many of the functions of the AWTY package in the R
    environment.
Tools for getting historical weather information and forecasts 
    from wunderground.com. Historical weather and forecast data includes, but 
    is not limited to, temperature, humidity, windchill, wind speed, dew point, 
    heat index. Additionally, the weather underground weather API also includes 
    information on sunrise/sunset, tidal conditions, satellite/webcam imagery, 
    weather alerts, hurricane alerts and historical high/low temperatures.
Visualize your favorite XKCD comic strip directly from
        R. XKCD <https://xkcd.com> web comic content is provided under the Creative
        Commons Attribution-NonCommercial 2.5 License.
Functions which allow to read many XMCDA tags and transform them into R variables which are then usable in MCDA algorithms written in R. It also allows to write certain R variables into XML files respecting the XMCDA standard.
Facilities for running simulations from ordinary
    differential equation ('ODE') models, such as pharmacometrics and other
    compartmental models.  A compilation manager translates the ODE model
    into C, compiles it, and dynamically loads the object code into R for
    improved computational efficiency.  An event table object facilitates
    the specification of complex dosing regimens (optional) and sampling
    schedules.  NB: The use of this package requires both C and
    Fortran compilers, for details on their use with R please see
    Section 6.3, Appendix A, and Appendix D in the "R Administration and
    Installation" manual. Also the code is mostly released under GPL.  The
    'VODE' and 'LSODA' are in the public domain.  The information is available
    in the inst/COPYRIGHTS.
Provides the event table and support functions needed for
   'rxode2' (Wang, Hallow and  James (2016) <doi:10.1002/psp4.12052>) and 'nlmixr2'
    (Fidler et al (2019) <doi:10.1002/psp4.12445>).
    This split will reduce computational burden of recompiling 'rxode2'.
Provides the log-likelihoods with gradients from 'stan'
    (Carpenter et al (2015), <arXiv:1509.07164>) needed
    for generalized log-likelihood estimation in 'nlmixr2'
    (Fidler et al (2019) <doi:10.1002/psp4.12445>). This is
    split of to reduce computational burden of recompiling 'rxode2'
    (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) which runs
    the 'nlmixr2' models during estimation.
Provides the parsing needed for 'rxode2' (Wang, Hallow and
    James (2016) <doi:10.1002/psp4.12052>). It also provides the 'stan'
    based advan linear compartment model solutions with gradients
    (Carpenter et al (2015), <arXiv:1509.07164>) needed in 'nlmixr2'
    (Fidler et al (2019) <doi:10.1002/psp4.12445>).
    This split will reduce computational burden of recompiling 'rxode2'.
Provides the random number generation (in parallel) needed for
   'rxode2' (Wang, Hallow and  James (2016) <doi:10.1002/psp4.12052>) and 'nlmixr2'
    (Fidler et al (2019) <doi:10.1002/psp4.12445>).
    This split will reduce computational burden of recompiling 'rxode2'.
Analysis of combined total and allele specific reads from the reciprocal cross study with RNA-seq data.
Functions are provided to calculate and display ridge TRACE Diagnostics for a 
  variety of alternative Shrinkage Paths. While all methods focus on Maximum Likelihood 
  estimation of unknown true effects under normal distribution-theory, some estimates are 
  modified to be Unbiased or to have "Correct Range" when estimating either [1] the noncentrality 
  of the F-ratio for testing that true Beta coefficients are Zeros or [2] the "relative" MSE 
  Risk (i.e. MSE divided by true sigma-square, where the "relative" variance of OLS is known.) 
  The eff.ridge() function implements the "Efficient Shrinkage Path" introduced in Obenchain
  (2022) <Open Statistics>. This "p-Parameter" Shrinkage-Path always passes through the
  vector of regression coefficient estimates Most-Likely to achieve the overall Optimal
  Variance-Bias Trade-Off and is the shortest Path with this property. Functions eff.aug() and
  eff.biv() augment the calculations made by eff.ridge() to provide plots of the bivariate
  confidence ellipses corresponding to any of the p*(p-1) possible ordered pairs of shrunken
  regression coefficients. Functions for plotting TRACE Diagnostics now have more options.
Provides access to the 'xylib' C library for to import xy 
  data from powder diffraction, spectroscopy and other experimental methods.
Interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).
A legacy version of 'Ryacas', an interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).
Load data from 'Yandex Direct' API V5 
    <https://yandex.ru/dev/direct/doc/dg/concepts/about-docpage> into R.
	Provide function for load lists of campaings, ads, keywords and other 
	objects from 'Yandex Direct' account. Also you can load statistic from
	API 'Reports Service' <https://yandex.ru/dev/direct/doc/reports/reports-docpage>.
	And allows keyword bids management.
'Yandex Translate' (https://translate.yandex.com/) is a statistical machine translation system.
	The system translates separate words, complete texts, and webpages.
	This package can be used to detect language from text and to translate it to supported target language.
	For more info: https://tech.yandex.com/translate/doc/dg/concepts/About-docpage/ .
Allows work with 'Management API' for load counters, segments, filters,
	user permissions and goals list from Yandex Metrica, 'Reporting API' allows you to get 
	information about the statistics of site visits and other data without
	using the web interface, 'Logs API' allows to receive non-aggregated data and 
	'Compatible with Google Analytics Core Reporting API v3' allows 
	receive information about site traffic and other data using field names 
	from Google Analytics Core API.	For more information see official 
	documents <https://yandex.ru/dev/metrika/doc/api2/concept/about-docpage>.
A programmatic interface to web-services of YouTheria. YouTheria is
    an online database of mammalian trait data <http://www.utheria.org/>.
Provide function for get data from 'YouTube Data API' 
    <https://developers.google.com/youtube/v3/docs/>, 'YouTube Analytics API' 
    <https://developers.google.com/youtube/analytics/reference/> and 
    'YouTube Reporting API' <https://developers.google.com/youtube/reporting/v1/reports>.
R interface to the 'Zabbix API' data <https://www.zabbix.com/documentation/3.0/manual/api/reference>. Enables easy and direct communication with 'Zabbix API' from 'R'.
Provides functionality to read settings, statuses and readings of
  weather stations from the 'ZENTRA Cloud' API 
  <https://zentracloud.com/api/v1/guide#APIGuidelines>.
Implements the Zig-Zag algorithm (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> applied and Bouncy Particle Sampler <arXiv:1510.02451> for a Gaussian target and Student distribution.
Interface to the 'ZeroMQ' lightweight messaging kernel (see <https://zeromq.org/> for more information).
Functions to identify Homozygous-by-Descent (HBD) segments associated with runs of homozygosity (ROH) and to
    estimate individual autozygosity (or inbreeding coefficient). HBD segments and autozygosity are assigned to multiple HBD classes
    with a model-based approach relying on a mixture of exponential distributions. The rate of the exponential distribution is distinct
    for each HBD class and defines the expected length of the HBD segments. These HBD classes are therefore related to the age of the
    segments (longer segments and smaller rates for recent autozygosity / recent common ancestor). The functions allow to estimate the
    parameters of the model (rates of the exponential distributions, mixing proportions), to estimate global and local autozygosity
    probabilities and to identify HBD segments with the Viterbi decoding. The method is fully described in Druet and Gautier (2017)
    <doi:10.1111/mec.14324>.
Provides R bindings for Google's s2 library for geometric calculations on
    the sphere. High-performance constructors and exporters provide high compatibility
    with existing spatial packages, transformers construct new geometries from existing
    geometries, predicates provide a means to select geometries based on spatial 
    relationships, and accessors extract information about geometries.
A set of functions used in teaching STATS 201/208 Data Analysis at
    the University of Auckland. The functions are designed to make parts of R more
    accessible to a large undergraduate population who are mostly not statistics
    majors.
The advanced version of package 's2dverification'. It is 
    intended for 'seasonal to decadal' (s2d) climate forecast verification, but 
    it can also be used in other kinds of forecasts or general climate analysis.  
    This package is specially designed for the comparison between the experimental 
    and observational datasets. The functionality of the included functions covers 
    from data retrieval, data post-processing, skill scores against observation, 
    to visualization. Compared to 's2dverification', 's2dv' is more compatible 
    with the package 'startR', able to use multiple cores for computation and 
    handle multi-dimensional arrays with a higher flexibility. The CDO version used
    in development is 1.9.8.
Set of tools to verify forecasts through the computation of typical
    prediction scores against one or more observational datasets or reanalyses (a
    reanalysis being a physical extrapolation of observations that relies on the
    equations from a model, not a pure observational dataset). Intended for seasonal
    to decadal climate forecasts although can be useful to verify other kinds of
    forecasts. The package can be helpful in climate sciences for other purposes
    than forecasting. To find more details, see the review paper Manubens, N.et al. (2018)
    <doi:10.1016/j.envsoft.2018.01.018>.
Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models.  We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.
A S3 resource is provided by Amazon Web Services S3 or a S3-compatible object store (such as Minio). 
    The resource can be a tidy file to be downloaded from the object store, or a data lake (such as Delta Lake) 
    Parquet file to be read by Apache Spark.
Access 'Amazon Web Service Simple Storage Service' ('S3') <https://aws.amazon.com/s3/>
    as if it were a file system. Interface based on the R package 'fs'.
The main function s4vd() performs a biclustering via sparse
        singular value decomposition with a nested stability selection.
        The results is an biclust object and thus all methods of the
        biclust package can be applied.
Perform joint segmentation on two signal dimensions derived from 
    total read depth (intensity) and allele specific read depth (intensity) for 
    whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data.
Calculates a degree of spatial association between regionalizations 
    or categorical maps using the information-theoretical V-measure 
    (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>). It also
    offers an R implementation of the MapCurve method 
    (Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).
Semiparametric empirical likelihood ratio
    based tests of change-point with one-change or epidemic alternatives
    with data-based model diagnostic are contained.
A modular and extendable approach to extract (micro)saccades from gaze samples via an ensemble of methods.
  Although there is an agreement about a general definition of a saccade, the more specific details are harder to agree upon.
  Therefore, there are numerous algorithms that extract saccades based on various heuristics, which differ in the assumptions about velocity,
  acceleration, etc. The package uses three methods (Engbert and Kliegl (2003) <doi:10.1016/S0042-6989(03)00084-1>,
  Otero-Millan et al. (2014)<doi:10.1167/14.2.18>, and Nyström and Holmqvist (2010) <doi:10.3758/BRM.42.1.188>)
  to label individual samples and then applies a majority vote approach to identify saccades. The package includes three
  methods but can be extended via custom functions. It also uses a modular approach to compute velocity and
  acceleration from noisy samples. Finally, you can obtain methods votes per gaze sample instead of saccades.
Computes the Exposure-At-Default based on  the standardized approach
    of CRR2 (SA-CCR). The simplified version of SA-CCR has been included, as well as the OEM methodology.
	Multiple trade types of all the five major asset classes are being supported including the 'Other' Exposure and, given the inheritance-
    based structure of the application, the addition of further trade types
    is straightforward. The application returns a list of trees per Counterparty and CSA after
    automatically separating the trades based on the Counterparty, the CSAs, the hedging sets, the
    netting sets and the risk factors. The basis and volatility transactions are
    also identified and treated in specific hedging sets whereby the corresponding 
    penalty factors are applied. All the examples appearing on the
    regulatory papers (both for the margined and the un-margined workflow) have been
    implemented including the latest CRR2 developments.
Performs surrogate-assisted optimization for expensive black-box constrained problems.
Implementation of the wavelet-based spatial verification method of Buschow and Friederichs "SAD: Verifying the Scale, Anisotropy and Direction of precipitation forecasts" (2020, submitted to QJRMS). Forecasts and Observations are transformed by a decimated or redundant dual-tree complex wavelet transform to analyze the spatial scale, degree of anisotropy and preferred direction in each field. These structural attributes are compared by a series of scores. An experimental algorithm for the correction of these errors is included as well.
We analyzed the nucleotide composition of genes with a special emphasis on stability of DNA sequences. Besides, in a variety of different organisms unequal use of synonymous codons, or codon usage bias, occurs which also show variation among genes in the same genome. Seemingly, codon usage bias is affected by both selective constraints and mutation bias which allows and enables us to examine and detect changes in these two evolutionary forces between genomes or along one genome. Therefore, we determined the codon adaptation index (CAI), effective number of codons (ENC) and codon usage analysis with calculation of the relative synonymous codon usage (RSCU), and subsequently predicted the translation efficiency and accuracy through GC-rich codon usages. Furthermore, we estimated the relative stability of the DNA sequence following calculation of the average free energy (Delta G) and Dimer base-stacking energy level.
Computes the probability of a set of species abundances of a single or multiple samples of individuals with one or more guilds under a mainland-island model. One must specify the mainland (metacommunity) model and the island (local) community model. It assumes that species fluctuate independently. The package also contains functions to simulate under this model. See Haegeman, B. & R.S. Etienne (2017). A general sampling formula for community structure data. Methods in Ecology & Evolution 8: 1506-1519 <doi:10.1111/2041-210X.12807>.
Provides the density, distribution, quantile and generation functions of some obscure probability 
    distributions, including the doubly non-central t, F, Beta, and Eta distributions; 
    the lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of 
    non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares;
    the product of non-central chi-squares to powers; the product of doubly non-central
    F variables; the product of independent normals.
Maximum likelihood tools to fit and compare models of species
    abundance distributions and of species rank-abundance distributions.
Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.
Select best combination of auxiliary variables with certain criterion.
Estimates the parameter of small area in binary data without
             auxiliary variable using Empirical Bayes technique, mainly from Rao 
             and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". 
             This package provides another option of direct estimation using weight.
             This package also features alpha and beta parameter estimation on calculating process of small area.
             Those methods are Newton-Raphson and Moment which based on Wilcox (1979) <doi:10.1177/001316447903900302> 
             and Kleinman (1973) <doi:10.1080/01621459.1973.10481332>.
Provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) <doi:10.2307/2532003>, the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) <doi:10.1093/biostatistics/kxl008> and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) <doi:10.1214/aos/1043351257>.
Mixed Effects Random Forests (MERFs) are a data-driven,
    nonparametric alternative to current methods of Small Area Estimation
    (SAE). 'SAEforest' provides functions for the estimation of regionally
    disaggregated linear and nonlinear indicators using survey sample
    data. Included procedures facilitate the estimation of domain-level
    economic and inequality metrics and assess associated uncertainty.
    Emphasis lies on straightforward interpretation and visualization of results.
    From a methodological perspective, the package builds on approaches discussed in 
    Krennmair and Schmid (2022) <arXiv:2201.10933v2> and Krennmair 
    et al. (2022) <arXiv:2204.10736>.
Provides several functions for area level of small area estimation using hierarchical Bayesian (HB) method with several univariate distributions for variable of interest. The dataset that used in every function is generated accordingly in the Example. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.
We designed this package to provide function for area level of Small Area Estimation using Hierarchical Bayesian (HB) method under Generalized Poisson Distribution. This package provides model using Univariate Generalized Poisson Distribution for variable of interest. Some datasets simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Wang (2021) <doi:10.1016/j.ecoinf.2021.101301> and Ntzoufras (2009) <doi:10.1002/9780470434567>.
We design this package to provide a function for area level of small area estimation using Hierarchical Bayesian (HB) method under Hurdle Negative Binomial Distribution. This package provides model using Univariate Hurdle Negative Binomial Distribution for variable of interest. This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the Hierarchical Bayes estimators which include the mean and the variation of mean. For references, see Hilbe (2011) <doi:10.1017/CBO9780511973420> and Rao (2015) <doi:10.1002/9781118735855>.
Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The 'rjags' package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).
Implementation of Small Area Estimation (SAE) using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error under Beta Distribution. The 'rjags' package is employed to obtain parameter estimates. For the references, see J.N.K & Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Sharon (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).
We designed this package to provide several functions for area level of small area estimation using hierarchical Bayesian (HB) method. This package provides model using panel data for variable interest.This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015).
Several functions are provided for small area estimation at
    the area level using the hierarchical bayesian (HB) method with panel
    data under beta distribution for variable interest. This package also
    provides a dataset produced by data generation. The 'rjags' package is
    employed to obtain parameter estimates. Model-based estimators involve
    the HB estimators, which include the mean and the variation of the
    mean. For the reference, see Rao and Molina (2015, ISBN:
    978-1-118-73578-7).
Provides several functions and datasets for area level of Small Area Estimation under Spatial SAR Model using Hierarchical Bayesian (HB) Method.
We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, and Erciulescu et al.(2019) <doi:10.1111/rssa.12390>.
Provides function for area level of small area estimation using hierarchical Bayesian (HB) method with Zero-Inflated Binomial distribution for variables of interest. Some dataset produced by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean.
We designed this package to provide a function for area level of small area estimation using Hierarchical Bayesian (HB) method under Zero Inflated Negative Binomial Distribution. This package provides model using Univariate Zero Inflated Negative Binomial Distribution for variable of interest. This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean, and the quantile. For the reference, see Rao,J.N.K & Molina (2015) <doi:10.1002/9781118735855>.
Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) <http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.
A set of functions and datasets implementation of small area estimation when auxiliary variable is measured with error. These functions provide a empirical best linear unbiased prediction (EBLUP) estimator and mean squared error (MSE) estimator of the EBLUP. These models were developed by Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>.
The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.
We describe a new R package entitled 'saeMSPE' for the well-known Fay Herriot model and nested error regression model in small area estimation. Based on this package, it is possible to easily compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for these two models.
Methods to fit robust alternatives to commonly used models used in
    Small Area Estimation. The methods here used are based on best linear
    unbiased predictions and linear mixed models. At this time available models
    include area level models incorporating spatial and temporal correlation in
    the random effects.
Tools for the simulation of data in the context of small area
    estimation. Combine all steps of your simulation - from data generation
    over drawing samples to model fitting - in one object. This enables easy
    modification and combination of different scenarios. You can store your
    results in a folder or start the simulation in parallel.
The aim of this package is to offer new methodology for unit-level 
    small area models under transformations and limited population auxiliary 
    information. In addition to this new methodology, the widely used nested 
    error regression model without transformations (see "An Error-Components 
    Model for Prediction of County Crop Areas Using Survey and Satellite Data" 
    by Battese, Harter and Fuller (1988) <doi:10.1080/01621459.1988.10478561>) 
    and its well-known uncertainty estimate (see "The estimation of the mean 
    squared error of small-area estimators" by Prasad and Rao (1990) 
    <doi:10.1080/01621459.1995.10476570>) are provided. In this package, the 
    log transformation and the data-driven log-shift transformation are 
    provided. If a transformation is selected, an appropriate method is chosen 
    depending on the respective input of the population data: Individual 
    population data (see "Empirical best prediction under a nested error model 
    with log transformation" by Molina and Martín (2018) 
    <doi:10.1214/17-aos1608>) but also aggregated population data (see 
    "Estimating regional income indicators under transformations and access to 
    limited population auxiliary information" by Würz, Schmid and Tzavidis 
    <unpublished>) can be entered. Especially under limited data access, new 
    methodologies are provided in 'saeTrafo'. Several options are available to 
    assess the used model and to judge, present and export its results. For a 
    detailed description of the package and the methods used see the 
    corresponding vignette.
Allows users to produce diagnostic procedures and graphic tools for the evaluation of Small Area estimators.
Provides functionality for image processing and shape analysis in 
    the context of reconstructed medical images generated by deep learning-based
    methods or standard image processing algorithms and produced from different
    medical imaging types, such as X-ray, Computational Tomography (CT),
    Magnetic Resonance Imaging (MRI), and pathology imaging. Specifically,
    offers tools to segment regions of interest and to extract quantitative
    shape descriptors for applications in signal processing,
    statistical analysis and modeling, and machine learning.
The aim of the package is to provide some basic functions
        for doing statistics with one dimensional Fuzzy Data (in the
        form of polygonal fuzzy numbers). In particular, the package
        contains functions for the basic operations on the class of
        fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference) 
        as well as for calculating (Bertoluzza) distance and sample variance. 
        Moreover a function to simulate fuzzy random variables and bootstrap tests 
        for the equality of means is included. Version 2.1 fixes some bugs
        of previous versions.
The SAFE Project (<https://www.safeproject.net/>) is a large 
	scale ecological experiment in Malaysian Borneo that explores the impact 
	of habitat fragmentation and conversion on ecosystem function and services. 
	Data collected at the SAFE Project is made available under a common format 
	through the Zenodo data repository and this package makes it easy to 
	discover and load that data into R.
The goal of 'safejoin' is to guarantee that when performing joins extra rows are not added to your data. 'safejoin' provides a wrapper around 'dplyr::left_join' that will raise an error when extra rows are unexpectedly added to your data. This can be useful when working with data where you expect there to be a many to one relationship but you are not certain the relationship holds.
A consistent interface to encrypt and decrypt strings, R objects and files using symmetric and asymmetric key encryption.
Functions to design and apply tests that are anytime valid. The
  functions can be used to design hypothesis tests in the prospective/randomised
  control trial setting or in the observational/retrospective setting. The
  resulting tests remain valid under both optional stopping and optional
  continuation. The current version includes safe t-tests and safe tests of
  two proportions. For details on the theory of safe tests, see
  Grunwald, de Heide and Koolen (2019) "Safe Testing" <arXiv:1906.07801>, 
  for details on safe logrank tests see ter Schure, Perez-Ortiz, Ly and Grunwald
  (2020) "The Safe Logrank Test: Error Control under Continuous Monitoring with 
  Unlimited Horizon" <arXiv:2011.06931v3> and Turner, Ly and Grunwald (2021)
  "Safe Tests and Always-Valid Confidence Intervals for contingency tables and 
  beyond" <arXiv:2106.02693> for details on safe contingency table tests.
A file format for storing tensors that is secure (doesn't allow for 
    code execution), fast and simple to implement. 'safetensors' also enables cross 
    language and cross frameworks compatibility making it an ideal format for 
    storing machine learning model weights.
Contains chart code for monitoring clinical trial safety. Charts can be used as standalone output, but are also designed for use with the 'safetyGraphics' package, which makes it easy to load data and customize the charts using an interactive web-based interface created with Shiny.
Example clinical trial data sets formatted for easy use in R.
A framework for evaluation of clinical trial safety. Users can interactively explore their data using the included 'Shiny' application.
Fork of 'vote_2.3-2', Raftery et al. (2021)
  <DOI:10.32614/RJ-2021-086>, with additional support 
  for stochastic experimentation.
Datasets and functions for the book "Statistiques pour l’économie et la gestion", "Théorie et applications en entreprise", F. Bertrand, Ch. Derquenne, G. Dufrénot, F. Jawadi and M. Maumy, C. Borsenberger editor, (2021, ISBN:9782807319448, De Boeck Supérieur, Louvain-la-Neuve). 
    The first chapter of the book is dedicated to an introduction to statistics and their world. 
    The second chapter deals with univariate exploratory statistics and graphics. 
    The third chapter deals with bivariate and multivariate exploratory statistics and graphics. 
    The fourth chapter is dedicated to data exploration with Principal Component Analysis. 
    The fifth chapter is dedicated to data exploration with Correspondance Analysis.
    The sixth chapter is dedicated to data exploration with Multiple Correspondance Analysis. 
    The seventh chapter is dedicated to data exploration with automatic clustering. 
    The eighth chapter is dedicated to an introduction to probability theory and classical probability distributions.
    The ninth chapter is dedicated to an estimation theory, one-sample and two-sample tests.
    The tenth chapter is dedicated to an Gaussian linear model.
    The eleventh chapter is dedicated to an introduction to time series.
    The twelfth chapter is dedicated to an introduction to probit and logit models.
    Various example datasets are shipped with the package as well as some new functions.
Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) <doi:10.1201/9780429446177>. It also provides some test data generation and plotting functionality to assist with this process.
Highest posterior model is widely accepted as a good model among available models. In terms of variable selection highest posterior model is often the true model. Our stochastic search process SAHPM based on simulated annealing maximization method tries to find the highest posterior model by maximizing the model space with respect to the posterior probabilities of the models. This package currently contains the SAHPM method only for linear models. The codes for GLM will be added in future.
Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.
A new diagram for the verification of vector variables (wind, current, etc) generated by multiple models against a set of observations is presented in this package. It has been designed as a generalization of the Taylor diagram to two dimensional quantities. It is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. The matrix is divided into the part corresponding to the relative rotation and the bias of the empirical orthogonal functions of the data. The full set of diagnostics produced by the analysis of the errors between model and observational vector datasets comprises the errors in the means, the analysis of the total variance of both datasets, the rotation matrix corresponding to the principal components in observation and model, the angle of rotation of model-derived empirical orthogonal functions respect to the ones from observations, the standard deviation of model and observations, the root mean squared error between both datasets and the squared two-dimensional correlation coefficient. See the output of function UVError() in this package.
A coordinate descent algorithm for computing the solution paths of
    the sparse and coupled sparse asymmetric least squares, including the
    (adaptive) elastic net and Lasso penalized SALES and COSALES regressions.
Functions connecting to the 'Salesforce' Platform APIs (REST, SOAP, 
    Bulk 1.0, Bulk 2.0, Metadata, Reports and Dashboards) 
    <https://trailhead.salesforce.com/en/content/learn/modules/api_basics/api_basics_overview>. 
    "API" is an acronym for "application programming interface". Most all calls 
    from these APIs are supported as they use CSV, XML or JSON data that can be 
    parsed into R data structures. For more details please see the 'Salesforce' 
    API documentation and this package's website 
    <https://stevenmmortimer.github.io/salesforcer/> for more information, 
    documentation, and examples.
The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) <doi:10.1080/10618600.2022.2069779>.
The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC)
    sampling of random variables on a simplex. A Self-Adjusting Logit Transform
    (SALT) proposal is used so that sampling is still efficient even in difficult
    cases, such as those in high dimensions or with parameters that differ by orders
    of magnitude. Special care is also taken to maintain accuracy even when some
    coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are
    included in the package, enabling easy assessment of the convergence and mixing
    of the chain within the constrained space.
Take real or simulated data and salt it with errors commonly 
    found in the wild, such as pseudo-OCR errors, Unicode problems, numeric 
    fields with nonsensical punctuation, bad dates, etc.
Computationally efficient tools for high dimensional predictive
        modeling (regression and classification). SAM is short for sparse 
        additive modeling, and adopts the computationally efficient basis 
        spline technique. We solve  the optimization problems by various 
        computational algorithms including the block coordinate descent 
        algorithm, fast iterative soft-thresholding algorithm, and newton method. 
        The computation is further accelerated by warm-start and active-set tricks.
An R API providing access to a relational database with macroeconomic time series data for South Africa,
 obtained from the South African Reserve Bank (SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis
 via the EconData <https://www.econdata.co.za/> platform and automated scraping of the SARB and STATSSA websites.
 The database is maintained at the Department of Economics at Stellenbosch University.
Health research using data from electronic health records (EHR) has gained
    popularity, but misclassification of EHR-derived disease status and lack of
    representativeness of the study sample can result in substantial bias in
    effect estimates and can impact power and type I error for association
    tests. Here, the assumed target of inference is the relationship between
    binary disease status and predictors modeled using a logistic regression
    model. 'SAMBA' implements several methods for obtaining bias-corrected
    point estimates along with valid standard errors as proposed in Beesley and
    Mukherjee (2020) <doi:10.1101/2019.12.26.19015859>, currently under review.
A collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>. The methods may be used for further purposes where weighting and generation of new observations is needed.
Implements functions for working with absorbing Markov chains. The
    implementation is based on the framework described in "Toward a unified
    framework for connectivity that disentangles movement and mortality in space
    and time" by Fletcher et al. (2019) <doi:10.1111/ele.13333>, which applies
    them to spatial ecology. This framework incorporates both resistance and 
    absorption with spatial absorbing Markov chains (SAMC) to provide several
    short-term and long-term predictions for metrics related to connectivity in 
    landscapes. Despite the ecological context of the framework, this package
    can be used in any application of absorbing Markov chains.
Design a Bayesian seamless multi-arm biomarker-enriched phase II/III 
             design with the survival endpoint with allowing sample size re-estimation.
             James M S Wason, Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D Brenton, Helena M Earl, Adrian P Mander (2015) <doi:10.1038/bjc.2015.278>.
             Guosheng Yin, Nan Chen, J. Jack Lee (2018) <doi:10.1007/s12561-017-9199-7>.
             Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016) <doi:10.1002/sim.6971>.
A novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data.
This program can be used to fit Gaussian linear mixed models (LMM). Univariate and multivariate response models, multiple variance components, as well as, certain correlation and covariance structures are supported. In many occasions, the user can pick one of the several mixed model fitting algorithms, which are explained further in the details section. Some algorithms are specific to certain types of models (univariate or multivariate, diagonal or non-diagonal residual, one or multiple variance components, etc,...).
Computes the required sample size using the optimal designs with multiple constraints proposed in Mayo et al.(2010). This optimal method is designed for two-arm, randomized phase II clinical trials, and the required sample size can be optimized either using fixed or flexible randomization allocation ratios.
Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs.
Two-step
   and maximum likelihood estimation
   of Heckman-type sample selection models:
   standard sample selection models (Tobit-2),
   endogenous switching regression models (Tobit-5),
   sample selection models with binary dependent outcome variable,
   interval regression with sample selection (only ML estimation),
   and endogenous treatment effects models.
   These methods are described in the three vignettes
   that are included in this package 
   and in econometric textbooks such as
   Greene (2011, Econometric Analysis, 7th edition, Pearson).
Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties.
There are four categories of Phase III clinical trials according to different research goals, including (1) Testing for equality, (2) Superiority trial, (3) Non-inferiority trial, and (4) Equivalence trial. This package aims to help researchers to calculate sample size when comparing means or proportions in Phase III clinical trials with different research goals.
Computes the required sample size for estimation of totals, means
    and proportions under complex sampling designs.
Calculates the power and sample size for Cochran-Mantel-Haenszel tests. 
    There are also several helper functions for working with probability,
    odds, relative risk, and odds ratio values.
To determine sample size or power for case-control studies to be analyzed using logistic regression.
Sample size requirements calculation
        using three different Bayesian criteria in the
        context of designing an experiment to estimate a normal mean or
        the difference between two normal means.  Functions for
        calculation of required sample sizes for the Average Length
        Criterion, the Average Coverage Criterion and the Worst Outcome
        Criterion in the context of normal means are provided.
        Functions for both the fully Bayesian and the mixed
        Bayesian/likelihood approaches are provided.
        For reference see Joseph L. and B\'elisle P. (1997) <https://www.jstor.org/stable/2988525>.
Sample size requirements calculation
        using three different Bayesian criteria in the
        context of designing an experiment to estimate the difference
        between two binomial proportions. Functions for calculation of
        required sample sizes for the Average Length Criterion, the
        Average Coverage Criterion and the Worst Outcome Criterion in
        the context of binomial observations are provided. In all
        cases, estimation of the difference between two binomial
        proportions is considered. Functions for both the fully
        Bayesian and the mixed Bayesian/likelihood approaches are
        provided.
        For reference see Joseph L., du Berger R. and B\'elisle P. (1997) <doi:10.1002/(sici)1097-0258(19970415)16:7%3C769::aid-sim495%3E3.0.co;2-v>.
Affords researchers the ability to draw stratified samples from the U.S. Department of Veteran's Affairs/Department of Defense Identity Repository (VADIR) database according to a variety of population characteristics. The VADIR database contains information for all veterans who were separated from the military after 1980. The central utility of the present package is to integrate data cleaning and formatting for the VADIR database with the stratification methods described by Mahto (2019) <https://CRAN.R-project.org/package=splitstackshape>. Data from VADIR are not provided as part of this package.
Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators.
Select sampling methods for probability samples using large data sets.  This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer.
Sampling procedures from the book 'Stichproben - Methoden und praktische Umsetzung mit R' by Goeran Kauermann and Helmut Kuechenhoff (2010).
Package provides the possibility to sampling complete datasets 
  from a normal distribution to simulate cluster randomized trails for different study designs.
Functions to estimate from survey data. This package is a user-friendly wrapper of the samplingVarEst package. It considers that the user is more familiar with practical survey data rather than with research on survey sampling (variance estimation). More functionalities are on the way.
Functions to take samples of data, sample size estimation and getting useful estimators such as total, mean, proportion about its population using simple random, stratified, systematic and cluster sampling.
In the field of stratified sampling design, this package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample cost under the condition to satisfy precision constraints in a multivariate and multidomain case. This approach is based on the use of the genetic algorithm: each solution (i.e. a particular partition in strata of the sampling frame) is considered as an individual in a population; the fitness of all individuals is evaluated applying the Bethel-Chromy algorithm to calculate the sampling size satisfying precision constraints on the target estimates. Functions in the package allows to: (a) analyse the obtained results of the optimisation step; (b) assign the new strata labels to the sampling frame; (c) select a sample from the new frame accordingly to the best allocation. Functions for the execution of the genetic algorithm are a modified version of the functions in the 'genalg' package. M.Ballin, G.Barcaroli (2020) <arXiv:2004.09366> "R package SamplingStrata: new developments and extension to Spatial Sampling".
Functions to calculate some point estimators and estimate their variance under unequal probability sampling without replacement. Single and two-stage sampling designs are considered. Some approximations for the second-order inclusion probabilities (joint inclusion probabilities) are available (sample and population based). A variety of Jackknife variance estimators are implemented. Almost every function is written in C (compiled) code for faster results. The functions incorporate some performance improvements for faster results with large datasets.
Estimation of the required sample size to validate a risk model for binary outcomes, based on the sample size equations proposed by Pavlou et al. (2021) <doi:10.1177/09622802211007522>. For precision-based sample size calculations, the user is required to enter the anticipated values of the C-statistic and outcome prevalence, which can be obtained from a previous study. The user also needs to specify the required precision (standard error) for the C-statistic, the calibration slope and the calibration in the large. The calculations are valid under the assumption of marginal normality for the distribution of the linear predictor.
Significance Analysis of Microarrays for differential expression analysis, RNAseq data and related problems.
Markov chain Monte Carlo samplers for posterior simulations of conjugate Bayesian nonparametric
    mixture models. Functionality is provided for Gibbs sampling as in Algorithm 3 of Neal (2000)
    <DOI:10.1080/10618600.2000.10474879>, restricted Gibbs merge-split sampling as described in Jain & Neal
    (2004) <DOI:10.1198/1061860043001>, and sequentially-allocated merge-split sampling <DOI:10.1080/00949655.2021.1998502>, as well as
    summary and utility functions.
Simulation tools for closed-loop simulation are provided for the 'MSEtool' operating model to inform data-rich fisheries. 
  'SAMtool' provides a conditioning model, assessment models of varying complexity with standardized reporting, 
  model-based management procedures, and diagnostic tools for evaluating assessments inside closed-loop simulation.
A sensitivity analysis approach for unmeasured confounding in observational data with multiple treatments and a binary outcome. This approach derives the general bias formula and provides adjusted causal effect estimates in response to various assumptions about the degree of unmeasured confounding. Nested multiple imputation is embedded within the Bayesian framework to integrate   uncertainty about the sensitivity parameters and sampling variability.  Bayesian Additive Regression Model (BART) is used for outcome modeling. The causal estimands are the conditional average treatment effects (CATE) based on the risk difference.  For more details, see paper: Hu L et al. (2020) A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data <arXiv:2012.06093>.
Augmenting a matched data set by generating multiple stochastic, matched samples from the data using a
  multi-dimensional histogram constructed from dropping the input matched data into a multi-dimensional grid built on
  the full data set. The resulting stochastic, matched sets will likely provide a collectively higher coverage of the full
  data set compared to the single matched set. Each stochastic match is without duplication, thus allowing downstream
  validation techniques such as cross-validation to be applied to each set without concern for overfitting.
Provides a variety of original and flexible user-friendly 
    statistical latent variable models and unsupervised learning algorithms to 
    segment and represent time-series data (univariate or multivariate), and 
    more generally, longitudinal data, which include regime changes. 
    'samurais' is built upon the following packages, each of them is an 
    autonomous time-series segmentation approach: Regression with Hidden 
    Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'), 
    Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), Piece-Wise 
    regression ('PWR'). For the advantages/differences of each of them, the 
    user is referred to our mentioned paper references.
Data sets and code blocks for the book 'Statistical Analysis of 
  Network Data with R, 2nd Edition'.
A flexible framework for definition and application of time/depth-
    based rules for sets of parameters for single grains that can be used to 
    create artificial sediment profiles. Such profiles can be used for virtual 
    sample preparation and synthetic, for instance, luminescence measurements.
Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic 
             robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC)
	     covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC)
	     covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators);
	     clustered covariances (one-way and multi-way); panel and panel-corrected covariances;
	     outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are
	     applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted
	     to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>,
	     Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.
Spatial prediction is a common practice in social and environmental science. This package enables the implementation of SSH-based spatial prediction proposed by Wang et al. (2013) <doi:10.1068/a44710>. It provides functions to (1) evaluate stratification schemes, (2) interpolate sampling data over user-defined reporting units, (3) assess prediction uncertainties, and (4) evaluate overall accuracy using the k-fold cross-validation estimate.
Downloads and tidies the San Francisco Public Utilities Commission Beach Water Quality Monitoring Program data. Data sets can be downloaded per beach, or the raw data can be downloaded. See <https://sfwater.org/cfapps/lims/beachmain1.cfm>.
Created for population health analytics and monitoring.
    The functions in this package work best when working with patient level Master Patient Index-like datasets .
    Built to be used by  NHS bodies and other health service providers.
Routines for solving large systems of linear equations in R.
  Direct and iterative solvers from the Eigen C++ library are made available.
  Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate
  Gradient, BiCGSTAB). Both dense and sparse problems are supported.
Recent gcc and clang compiler versions provide functionality to
 test for memory violations and other undefined behaviour; this is often 
 referred to as "Address Sanitizer" (or 'ASAN') and "Undefined Behaviour 
 Sanitizer" ('UBSAN'). The Writing R Extension manual describes this in some
 detail in Section 4.3 title "Checking Memory Access".

   This feature has to be enabled in the corresponding binary, eg in R, which
 is somewhat involved as it also required a current compiler toolchain which 
 is not yet widely available, or in the case of Windows, not available at all
 (via the common Rtools mechanism).

   As an alternative, pre-built Docker containers such as the Rocker container
 'r-devel-san' or the multi-purpose container 'r-debug' can be used.

   This package then provides a means of testing the compiler setup as the
 known code failures provides in the sample code here should be detected
 correctly, whereas a default build of R will let the package pass.

   The code samples are based on the examples from the Address Sanitizer
 Wiki at <https://github.com/google/sanitizers/wiki>.
During the preparation of data set(s) one usually performs
    some sanity checks. The idea is that irrespective of where the
    checks are performed, they are centralized by this package in order
    to list all at once with examples if a check failed.
Plots that illustrate the flow of information or material.
By binding R functions and the 'Highcharts' <http://www.highcharts.com/> charting library, 'sankeywheel' package provides a simple way to draw dependency wheels and sankey diagrams.
There are several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.
Machine learning is widely used in information-systems design. Yet, training algorithms on imbalanced datasets may severely affect performance on unseen data. For example, in some cases in healthcare, financial, or internet-security contexts, certain sub-classes are difficult to learn because they are underrepresented in training data. This 'R' package offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm ('SANSA'), which, in contrast to other solutions, introduces a novel “placement” parameter that can be tuned to adapt to each datasets unique manifestation of the imbalance. More information about the algorithm's parameters can be found at Nasir et al. (2022) <https://murtaza.cc/SANSA/>.
A graphical and automated pipeline for the analysis 
		of short time-series in R ('santaR'). This approach is designed to accommodate asynchronous 
		time sampling (i.e. different time points for different individuals), 
		inter-individual variability, noisy measurements and large numbers of variables. 
		Based on a smoothing splines functional model, 'santaR' is able to detect variables
		highlighting significantly different temporal trajectories between study groups.
		Designed initially for metabolic phenotyping, 'santaR' is also suited for other Systems Biology 
		disciplines. Command line and graphical analysis (via a 'shiny' application) enable fast and
		parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting
		options for non-specialist users.
A tool for cutting data into intervals. Allows singleton intervals.
  Always includes the whole range of data by default. Flexible labelling. 
  Convenience functions for cutting by quantiles etc. Handles dates, times, units
  and other vectors.
Inspired by the art and color research of Sanzo Wada (1883-1967), 
    his "Dictionary Of Color Combinations" (2011, ISBN:978-4861522475), and the 
    interactive site by Dain M. Blodorn Kim <https://github.com/dblodorn/sanzo-wada>, 
    this package brings Wada's color combinations to R for easy use in data 
    visualizations. This package honors 60 of Wada's color combinations: 
    20 duos, 20 trios, and 20 quads.
This analytic is an in initial foray into sentiment analysis.  
    This analytic will allow a user to access the Twitter API (once they create 
    their own developer account), ingest tweets of their interest, clean / tidy 
    data, perform topic modeling if interested, compute sentiment scores 
    utilizing the Bing Lexicon, and output visualizations.
The Hypothesis tests for the means of independent or paired groups.
    This package investigates the normality assumption automatically.
    Then, it tests the hypothesis tests for two independent or paired group means
    by using parametric or non-parametric tests. It uses the Shapiro-Wilk test to
    test the normality assumption. For independent two groups, If data comes from
    the normal distribution, the package uses the Z or t-test according to whether
    variances are known. For paired groups, it uses paired t-test under normal data
    sets. If data does not come from the normal distribution, the package uses the
    Wilcoxon test for independent and paired cases.
Implementation of SAPEVO-M, a Group Ordinal Method for Multiple Criteria Decision-Making (MCDM). SAPEVO-M is an acronym for Simple Aggregation of Preferences Expressed by Ordinal Vectors Group Decision Making. This method provides alternatives ranking given decision makers' preferences: criteria preferences and alternatives preferences for each criterion.This method is described in Gomes et al. (2020) <doi: 10.1590/0101-7438.2020.040.00226524 >.
Access, modify, aggregate and plot data from the 'Sapfluxnet' project
  (<http://sapfluxnet.creaf.cat>), the first global database of sap flow measurements.
Functions for statistical analysis of point processes.
A collection of tools to access prepared air quality monitoring
    data files from web servers with ease and speed. Air quality data are 
    sourced from open and publicly accessible repositories and can be found in 
    these locations: 
    <https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8> 
    and <https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm>. The web 
    server space has been provided by Ricardo Energy & Environment.
'Smart Adaptive Recommendations' (SAR) is the name of a fast, scalable, adaptive algorithm for personalized recommendations based on user transactions and item descriptions. It produces easily explainable/interpretable recommendations and handles "cold item" and "semi-cold user" scenarios. This package provides two implementations of 'SAR': a standalone implementation, and an interface to a web service in Microsoft's 'Azure' cloud: <https://github.com/Microsoft/Product-Recommendations/blob/master/doc/sar.md>. The former allows fast and easy experimentation, and the latter provides robust scalability and extra features for production use.
A Graphical user interface to calculate the rainfall-runoff relation using the Natural Resources Conservation Service - Curve Number method (NRCS-CN method) but include modifications by Hawkins et al., (2002) about the Initial Abstraction. This GUI follows the programming logic of a previously published software (Hernandez-Guzman et al., 2011)<doi:10.1016/j.envsoft.2011.07.006>. It is a raster-based GIS tool that outputs runoff estimates from Land use/land cover and hydrologic soil group maps.
    This package has already been published in Journal of Hydroinformatics (Hernandez-Guzman et al., 2021)<doi:10.2166/hydro.2020.087> but it is under constant development at the Institute about Natural Resources Research (INIRENA) from the Universidad Michoacana de San Nicolas de Hidalgo and represents a collaborative effort between the Hydro-Geomatic Lab (INIRENA) with the Environmental Management Lab (CIAD, A.C.).
Functions, classes and methods for time series modelling with ARIMA
    and related models. The aim of the package is to provide consistent
    interface for the user. For example, a single function autocorrelations()
    computes various kinds of theoretical and sample autocorrelations. This is
    work in progress, see the documentation and vignettes for the current
    functionality.  Function sarima() fits extended multiplicative seasonal
    ARIMA models with trends, exogenous variables and arbitrary roots on the
    unit circle, which can be fixed or estimated (for the algebraic basis for
    this see <arXiv:2208.05055>, a paper on the methodology is being prepared).
    The suggested package 'FitARMA' can be installed with
    'remotes::install_github("cran/FitARMA")' if necessary but is no longer
    needed in normal use.
Provides a set of functions to interpret changes in
 compositional data based on a network representation of all pairwise ratio
 comparisons: computation of all pairwise ratio, construction of a
 p-value matrix of all pairwise tests of these ratios between
 conditions, conversion of this matrix to a network.
Provides a set of basic functions for creating Moodle XML
 output files suited for importing questions in Moodle (a learning
 management system, see <https://moodle.org/> for more information).
Analysis and plotting tools for snow profile data produced from manual snowpack 
  observations and physical snowpack models. The functions in this package support snowpack 
  and avalanche research by reading various formats of data (including CAAML, SMET,
  generic csv, and outputs from the snow cover model SNOWPACK), manipulate the data, and 
  produce graphics such as stratigraphy and time series profiles. Package developed by 
  the Simon Fraser University Avalanche Research Program <http://www.avalancheresearch.ca>. 
  Graphics apply visualization concepts from Horton, Nowak, and Haegeli (2020, 
  <doi:10.5194/nhess-20-1557-2020>).
Snow profiles describe the vertical (1D) stratigraphy of layered 
    snow with different layer characteristics, such as grain type, hardness, 
    deposition date, and many more. Hence, they represent a data format similar 
    to multivariate time series containing categorical, ordinal, and numerical 
    data types. Use this package to align snow profiles by matching their 
    individual layers based on Dynamic Time Warping (DTW). The aligned profiles 
    can then be assessed with an independent, global similarity measure that is 
    geared towards avalanche hazard assessment. Finally, through exploiting data
    aggregation and clustering methods, the similarity measure provides the
    foundation for grouping and summarizing snow profiles according to similar
    hazard conditions. In particular, this package allows for averaging large
    numbers of snow profiles with DTW Barycenter Averaging and thereby 
    facilitates the computation of individual layer distributions and summary 
    statistics that are relevant for avalanche forecasting purposes. 
    For more background information refer to Herla, Horton, Mair,
    and Haegeli (2021) <doi:10.5194/gmd-14-239-2021>, and Herla, Mair, and Haegeli 
    (2022) <doi:10.5194/tc-16-3149-2022>.
The development of post-processing functionality for simulated snow profiles 
    by the snow and avalanche community is often done in 'python'. This package
    aims to make these tools accessible to 'R' users. Currently integrated modules 
    contain functions to calculate dry snow layer instabilities in support of
    avalache hazard assessments following the publications of  Richter, Schweizer, 
    Rotach, and Van Herwijnen (2019) <doi:10.5194/tc-13-3353-2019>, and Mayer, 
    Van Herwijnen, Techel, and Schweizer (2022) <doi:10.5194/tc-2022-34>.
Implements the basic elements of the multi-model
    inference paradigm for up to twenty species-area relationship models (SAR), using simple
    R list-objects and functions, as in Triantis et al. 2012 <DOI:10.1111/j.1365-2699.2011.02652.x>.
    The package is scalable and users can easily create their own model and data objects. Additional
    SAR related functions are provided.
A toolkit for Partially Observed Markov Decision Processes (POMDP). Provides
    bindings to C++ libraries implementing the algorithm SARSOP (Successive Approximations
    of the Reachable Space under Optimal Policies) and described in Kurniawati et al (2008),
    <doi:10.15607/RSS.2008.IV.009>.  This package also provides a high-level interface
    for generating, solving and simulating POMDP problems and their solutions.
Documentation and prototypes for the earliest (circa 2010) open-source effort to reverse engineer the sas7bdat file format. The package includes a prototype reader for sas7bdat files. However, newer packages (notably the haven package) contain more robust readers for sas7bdat files.
Using any importation code designed for 'SAS' users to read ASCII files into 'sas7bdat' files, this package parses through the INPUT block of a '.sas' syntax file to design the parameters needed for a 'read.fwf()' function call.  This allows the user to specify the location of the ASCII (often a '.dat') file and the location of the 'SAS' syntax file, and then load the data frame directly into R in just one step.
Converts the dates to different 'SAS' date formats. In 'SAS' dates are a special case of 
             numeric values. Each day is assigned a specific numeric value, starting from January 1, 1960.
             This date is assigned the date value 0, and the next date has a date value of 1 and so on.
             The previous days to this date are represented by -1 , -2 and so on. With this approach, 
             'SAS' can represent any date in the future or any date in the past. There are many date 
             formats used in 'SAS' to represent date-time. Here, we try to develop functions which will
             convert the date to different 'SAS' date formats.
Implements the sparse and smooth functional clustering (SaS-Funclust) method (Centofanti et al. (2021) <arXiv:2103.15224>) that aims to classify a sample of curves into homogeneous groups while jointly detecting the most informative portions of domain.
This is a core implementation of 'SAS' procedures for linear models - GLM, REG, ANOVA, TTEST, FREQ, and UNIVARIATE. Some R packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of 'SAS.' Different results does not necessarily mean incorrectness. However, many wants the same results to SAS. This package aims to achieve that. 
             Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).
A static code analysis tool for 'SAS' scripts. It is designed to load, count, extract, remove, and summarise components of 'SAS' code.
Settings and functions to extend the 'knitr' 'SAS' engine.
Data sets and sample lmer analyses corresponding
  to the examples in Littell, Milliken, Stroup and Wolfinger
  (1996), "SAS System for Mixed Models", SAS Institute.
Provides a 'SAS' interface, through 'SASPy'(<https://sassoftware.github.io/saspy/>) and 'reticulate'(<https://rstudio.github.io/reticulate/>).
  This package helps you create 'SAS' sessions, execute 'SAS' code in remote 'SAS' servers, retrieve execution results and log, and exchange datasets between 'SAS' and 'R'.
  It also helps you to install 'SASPy' and create a configuration file for the connection. Please review the 'SASPy' license file as instructed so that you comply with its separate and independent license.
An 'SCSS' compiler, powered by the 'LibSass' library. With this,
    R developers can use variables, inheritance, and functions to generate
    dynamic style sheets. The package uses the 'Sass CSS' extension language,
    which is stable, powerful, and CSS compatible.
A meta-package that aims to make 'R' easier for everyone, 
    especially programmers who have a background in 'SAS®' software.
    This set of packages brings many useful concepts to 'R', including
    data libraries, data dictionaries, formats 
    and format catalogs, a data step, and a traceable log.  The 'flagship'
    package is a reporting package that can output in text, rich text, 'PDF', 
    'HTML', and 'DOCX' file formats.
Tools for analyzing tail dependence in any sample or in particular theoretical models.  The package uses only theoretical and non parametric methods, without inference.  The primary goals of the package are to provide: (a)symmetric multivariate extreme value models in any dimension;  theoretical and empirical indices to order tail dependence;  theoretical and empirical graphical methods to visualize tail dependence.
Bundles functions used to analyze the harmfulness of trial errors in criminal trials.
    Functions in the Scientific Analysis of Trial Errors ('SATE') package help users estimate the 
    probability that a jury will find a defendant guilty given jurors' preferences for a guilty 
    verdict and the uncertainty of that estimate. Users can also compare actual and hypothetical 
    trial conditions to conduct harmful error analysis. The relationship between individual jurors' 
    verdict preferences and the probability that a jury returns a guilty verdict has been studied 
    by Davis (1973) <doi:10.1037/h0033951>; MacCoun & Kerr (1988) <doi:10.1037/0022-3514.54.1.21>, 
    and Devine et el. (2001) <doi:10.1037/1076-8971.7.3.622>, among others.
Herein, we provide a broad variety of functions which are useful
    for handling, manipulating, and visualizing satellite-based remote sensing 
    data. These operations range from mere data import and layer handling (eg 
    subsetting), over Raster* typical data wrangling (eg crop, extend), to more 
    sophisticated (pre-)processing tasks typically applied to satellite imagery 
    (eg atmospheric and topographic correction). This functionality is 
    complemented by a full access to the satellite layers' metadata at any 
    stage and the documentation of performed actions in a separate log file. 
    Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8 
    (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat 
    Global Land Survey data set.
With 'satin' functions, visualisation, data extraction and further analysis like producing climatologies from several images, and anomalies of satellite derived ocean data can be easily done.  Reading functions can import a user defined geographical extent of data stored in netCDF files.  Currently supported ocean data sources include NASA's Oceancolor web page <https://oceancolor.gsfc.nasa.gov/>, sensors VIIRS-SNPP; MODIS-Terra; MODIS-Aqua; and SeaWiFS.  Available variables from this source includes chlorophyll concentration, sea surface temperature (SST), and several others.  Data sources specific for SST that can be imported too includes Pathfinder AVHRR <https://www.ncei.noaa.gov/products/avhrr-pathfinder-sst> and GHRSST <https://www.ghrsst.org/>.  In addition, ocean productivity data produced by Oregon State University <http://sites.science.oregonstate.edu/ocean.productivity/> can also be handled previous conversion from HDF4 to HDF5 format.  Many other ocean variables can be processed by importing netCDF data files from two European Union's Copernicus Marine Service databases <https://marine.copernicus.eu/>, namely Global Ocean Physical Reanalysis and Global Ocean Biogeochemistry Hindcast.
Supports the generation of maps based on the results from 
     'SaTScan' (TM) cluster analysis.
     The package handles mapping of Spatial and Spatial-Time analysis using
     the discrete Poisson, Bernoulli, and exponential models of case data generating
     cluster and location ('GIS') records containing observed, expected and observed/expected
     ratio for U. S. states (and DC), counties or census tracts of individual 
     states based on the U. S. 'FIPS' codes for state, county and census tracts 
     (locations) using 2000 or 2010 Census areas, 'FIPS' codes, and boundary data.
     'satscanMapper' uses the 'SeerMapper' package for the boundary data and 
     mapping of locations.  Not all of the 'SaTScan' (TM) analysis and models generate
     the observed, expected and observed/expected ratio values for the clusters and 
     locations.
     The user can map the observed/expected ratios for locations 
     (states, counties, or census tracts) for each cluster with a p-value less than 0.05 
     or a user specified p-value.  
     The locations are categorized and colored based on either the cluster's Observed/Expected 
     ratio or the locations' Observed/Expected ratio. 
     The place names are provided for each census tract using data from 'NCI', the 'HUD' crossover 
     tables (Tract to Zip code) as of December, 2013, the USPS Zip code 5 database for 1999, 
     and manual look ups on the USPS.gov web site.
Bayesian inference of graphical model structures using spanning trees.
Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.
An implementation of a regularized regression prediction and 
    empirical Bayes method to recover the true gene expression profile in 
    noisy and sparse single-cell RNA-seq data. See Huang M, et al (2018) 
    <doi:10.1038/s41592-018-0033-z> for more details.
Provides a toolbox that allows the user to implement accessibility related concepts.
The SAWNUTI algorithm performs sequence comparison for finite sequences of discrete events with non-uniform time intervals. Further description of the algorithm can be found in the paper: A. Murph, A. Flynt, B. R. King (2021). Comparing finite sequences of discrete events with non-uniform time intervals, Sequential Analysis, 40(3), 291-313. <doi:10.1080/07474946.2021.1940491>.
Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.
Spectral and Average Autocorrelation Zero Distance Density
    ('sazed') is a method for estimating the season length of a 
    seasonal time series. 'sazed' is aimed at practitioners, as it employs only 
    domain-agnostic preprocessing and does not depend on parameter tuning or 
    empirical constants. The computation of 'sazed' relies on the efficient 
    autocorrelation computation methods suggested by Thibauld Nion (2012, URL: 
    <https://etudes.tibonihoo.net/literate_musing/autocorrelations.html>) and by 
    Bob Carpenter (2012, URL: 
    <https://lingpipe-blog.com/2012/06/08/autocorrelation-fft-kiss-eigen/>).
Get the most appropriate autoregressive integrated moving average, generalized auto-regressive conditional heteroscedasticity and Markov switching GARCH model. For method details see Haas M, Mittnik S, Paolella MS (2004). <doi:10.1093/jjfinec/nbh020>, Bollerslev T (1986). <doi:10.1016/0304-4076(86)90063-1>.
Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 <doi: 10.1002/sim.8549>.
An MCMC algorithm for simultaneous feature selection and classification, 
    and visualization of the selected features and feature interactions. 
    An implementation of SBFC by Krakovna, Du and Liu (2015), <arXiv:1506.02371>.
Estimation and inference for parameters in a Gaussian copula model,
        treating the univariate marginal distributions as nuisance
        parameters as described in Hoff (2007) <doi:10.1214/07-AOAS107>. 
        This package also provides a
        semiparametric imputation procedure for missing multivariate
        data.
Computes the sBIC for various singular model collections including:
    binomial mixtures, factor analysis models, Gaussian mixtures,
    latent forests, latent class analyses, and reduced rank regressions.
This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review).
   In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. 
   Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.
sbioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction
  networks with spatial effects.
  Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions are defined.
  For deterministic solutions, sbioPN creates the associated system of differential equations "on the fly", and
  solves it with a Runge Kutta Dormand Prince 45 explicit algorithm.
  For stochastic solutions, sbioPN offers two variants of Gillespie algorithm, or SSA.
  For hybrid deterministic/stochastic,
  it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions.
  sbioPN algorithms are developed in C to achieve adequate performance.
Implements sparse Bayesian learning method for QTL mapping and genome-wide association studies.
A collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). 
  Supports at the moment Simple, Bipartite, 'Multipartite' and Multiplex SBM (undirected or directed with Bernoulli,
  Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM).
  See Léger (2016) <arxiv:1602.07587>, 'Barbillon et al.' (2020) <doi:10.1111/rssa.12193> and 
  'Bar-Hen et al.' (2020) <arxiv:1807.10138>.
Median-of-means is a generic yet powerful framework for scalable and robust estimation. A framework for Bayesian analysis is called M-posterior, which estimates a median of subset posterior measures. For general exposition to the topic, see the paper by Minsker (2015) <doi:10.3150/14-BEJ645>.
An ADMM implementation of SDP-1, a semidefinite programming relaxation of the maximum likelihood estimator for fitting a block model. SDP-1 has a tendency to produce equal-sized blocks and is ideal for producing a form of network histogram approximating a nonparametric graphon model. Alternatively, it can be used for community detection. (This is experimental code, proceed with caution.)
Inference in a Bayesian framework for a generalised stochastic block model. The generalised stochastic block model (SBM) can capture group structure in network data without requiring conjugate priors on the edge-states. Two sampling methods are provided to perform inference on edge parameters and block structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet process sampler. Green, Richardson (2001) <doi:10.1111/1467-9469.00242>; Neal (2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019) <arXiv:1909.09421>.
Generate Stochastic Branching Networks ('SBNs'). Used to model the branching structure of rivers.
Utilities for training and evaluating text predictors based on Stupid Back-Off N-gram models (Brants et al., 2007, <https://www.aclweb.org/anthology/D07-1090/>).
Creates classifier for binary outcomes using Adaptive Boosting 
    (AdaBoost) algorithm on decision stumps with a fast C++ implementation. 
    For a description of AdaBoost, see Freund and Schapire (1997) 
    <doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but
    easy to interpret and visualize. Feature vectors may be a combination of
    continuous (numeric) and categorical (string, factor) elements. Methods 
    for classifier assessment, predictions, and cross-validation also included.
Calculates a Satorra-Bentler scaled chi-squared difference test between nested models that were estimated using maximum likelihood (ML) with robust standard errors, which cannot be calculated the traditional way. For details see Satorra & Bentler (2001) <doi:10.1007/bf02296192> and Satorra & Bentler (2010) <doi:10.1007/s11336-009-9135-y>. This package may be particularly helpful when used in conjunction with 'Mplus' software, specifically when implementing the complex survey option. In such cases, the model estimator in 'Mplus' defaults to ML with robust standard errors.
Tools for interacting with U.S. Geological Survey ScienceBase
    <https://www.sciencebase.gov> interfaces. ScienceBase is a data cataloging and
    collaborative data management platform. Functions included for querying
    ScienceBase, and creating and fetching datasets.
Implements the Stable Balancing Weights by Zubizarreta (2015) <DOI:10.1080/01621459.2015.1023805>. These are the weights of minimum variance that approximately balance the empirical distribution of the observed covariates. For an overview, see Chattopadhyay, Hase and Zubizarreta (2020) <DOI:10.1002/(ISSN)1097-0258>. To solve the optimization problem in 'sbw', the default solver is 'quadprog', which is readily available through CRAN. The solver 'osqp' is also posted on CRAN. To enhance the performance of 'sbw', users are encouraged to install other solvers such as 'gurobi' and 'Rmosek', which require special installation. For the installation of gurobi and pogs, please follow the instructions at <https://www.gurobi.com/documentation/9.1/quickstart_mac/r_ins_the_r_package.html> and <http://foges.github.io/pogs/stp/r>.
Spatial clustering with hidden markov random field fitted via EM algorithm, details of which can be found in Yi Yang (2021) <doi:10.1101/2021.06.05.447181>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.
A wrapper for Blizzard's Starcraft II (a 2010 real-time strategy game) Application Programming Interface (API). All documented API calls are implemented in an easy-to-use and consistent manner.
Transfers/imputes statistics among Spanish spatial polygons (census sections or postal code areas) from different moments in time (2001-2022) without need of spatial files, just linking statistics to the ID codes of the spatial units. 
    The data available in the census sections of a partition/division (cartography) into force in a moment of time is transferred to the census sections of another partition/division employing the geometric approach (also known as areal weighting or polygon overlay). 
    References: 
    Goerlich (2022) <doi:10.12842/WPIVIE_0322>.
    Pavía and Cantarino (2017a, b) <doi:10.1111/gean.12112>, <doi:10.1016/j.apgeog.2017.06.021>.
Simple Component Analysis (SCA) often provides much more
   interpretable components than Principal Components (PCA) while still
   representing much of the variability in the data.
Comprehensive set of tools for scaffolding R
  interfaces to modules, classes, functions, and documentations
  written in other programming languages, such as 'Python'.
Calculates graph theoretic scagnostics. Scagnostics describe various measures of interest for pairs of variables, based on their appearance on a scatterplot.  They are useful tool for discovering interesting or unusual scatterplots from a scatterplot matrix, without having to look at every individual plot.
Scale alignment is a new procedure for rescaling dimensions of 
    between-items multidimensional Rasch family models so that dimensions scores
    can be compared directly (Feuerstahler & Wilson, 2019; under review) 
	<doi:10.1111/jedm.12209>. This package includes functions for implementing 
    delta-dimensional alignment (DDA) and logistic regression alignment (LRA) 
    for dichotomous or polytomous data. This function also includes a wrapper 
	for models fit using the 'TAM' package.
Calculating approximately unbiased (AU) p-values
	from multiscale bootstrap probabilities. See
	Shimodaira (2004) <doi:10.1214/009053604000000823>,
	Shimodaira (2008) <doi:10.1016/j.jspi.2007.04.001>, 
	Terada ans Shimodaira (2017) <arXiv:1711.00949>, and
	Shimodaira and Terada (2019) <doi.org/10.3389/fevo.2019.00174>.
Perform a 'probabilistic' linkage of two data files using a scaling procedure using the methods described in Goldstein, H., Harron, K. and Cortina-Borja, M. (2017) <doi:10.1002/sim.7287>.
Graphical scales map data to aesthetics, and provide methods
    for automatically determining breaks and labels for axes and legends.
A scalable Gibbs sampling implementation for high dimensional Bayesian regression with the continuous spike-and-slab prior. Niloy Biswas, Lester Mackey and Xiao-Li Meng, "Scalable Spike-and-Slab" (2022) <arXiv:2204.01668>.
Identifies the locations of neurons, and estimates their calcium concentrations over time using the SCALPEL method proposed in Petersen, Ashley; Simon, Noah; Witten, Daniela. SCALPEL: Extracting neurons from calcium imaging data. Ann. Appl. Stat. 12 (2018), no. 4, 2430--2456. <doi:10.1214/18-AOAS1159>. <https://projecteuclid.org/euclid.aoas/1542078051>.
Algorithms for fitting scaled sparse linear regression and estimating precision matrices.
Routines for generalized additive modelling under shape
        constraints on the component functions of the linear predictor
        (Pya and Wood, 2015) <doi:10.1007/s11222-013-9448-7>.
        Models can contain multiple shape constrained (univariate
        and/or bivariate) and unconstrained terms. The routines of gam() 
        in package 'mgcv' are used for setting up the model matrix,  
        printing and plotting the results.  Penalized likelihood
        maximization based on Newton-Raphson method is used to fit a
        model with multiple smoothing parameter selection by GCV or
        UBRE/AIC.
A collection of procedures for analysing, visualising, 
  and managing single-case data. These include piecewise linear regression 
  models, multilevel models, overlap indices ('PND', 'PEM', 'PAND', 'PET', 'tau-u', 
  'baseline corrected tau', 'CDC'), and randomization tests. Data preparation functions 
  support outlier detection, handling missing values, scaling, truncation, 
  rank transformation, and smoothing. An export function helps to generate 
  html and latex tables in a publication friendly style. More details can be 
  found in the online book 'Analyzing single-case data with R and scan', Juergen Wilbert (2023)
  <https://jazznbass.github.io/scan-Book/>.
An entirely data-driven cell type annotation tools, which requires training data to learn the classifier, but not biological knowledge to make subjective decisions. It consists of three steps: preprocessing training and test data, model fitting on training data, and cell classification on test data. See Xiangling Ji,Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui Zhang.(2022)<doi:10.1101/2022.02.19.481159> for more details.
Detection of anomalous space-time clusters using the scan 
  statistics methodology. Focuses on prospective surveillance of data streams, 
  scanning for clusters with ongoing anomalies. Hypothesis testing is made 
  possible by Monte Carlo simulation. Allévius (2018) <doi:10.21105/joss.00515>.
Import, plot, and diagnose results from statistical
  catch-at-age models, used in fisheries stock assessment.
Series of algorithms to translate  users' mental models of seascapes, 
  landscapes and, more generally, of geographic features into computer representations 
  (classifications). Spaces and geographic objects are classified with user-defined 
  rules taking into account spatial data as well as spatial relationships among 
  different classes and objects.
It is a single cell active pathway analysis tool based on the graph neural network (F. Scarselli (2009) <doi:10.1109/TNN.2008.2005605>; Thomas N. Kipf (2017) <arXiv:1609.02907v4>) to construct the gene-cell association network, infer pathway activity scores from different single cell modalities data, integrate multiple modality data on the same cells into one pathway activity score matrix, identify cell phenotype activated gene modules and parse association networks of gene modules under multiple cell phenotype. In addition, abundant visualization programs are provided to display the results.
Computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing. For details, see Chen and Samworth (2016) <doi:10.1111/rssb.12137>.
A port of the Scarabee toolkit originally written as a 
  Matlab-based application. scaRabee provides a framework for simulation and optimization 
  of pharmacokinetic-pharmacodynamic models at the individual and population level.
  It is built on top of the neldermead package, which provides the direct search 
  algorithm proposed by Nelder and Mead for model optimization.
Allows you to make clean, good-looking scatter plots with the option to 
    easily add marginal density or box plots on the axes. It is also available as a module for 'jamovi'
    (see <https://www.jamovi.org> for more information). 'Scatr' is based on the 
    'cowplot' package by Claus O. Wilke and the 'ggplot2' package by Hadley Wickham.
Creates 'D3' 'JavaScript' scatterplots from 'R' with interactive
    features : panning, zooming, tooltips, etc.
The user has the option to utilize the two-dimensional density estimation techniques called smoothed density published by Eilers and Goeman (2004) <doi:10.1093/bioinformatics/btg454>, and pareto density which was evaluated for univariate data by Thrun, Gehlert and Ultsch, 2020 <doi:10.1371/journal.pone.0238835>. Moreover, it provides visualizations of the density estimation in the form of two-dimensional scatter plots in which the points are color-coded based on increasing density. Colors are defined by the one-dimensional clustering technique called 1D distribution cluster algorithm (DDCAL) published by Lux and Rinderle-Ma (2023) <doi:10.1007/s00357-022-09428-6>.
C-based conversion of large scatterplot data to rasters plus other 
             operations such as data blurring or data alpha blending. Speeds up 
             plotting of data with millions of points.
Creates scatterpie plots, especially useful for plotting pies on a map.
Plots a three dimensional (3D) point cloud.
Create a scatter plot matrix, using 'htmlwidgets' package and 'd3.js'.
Identifies a bicluster, a submatrix of the data such that the features and observations within the submatrix differ from those not contained in submatrix, using a two-step method. In the first step, observations in the bicluster are  identified to maximize the sum of weighted between cluster feature differences. The method is described in Helgeson et al. (2020) <doi:10.1111/biom.13136>. 'SCBiclust' can be used to identify biclusters which differ based on feature means, feature variances, or more general differences.
Cellular population mapping (CPM) a deconvolution algorithm in which single-cell genomics is required in only one or a few samples, where in other samples of the same tissue, only bulk genomics is measured and the underlying fine resolution cellular heterogeneity is inferred.
Statistical methods for estimating and inferring the mean of functional data. The methods include simultaneous confidence bands, local polynomial fitting,  bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.
Provides tools to import and export from several existing pieces of ion-channel analysis software such as 'TAC', 'QUB', 'SCAN', and 'Clampfit', implements procedures such as dwell-time correction and defining bursts with a critical time, and provides tools for analysis of bursts, such as tools for sorting and plotting.
An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) <doi:10.1016/j.isci.2020.100882> for more details.
An efficient implementation of SCCI using 'Rcpp'. SCCI is short for the Stochastic Complexity-based Conditional Independence criterium (Marx and Vreeken, 2019). SCCI is an asymptotically unbiased and L2 consistent estimator of (conditional) mutual information for discrete data.
Provides wrappers for 'scclust', a C library for computationally efficient
    size-constrained clustering with near-optimal performance.
    See <https://github.com/fsavje/scclust> for more information.
Core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP <doi:10.21105/joss.00861>, collapsing vertices of each cluster in the graph, and propagating graph labels.
The SC-SR Algorithm is used to calculate fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible making of the failure cause in a competing risks environment. In the version 2.0 the function creating the probability matrix from double-censored data is added.
Various self-controlled case series models used to investigate 
        associations between time-varying exposures such as vaccines or
        other drugs or non drug exposures and an adverse event can be
        fitted. Detailed information on the self-controlled case series
        method and its extensions with more examples can be found in 
        Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y.
        (2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies:
        A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) 
        and <https://sccs-studies.info/index.html>.
Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'.  'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions.  For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" <doi:10.5281/zenodo.5706430>.
Implements methods for obtaining kernel density estimates
    subject to a variety of shape constraints (unimodality, bimodality, 
    symmetry, tail monotonicity, bounds, and constraints on the number of 
    inflection points). Enforcing constraints can eliminate unwanted waves or 
    kinks in the estimate, which improves its subjective appearance and can 
    also improve statistical performance. The main function scdensity() is 
    very similar to the density() function in 'stats', allowing 
    shape-restricted estimates to be obtained with little effort. The 
    methods implemented in this package are described in Wolters and Braun 
    (2017) <doi:10.1080/03610918.2017.1288247>, Wolters (2012) 
    <doi:10.18637/jss.v047.i06>, and Hall and Huang (2002) 
    <http://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>.  
    See the scdensity() help for for full citations.
Provides a fast and accurate pipeline for single-cell analyses. 
    The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).
Provides a set of tools for estimating hierarchical linear
    models and effect sizes based on data from single-case designs. 
    Functions are provided for calculating standardized mean difference effect sizes that 
    are directly comparable to standardized mean differences estimated from between-subjects randomized experiments,
    as described in Hedges, Pustejovsky, and Shadish (2012) <DOI:10.1002/jrsm.1052>; 
    Hedges, Pustejovsky, and Shadish (2013) <DOI:10.1002/jrsm.1086>; 
    Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>; 
    and Chen, Pustejovsky, Klingbeil, and Van Norman (2023) <DOI:10.1016/j.jsp.2023.02.002>. 
    Includes an interactive web interface.
Analysis tools to investigate changes in intercellular
    communication from scRNA-seq data. Using a Seurat object as input,
    the package infers which cell-cell interactions are present in the dataset
    and how these interactions change between two conditions of interest
    (e.g. young vs old). It relies on an internal database of ligand-receptor
    interactions (available for both human and mouse) that have been gathered
    from several published studies. Detection and differential analyses
    rely on permutation tests. The package also contains several tools
    to perform over-representation analysis and visualize the results. See
    Lagger, C. et al. (2021) <doi:10.1101/2021.08.13.456238> for a full 
    description of the methodology.
Detection of item-wise Differential Item Functioning (DIF) 
             in fitted 'mirt', 'multipleGroup' or 'bfactor' models  
             using score-based structural change tests. Under the hood 
             the sctest() function from the 'strucchange' package is used.
PAM (Partitioning Around Medoids) algorithm application to samples of single cell sequencing techniques
	     with a high number of cells (as many as the computer memory allows). The package uses a binary format to
	     store matrices (either full, sparse or symmetric) in files written in the disk that can contain any data type
	     (not just double) which allows its manipulation when memory is sufficient to load them as int or float, but not
	     as double. The PAM implementation is done in parallel, using several/all the cores of the machine, if it has them.
	     This package shares a great part of its code with packages 'jmatrix' and 'parallelpam' but their functionality is
	     included here so there is no need to install them.
We introduce improved methods for statistically assessing birth seasonality and intra-annual variation. The first method we propose is a new idea that uses a nonparametric clustering procedure to group individuals with similar time series data and estimate birth seasonality based on the clusters. One can use the function SCEM() to implement this method. The second method estimates input parameters for use with a previously-developed parametric approach (Tornero et al., 2013). The relevant code for this approach is makeFits_OLS(), while makeFits_initial() is the code to implement the same method but with given initial conditions for two parameters. The latter can be used to show the disadvantage of the existing approach. One can use the function makeFits() to generate parametric birth seasonality estimates using either initialization. Detailed description can be found here: Chazin Hannah, Soudeep Deb, Joshua Falk, and Arun Srinivasan. (2019) "New Statistical Approaches to Intra-Individual Isotopic Analysis and Modeling Birth Seasonality in Studies of Herd Animals." <doi:10.1111/arcm.12432>.
Sometimes it is useful to serve up alternative 'shiny' UIs 
    depending on information passed in the request object, such as the value of 
    a cookie or a query parameter. This packages facilitates such switches.
Analyse single cell RNA sequencing data using entropy to
    calculate heterogeneity and homogeneity of genes amongst the cell
    population. From the work of Michael J. Casey, Ruben J. Sanchez-Garcia 
    and Ben D. MacArthur.
A pipeline for estimating the stellar
        age, mass, and radius given observational 
        effective temperature, [Fe/H], and astroseismic
	parameters. The results are obtained adopting a maximum likelihood
	technique over a grid of pre-computed stellar models, as
	described in Valle et al. (2014) <doi:10.1051/0004-6361/201322210>.
SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.
A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) <doi:10.1093/bioinformatics/btac141>.
An extension of the Fisher Scoring Algorithm to combine PLS regression with GLM 
    estimation in the multivariate context. Covariates can also be grouped in themes.
Fast and regularized version of GWR for large dataset, detailed in Murakami, Tsutsumida, Yoshida, Nakaya, and Lu (2019) <arXiv:1905.00266>.
A fast and adaptable tool to convert photos and images into usable colour schemes for data visualisation. Contains functionality to extract colour palettes from images, as well for the conversion of images between colour spaces.
Functions for creating and manipulating 12-tone (i.e., dodecaphonic) musical matrices using Arnold Schoenberg's (1923) serialism technique. This package can generate random 12-tone matrices and can generate matrices using a pre-determined sequence of notes.
Provides functions to extract citation data from Google
    Scholar.  Convenience functions are also provided for comparing
    multiple scholars and predicting future h-index values.
Contains functions and datasets for math taught in school.
  A main focus is set to prime-calculation.
Offers a helping hand to psychologists and other behavioral scientists who routinely deal with experimental data from factorial experiments. It includes several functions to format output from other R functions according to the style guidelines of the APA (American Psychological Association). This formatted output can be copied directly into manuscripts to facilitate data reporting. These features are backed up by a toolkit of several small helper functions, e.g., offering out-of-the-box outlier removal. The package lends its name to Georg "Schorsch" Schuessler, ingenious technician at the Department of Psychology III, University of Wuerzburg. For details on the implemented methods, see Roland Pfister and Markus Janczyk (2016) <doi: 10.20982/tqmp.12.2.p147>.
The complete scripts from the American version of the Office
    television show in tibble format. Use this package to analyze and have
    fun with text from the best series of all time.
A collection of useful functions and example code created and
    used by the Schloss Lab for reproducible microbiome research. Perform
    common tasks like read files created by mothur <https://mothur.org/>,
    tidy up your microbiome data, and format R Markdown documents for
    publication.  See the website <http://www.schlosslab.org/schtools/>
    for more information, documentation, and examples.
This is a shape preserving spline <doi:10.1137/0720057>
    which is guaranteed to be monotonic and concave or convex if the
    data is monotonic and concave or convex. It does not use any 
    optimisation and is therefore quick and smoothly converges to a 
    fixed point in economic dynamics problems including value function 
    iteration. It also automatically gives the first two derivatives 
    of the spline and options for determining behaviour when evaluated 
    outside the interpolation domain.
Functions for generating Standardized Climate Indices (SCI). 
		SCI is a transformation of (smoothed) climate (or environmental)
		time series that  removes seasonality and forces the data to
		take values of the standard normal distribution. SCI was 
		originally developed for precipitation. In this case it is 
		known as the Standardized Precipitation Index (SPI).
Remove batch effects by projecting query batches into the reference batch space.
Colour choice in information visualisation is important in order to
    avoid being mislead by inherent bias in the used colour palette. The 'scico'
    package provides access to the perceptually uniform and colour-blindness 
    friendly palettes developed by Fabio Crameri and released under the 
    "Scientific Colour-Maps" moniker. The package contains 24 different palettes 
    and includes both diverging and sequential types.
Set of tools to import, summarize, wrangle, and visualize data. 
    These functions were originally written based on the needs of the 
    various synthesis working groups that were supported by the 
    National Center for Ecological Analysis and Synthesis (NCEAS). 
    These tools are meant to be useful inside and 
    outside of the context for which they were designed.
Data used in Taback, N. (2022). Design and Analysis of Experiments and Observational Studies using R. Chapman & Hall/CRC.
It involves bibliometric indicators calculation from bibliometric data.It also deals pattern analysis using the text part of bibliometric data.The bibliometric data are obtained from mainly Web of Science and Scopus.
Users may specify what fundamental qualities of a new study have 
             or have not changed in an attempt to reproduce or replicate an 
             original study. A comparison of the differences is visualized. 
             Visualization approach follows 'Patil', 'Peng', and 
             'Leek' (2016) <doi:10.1101/066803>.
An automatic cell type detection and assignment algorithm for single cell RNA-Seq and Cytof/FACS data. 'SCINA' is capable of assigning cell type identities to a pool of cells profiled by scRNA-Seq or Cytof/FACS data with prior knowledge of markers, such as genes and protein symbols that are highly or lowly expressed in each category. See Zhang Z, et al (2019) <doi:10.3390/genes10070531> for more details.
We develop a novel matrix factorization tool named 'scINSIGHT' to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages. Given multiple gene expression samples from different biological conditions, 'scINSIGHT' simultaneously identifies common and condition-specific gene modules and quantify their expression levels in each sample in a lower-dimensional space. With the factorized results, the inferred expression levels and memberships of common gene modules can be used to cluster cells and detect cell identities, and the condition-specific gene modules can help compare functional differences in transcriptomes from distinct conditions. Please also see Qian K, Fu SW, Li HW, Li WV (2022) <doi:10.1186/s13059-022-02649-3>.
A collection of functions that creates graphs with error
             bars for data collected from one-way or higher factorial
             designs.
Create and format tables and APA statistics for
    scientific publication. This includes making a 'Table 1'
    to summarize demographics across groups, correlation tables
    with significance indicated by stars, and extracting formatted
    statistical summarizes from simple tests for in-text notation.
    The package also includes functions for Winsorizing data based
    on a Z-statistic cutoff.
A set of 'Rmarkdown' themes for creating scientific and professional documents. Simple interface with features to ease navigation across the page and sub-pages.
Provides an imputation pipeline for single-cell RNA sequencing data. 
  The 'scISR' method uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and estimates the dropout values using a subspace regression model (Tran et.al. (2022) <DOI:10.1038/s41598-022-06500-4>).
Fast maximum-likelihood phylogeny inference from noisy single-cell data using the 'ScisTree' algorithm by Yufeng Wu (2019) <doi:10.1093/bioinformatics/btz676>. 'scistreer' provides an 'R' interface and improves speed via 'Rcpp' and 'RcppParallel', making the method applicable to massive single-cell datasets (>10,000 cells).
Single-cell Interpretable Tensor Decomposition (scITD) employs the 
    Tucker tensor decomposition to extract multicell-type gene expression patterns 
    that vary across donors/individuals. This tool is geared for use with single-cell
    RNA-sequencing datasets consisting of many source donors. The method has a wide
    range of potential applications, including the study of inter-individual variation
    at the population-level, patient sub-grouping/stratification, and the analysis
    of sample-level batch effects. Each "multicellular process" that is extracted 
    consists of (A) a multi cell type gene loadings matrix and (B) a
    corresponding donor scores vector indicating the level at which the corresponding
    loadings matrix is expressed in each donor. Additional methods are implemented
    to aid in selecting an appropriate number of factors and to evaluate stability
    of the decomposition. Additional tools are provided for downstream analysis,
    including integration of gene set enrichment analysis and ligand-receptor analysis. 
    Tucker, L.R. (1966) <doi:10.1007/BF02289464>. Unkel, S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011) <doi:10.1007/s13253-011-0055-9>. Zhou, G., & Cichocki, A. (2012) <doi:10.2478/v10175-012-0051-4>.
Functions to install SciViews additions to R, and more
        tools.
Uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data.
Maximum likelihood estimation of the scaled logit model parameters
    proposed in Dunning (2006) <doi:10.1002/sim.2282>.
Toolbox containing a variety of spectral clustering tools functions. Among the tools available are the hierarchical spectral clustering algorithm, the Shi and Malik clustering algorithm, the Perona and Freeman algorithm, the non-normalized clustering, the Von Luxburg algorithm, the Partition Around Medoids clustering algorithm, a multi-level clustering algorithm, recursive clustering and the fast method for all clustering algorithm. As well as other tools needed to run these algorithms or useful for unsupervised spectral clustering. This toolbox aims to gather the main tools for unsupervised spectral classification. See <http://mawenzi.univ-littoral.fr/> for more information and documentation.
Perform meta-analysis of single-case experiments, including calculating various effect size measures (SMD, PND, PEM and NAP) and probability combining (additive and multiplicative method), as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.
The single cell mapper (scMappR) R package contains a suite of bioinformatic tools that provide experimentally relevant cell-type specific information to a list of differentially expressed genes (DEG). The function "scMappR_and_pathway_analysis" reranks DEGs to generate cell-type specificity scores called cell-weighted fold-changes. Users input a list of DEGs, normalized counts, and a signature matrix into this function. scMappR then re-weights bulk DEGs by cell-type specific expression from the signature matrix, cell-type proportions from RNA-seq deconvolution and the ratio of cell-type proportions between the two conditions to account for changes in cell-type proportion. With cwFold-changes calculated, scMappR uses two approaches to utilize cwFold-changes to complete cell-type specific pathway analysis. The "process_dgTMatrix_lists" function in the scMappR package contains an automated scRNA-seq processing pipeline where users input scRNA-seq count data, which is made compatible for scMappR and other R packages that analyze scRNA-seq data. We further used this to store hundreds up regularly updating signature matrices. The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics. 3(1). Iqab011. <doi:10.1093/nargab/lqab011>.
Provides functions for fitting discrete distribution models to count data.
  Included are the Poisson, the negative binomial, the Poisson-inverse gaussian and, most importantly,
  a new implementation of the Poisson-beta distribution (density, distribution and quantile
  functions, and random number generator) together with a needed new implementation of
  Kummer's function (also: confluent hypergeometric function of the first kind). Three
  different implementations of the Gillespie algorithm allow data simulation based on the
  basic, switching or bursting mRNA generating processes. Moreover, likelihood functions for
  four variants of each of the three aforementioned distributions are also available.
  The variants include one population and two population mixtures, both with and without
  zero-inflation. The package depends on the 'MPFR' libraries (<https://www.mpfr.org/>) which need to be installed separately 
  (see description at <https://github.com/fuchslab/scModels>).
  This package is supplement to the paper "A mechanistic model for the negative binomial distribution of single-cell mRNA counts" 
  by Lisa Amrhein, Kumar Harsha and Christiane Fuchs (2019) <doi:10.1101/657619> available on bioRxiv.
Unequal granularity of cell type annotation makes it difficult to compare scRNA-seq datasets at scale. Leveraging the ontology system for defining cell type hierarchy, 'scOntoMatch' aims to align cell type annotations to make them comparable across studies. The alignment involves two core steps: first is to trim the cell type tree within each dataset so each cell type does not have descendants, and then map cell type labels cross-studies by direct matching and mapping descendants to ancestors. Various functions for plotting cell type trees and manipulating ontology terms are also provided. In the Single Cell Expression Atlas hosted at EBI, a compendium of datasets with curated ontology labels are great inputs to this package.
Provides a computational framework for identification of B cell clones from 
             Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data. Three main 
             functions are included (identicalClones, hierarchicalClones, and spectralClones) 
             that perform clustering among sequences of BCRs/IGs (B cell receptors/immunoglobulins) 
             which share the same V gene, J gene and junction length.
             Nouri N and Kleinstein SH (2018) <doi: 10.1093/bioinformatics/bty235>.
             Nouri N and Kleinstein SH (2019) <doi: 10.1101/788620>.
             Gupta NT, et al. (2017) <doi: 10.4049/jimmunol.1601850>.
Assigns a score projection from 0 to 1 between a given in 'vivo' stage and each single cluster from an in 'vitro' dataset. The score is assigned based on the the fraction of specific markers of the in 'vivo' stage that are conserved in the in 'vitro' clusters <https://github.com/ScialdoneLab>.
A non convex optimization package that optimizes any function under the criterion, combination of variables are on the surface of a unit sphere, as described in the paper : Das et al. (2019) <arXiv:1909.04024> .
Provides routines for scoring behavioral questionnaires. Includes scoring procedures for the 'International Physical Activity Questionnaire (IPAQ)' <http://www.ipaq.ki.se>. Compares physical functional performance to the age- and gender-specific normal ranges.
The `scorecard` package makes the development of credit risk scorecard 
  easier and efficient by providing functions for some common tasks, 
  such as data partition, variable selection, woe binning, scorecard scaling,
  performance evaluation and report generation. These functions can also used
  in the development of machine learning models.
    The references including: 
  1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: 
  Development and Implementation Using SAS. 
  2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. 
  Developing and Implementing Intelligent Credit Scoring.
Provides infrastructure functionalities such as missing value treatment, information value calculation, GINI calculation etc. which are used for developing a traditional credit scorecard as well as a machine learning based model. The functionalities defined are standard steps for any credit underwriting scorecard development, extensively used in financial domain.
Perform association test within linear mixed model framework using score test integrated with Empirical Bayes for genome-wide association study. Firstly, score test was conducted for each marker under linear mixed model framework, taking into account the genetic relatedness and population structure. And then all the potentially associated markers were selected with a less stringent criterion. Finally, all the selected markers were placed into a multi-locus model to identify the true quantitative trait nucleotide.
Provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.
Implementation of community detection algorithm SCORE in the paper J. Jin (2015) <arXiv:1211.5803>, and SCORE+ in J. Jin, Z. Ke and S. Luo (2018) <arXiv:1811.05927>. Membership estimation algorithm called Mixed-SCORE in J. Jin, Z. Ke and S. Luo (2017) <arXiv:1708.07852>.
Evaluating probabilistic forecasts via proper scoring rules.  scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families.  Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score.  For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.
Implements multiple consistent scoring functions
    (Gneiting T (2011) <doi:10.1198/jasa.2011.r10138>) for assessing point
    forecasts and point predictions. Detailed documentation of scoring
    functions' properties is included for facilitating interpretation of
    results.
Dictionary-like reference for computing scoring rules in a wide
    range of situations. Covers both parametric forecast distributions (such as
    mixtures of Gaussians) and distributions generated via simulation.
Grouping essential tools for credit scoring. These statistical tools may be useful for other use-cases as well but were primarily designed for it. First, there are Reject Inference methods (Ehrhardt et al. (2017) <arXiv:1903.10855>). Second, we build upon the already CRAN-available package 'discretization' to automate discretization of continuous features.
Provides a collection of metrics and proper scoring rules 
    (Tilmann Gneiting & Adrian E Raftery (2007) 
    <doi:10.1198/016214506000001437>, Jordan, A., Krüger, F., & Lerch, S. (2019)
    <doi:10.18637/jss.v090.i12>) within a consistent framework for 
    evaluation, comparison and visualisation of forecasts. 
    In addition to proper scoring rules, functions are provided to assess 
    bias, sharpness and calibration 
    (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind
    M. Eggo, W. John Edmunds (2019) <doi:10.1371/journal.pcbi.1006785>) of 
    forecasts. 
    Several types of predictions (e.g. binary, discrete, continuous) which may 
    come in different formats (e.g. forecasts represented by predictive samples 
    or by quantiles of the predictive distribution) can be evaluated. 
    Scoring metrics can be used either through a convenient data.frame format, 
    or can be applied as individual functions in a vector / matrix format. 
    All functionality has been implemented with a focus on performance and is 
    robustly tested.
A consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. See van der Laan and Robins (1997) <doi:10.2307/2670119>.
Constructs gene regulatory networks from single-cell gene expression data using the PANDA (Passing Attributes between Networks for Data Assimilation) algorithm.
An accurate and easy tool for performing linear trajectory inference on
  single cells using single-cell RNA sequencing data. In addition, SCORPIUS
  provides functions for discovering the most important genes with respect to
  the reconstructed trajectory, as well as nice visualisation tools.
  Cannoodt et al. (2016) <doi:10.1101/079509>.
Perform the balanced (Scott and Knott, 1974) and unbalanced <doi:10.1590/1984-70332017v17n1a1> Scott & Knott algorithm.
The Scott-Knott Effect Size Difference (ESD) test is a mean comparison approach that leverages a hierarchical clustering to partition the set of treatment means (e.g., means of variable importance scores, means of model performance) into statistically distinct groups with non-negligible difference [Tantithamthavorn et al., (2018) <doi:10.1109/TSE.2018.2794977>].
Implements the Scout method for regression, described in "Covariance-regularized regression and classification for high-dimensional problems", by Witten and Tibshirani (2008),  Journal of the Royal Statistical Society, Series B 71(3): 615-636.
Using principal component analysis as a base model, 'SCOUTer' 
    offers a new approach to simulate outliers in a simple and precise way. 
    The user can generate new observations defining them by a pair of well-known 
    statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2) 
    statistics. Just by introducing the target values of the SPE and T^2, 'SCOUTer' 
    returns a new set of observations with the desired target properties. 
    Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga and 
    Alberto Ferrer (2020).
Implements different inventory models, the bullwhip effect and other supply chain performance variables. Marchena Marlene (2010) <arXiv:1009.3977>.
R version of 'scperturb' tool for single-cell perturbation 
    analysis. Contains wrappers for performing E-statistics for Seurat objects. 
    More details on the method can be found in Peidli et al. (2023) 
    <doi:10.1101/2022.08.20.504663> and in Székely and Rizzo (2004).
Implementation of prediction and inference procedures for Synthetic Control methods using least square, lasso, ridge, or simplex-type constraints. Uncertainty is quantified with prediction intervals as developed in Cattaneo, Feng, and Titiunik (2021) <https://mdcattaneo.github.io/papers/Cattaneo-Feng-Titiunik_2021_JASA.pdf>.
Compute ploidy of single cells (or nuclei)
    based on single-cell (or single-nucleus)
    ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing)
    data <https://github.com/fumi-github/scPloidy>.
Add-on for the 'scan' package that creates plots 
  from single-case data frames ('scdf'). It includes functions for styling 
  single-case plots, adding phase-based lines to indicate various statistical 
  parameters, and predefined themes for presentations and publications. More 
  information and in depth examples can be found in the online book 
  "Analyzing Single-Case Data with R and 'scan" 
  Jürgen Wilbert (2023) <https://jazznbass.github.io/scan-Book/>.
Useful to visualize the Poissoneity (an independent Poisson statistical framework, 
  where each RNA measurement for each cell comes from its own independent Poisson distribution) of 
  Unique Molecular Identifier (UMI) based single cell RNA sequencing (scRNA-seq) data, and explore 
  cell clustering based on model departure as a novel data representation.
A system that provides a streamlined way of generating publication ready plots for known Single-Cell transcriptomics data in a “publication ready” format. This is, the goal is to automatically generate plots with the highest quality possible, that can be used right away or with minimal modifications for a research article.
Functions to implement the stability controlled 
    quasi-experiment (SCQE) approach to study the effects of 
    newly adopted treatments that were not assigned at random. This package 
    contains tools to help users avoid making statistical assumptions that rely 
    on infeasible assumptions. Methods developed in Hazlett (2019) 
    <doi:10.1002/sim.8717>.
Given bincount data from single-cell copy number profiling (segmented or unsegmented), estimates ploidy, and uses the ploidy estimate to scale the data to absolute copy numbers. Uses the modular quantogram proposed by Kendall (1986) <doi:10.1002/0471667196.ess2129.pub2>, modified by weighting segments according to confidence, and quantifying confidence in the estimate using a theoretical quantogram. Includes optional fused-lasso segmentation with the algorithm in Johnson (2013) <doi:10.1080/10618600.2012.681238>, using the implementation from glmgen by Arnold, Sadhanala, and Tibshirani.
Tools for scraping information from webpages and other XML contents, using XPath or CSS selectors.
A group of functions to scrape data from different websites, for 
    academic purposes.
Routines for a collection of screen-and-clean type
        variable selection procedures, including UPS and GS.
Optimal hard thresholding of singular values. The procedure adaptively estimates the best singular value threshold under unknown noise characteristics. The threshold chosen by 'ScreeNOT' is optimal (asymptotically, in the sense of minimum Frobenius error) under the the so-called "Spiked model" of a low-rank matrix observed in additive noise. In contrast to previous works, the noise is not assumed to be i.i.d. or white; it can have an essentially arbitrary and unknown correlation structure, across either rows, columns or both. 'ScreeNOT' is proposed to practitioners as a mathematically solid alternative to Cattell's ever-popular but vague Scree Plot heuristic from 1966. 
    If you use this package, please cite our paper: David L. Donoho, Matan Gavish and Elad Romanov (2023). "ScreeNOT: Exact MSE-optimal singular value thresholding in correlated noise." Annals of Statistics, 2023 (To appear). <arXiv:2009.12297>.
A base dependency solution with basic argument parsing for use with
    'Rscript'.
Tools for the analysis of high-dimensional data developed/implemented
  at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME).
  Main focus is on SNP data. But most of the functions can also be applied to other
  types of categorical data.
We provide a comprehensive scheme that is capable of simulating Single Cell RNA Sequencing data for various parameters of Biological Coefficient of Variation,
    busting kinetics, differential expression (DE), cell or sample groups, cell trajectory, batch effect and other experimental designs.
    'SCRIP' proposed and compared two frameworks with Gamma-Poisson and Beta-Gamma-Poisson models for simulating Single Cell RNA Sequencing data.
	Other reference is available in Zappia et al. (2017) <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1305-0>.
Run complex native scripts with a single command, similar to system commands.
Displays the content of a R script into the 'Cytoscape' network-visualization app <https://cytoscape.org/>.
A small set of functions wrapping up the call stack and command
  line inspection needed to determine a running script's filename from
  within the script itself.
Full text, in data frames containing one row per verse, of the 
    Standard Works of The Church of Jesus Christ of Latter-day Saints (LDS). 
    These are the Old Testament, (KJV), the New Testament (KJV), the Book of 
    Mormon, the Doctrine and Covenants, and the Pearl of Great Price.
A coalescent simulator that allows the rapid simulation of
    biological sequences under neutral models of evolution. Different to other
    coalescent based simulations, it has an optional approximation parameter that
    allows for high accuracy while maintaining a linear run time cost for long
    sequences. It is optimized for simulating massive data sets as produced by Next-
    Generation Sequencing technologies for up to several thousand sequences.
A pipeline that can process single or multiple Single Cell RNAseq 
    samples primarily specializes in Clustering and Dimensionality Reduction. 
    Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid cells, 
    Epithelial cells, and stromal cells (Fiboblast, Endothelial cells, Pericyte,
    Smooth muscle cells) to visualize the Seurat clusters, to facilitate labeling 
    them by biological names. Once users named each cluster, they can evaluate the 
    quality of them again and find the de novo marker genes also.
We integrated the common analysis methods utilized in single cell RNA sequencing data, which included cluster method, principal components analysis (PCA), the filter of differentially expressed genes, pathway enrichment analysis and correlated analysis methods.
'Last.fm'<https://www.last.fm> is a music platform focussed on building a 
    detailed profile of a users listening habits. It does this by 'scrobbling' (recording) 
    every track you listen to on other platforms ('spotify', 'youtube', 'soundcloud' etc)
    and transferring them to your 'Last.fm' database. This allows 'Last.fm' to act as a 
    complete record of your entire listening history. 'scrobbler' provides helper functions
    to download and analyse your listening history in R.
Allows the user to animate 'shiny' elements when scrolling to view them.
    The animations are activated using the 'scrollrevealjs' library. See <https://scrollrevealjs.org/> for more information.
Identifying cell types based on expression profiles is a pillar of single cell analysis. 'scROSHI' identifies cell types based on expression profiles of single cell analysis by utilizing previously obtained cell type specific gene sets. It takes into account the hierarchical nature of cell type relationship and does not require training or annotated data. A detailed description of the method can be found at: Prummer, Bertolini, Bosshard, Barkmann, Yates, Boeva, The Tumor Profiler Consortium, Stekhoven, and Singer (2022) <doi:10.1101/2022.04.05.487176>.
Contains four functions used in the DIC-tau_g procedure. SCRSELECT() and SCRSELECTRUN() uses Stochastic Search Variable Selection to select important
    covariates in the three hazard functions of a semi-competing risks model. These functions perform the Gibbs sampler for variable selection and a Metropolis-Hastings-Green sampler for the number of split points and parameters for the
    three baseline hazard function. The function SCRSELECT() returns the posterior sample of all quantities sampled in the Gibbs sampler after a burn-in period to a desired
    file location, while the function SCRSELECTRUN() returns posterior values of important quantities to the DIC-Tau_g procedure in a list.
    The function DICTAUG() returns a list containing the DIC values for the unique models visited by the DIC-Tau_g grid search.
    The function ReturnModel() uses SCRSELECTRUN() and DICTAUG() to return a summary of the posterior coefficient vectors for the optimal model along with saving this posterior sample to a desired path location.
Design single-case phase, alternation and multiple-baseline experiments, and conduct randomization tests on data gathered by means of such designs, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.
Test published summary statistics for consistency
    (Brown and Heathers, 2017, <doi:10.1177/1948550616673876>;
    Allard, 2018, <https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/>;
    Heathers and Brown, 2019, <https://osf.io/5vb3u/>).
    The package also provides infrastructure for implementing new
    error detection techniques.
Functions for working with the scrypt key derivation functions
    originally described by Colin Percival
    <https://www.tarsnap.com/scrypt/scrypt.pdf> and in Percival and Josefsson
    (2016) <doi:10.17487/RFC7914>. Scrypt is a password-based key derivation
    function created by Colin Percival. The algorithm was specifically designed
    to make it costly to perform large-scale custom hardware attacks by
    requiring large amounts of memory.
A simple, light, and robust interface between R and
    the 'Scryfall' card data API <https://scryfall.com/docs/api>.
Solves convex cone programs via operator splitting. Can solve:
    linear programs ('LPs'), second-order cone programs ('SOCPs'), semidefinite programs
    ('SDPs'), exponential cone programs ('ECPs'), and power cone programs ('PCPs'), or
    problems with any combination of those cones. 'SCS' uses 'AMD' (a set of routines for permuting sparse matrices prior to factorization) and 'LDL' (a sparse 'LDL' factorization and solve package) from 'SuiteSparse' (<https://people.engr.tamu.edu/davis/suitesparse.html>).
Implements the algorithm described in
	Guo, H., and Li, J., "scSorter: assigning cells to known cell types according to known marker genes". 
	Cluster cells to known cell types based on marker genes specified for each cell type.
A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input.  First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells.
A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.
A normalization method for single-cell UMI count data using a 
  variance stabilizing transformation. The transformation is based on a 
  negative binomial regression model with regularized parameters. As part of the
  same regression framework, this package also provides functions for
  batch correction, and data correction. See Hafemeister and Satija (2019)
  <doi:10.1186/s13059-019-1874-1>, and Choudhary and Satija (2022) <doi:10.1186/s13059-021-02584-9>
  for more details.
Code for describing and manipulating scuba diving profiles 
	(depth-time curves) and decompression models, 
        for calculating the predictions of decompression models,
	for calculating maximum no-decompression time and decompression tables,
	and for performing mixed gas calculations.
Analysis of single-cell RNA sequencing data can be simple and
    clear with the right utility functions. This package collects such functions,
    aiming to fulfill the following criteria:
    code clarity over performance (i.e. plain R code instead of C code),
    most important analysis steps over completeness
    (analysis 'by hand', not automated integration etc.),
    emphasis on quantitative visualization (intensity-coded color scale, etc.).
Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) <doi:10.5220/0005595502260234>. Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package.
Make graphical representations of single case data and transform graphical displays back to raw data, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend.
Provides an efficient framework for 
   high-dimensional linear and diagonal discriminant analysis with 
   variable selection.  The classifier is trained using James-Stein-type 
   shrinkage estimators and predictor variables are ranked using 
   correlation-adjusted t-scores (CAT scores).  Variable selection error 
   is controlled using false non-discovery rates or higher criticism.
Functions and Datasets from Lohr, S. (1999), Sampling:
        Design and Analysis, Duxbury.
We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2020), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", <arXiv:2002.11992>.
Provides digital tools for performing analyses within Social Dynamics and complexity in the Ancient Mediterranean (SDAM), which is a research group based at the Department of History and Classical Studies at Aarhus University.
Data sets and functions to support the books "Statistics: Data 
        analysis and modelling" by Speekenbrink, M. (2021) 
        <https://mspeekenbrink.github.io/sdam-book/> and "An R companion to 
        Statistics: data analysis and modelling" by Speekenbrink, M. (2021) 
        <https://mspeekenbrink.github.io/sdam-r-companion/>. All datasets 
        analysed in these books are provided in this package. In addition, the 
        package provides functions to compute sample statistics (variance, 
        standard deviation, mode), create raincloud and enhanced Q-Q plots, and 
        expand Anova results into omnibus tests and tests of individual 
        contrasts.
A fast, consistent tool for plotting and facilitating the analysis of stratigraphic
    and sedimentological data. Taking advantage of the flexible plotting tools available in R, 
    'SDAR' uses stratigraphic and sedimentological data to produce detailed graphic logs for 
    outcrop sections and borehole logs. These logs can include multiple features (e.g., bed thickness,
    lithology, samples, sedimentary structures, colors, fossil content, bioturbation index, 
    gamma ray logs) (Johnson, 1992, <ISSN 0037-0738>).
Includes all the datasets of 'Sampling: Design and Analysis'
    (3rd edition by Sharon Lohr) in R format and additional functions for
    analyzing and graphing probability samples.
Provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats.
  These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control.
Tools for researchers to explicitly show that their results
    comply to rules for statistical disclosure control imposed by research
    data centers. These tools help in checking descriptive statistics and
    models and in calculating extreme values that are not individual data.
    Also included is a simple function to create log files. The methods
    used here are described in the "Guidelines for the checking of output
    based on microdata research" by Bond, Brandt, and de Wolf (2015)
    <https://ec.europa.eu/eurostat/cros/system/files/dwb_standalone-document_output-checking-guidelines.pdf>.
Data from statistical agencies and other institutions are mostly
    confidential. This package (see also Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v067.i04>) can be used for the generation of anonymized
    (micro)data, i.e. for the creation of public- and scientific-use files.
    The theoretical basis for the methods implemented can be found in Templ (2017) <doi:10.1007/978-3-319-50272-4>.
    Various risk estimation and anonymisation methods are included. Note that the package
    includes a graphical user interface (Meindl and Templ, 2019 <doi:10.3390/a12090191>) that allows to use various methods of this
    package.
Tools for calculating disclosure risk measures for microdata,
    including record-level and file-level measures. The record-level disclosure
    risk is estimated primarily using exhaustive tabulation. The file-level
    disclosure risk is estimated by fitting loglinear models on the observed 
    sample counts in cells formed by key variables and their interactions. 
    Funded by the National Center for Education Statistics. See Skinner and
    Shlomo (2008) <doi:10.1198/016214507000001328> for a description of the
    file-level risk measures and the loglinear model approach.
Privacy protected raster maps 
  can be created from spatial point data. Protection
  methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) 
  <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf and 
  de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing 
  revealing values and a generalization of the quad tree method by 
  Suñé, Rovira, Ibáñez and Farré (2017) <doi:10.2901/EUROSTAT.C2017.001>.
Methods for statistical disclosure control in
    tabular data such as primary and secondary cell suppression as described for example
    in Hundepol et al. (2012) <doi:10.1002/9781118348239> are covered in this package.
Companion package to the book Simulation and Inference for
        Stochastic Differential Equations With R Examples, ISBN
        978-0-387-75838-1, Springer, NY.
Performs two tests to evaluate if the
        experiments are associated and returns a list of interesting
        features common to all the experiments.
Implementation of evolutionary fuzzy systems for the data mining task called
    "subgroup discovery". In particular, the algorithms presented in this package are:
    M. J. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero (2007) <doi:10.1109/TFUZZ.2006.890662>
    M. J. del Jesus, P. Gonzalez, F. Herrera (2007) <doi:10.1109/MCDM.2007.369416>
    C. J. Carmona, P. Gonzalez, M. J. del Jesus, F. Herrera (2010) <doi:10.1109/TFUZZ.2010.2060200>
    C. J. Carmona, V. Ruiz-Rodado, M. J. del Jesus, A. Weber, M. Grootveld, P. González, D. Elizondo (2015) <doi:10.1016/j.ins.2014.11.030>
    It also provide a Shiny App to ease the analysis. The algorithms work with data sets provided in
    KEEL, ARFF and CSV format and also with data.frame objects.
Implementation of statistical methods for the estimation of
    toroidal diffusions. Several diffusive models are provided, most of them
    belonging to the Langevin family of diffusions on the torus. Specifically,
    the wrapped normal and von Mises processes are included, which can be seen
    as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of
    methods for approximate maximum likelihood estimation, organized in four
    blocks, is given: (i) based on the exact transition probability density,
    obtained as the numerical solution to the Fokker-Plank equation; (ii) based
    on wrapped pseudo-likelihoods; (iii) based on specific analytic
    approximations by wrapped processes; (iv) based on maximum likelihood of
    the stationary densities. The package allows the reproducibility of the
    results in García-Portugués et al. (2019) <doi:10.1007/s11222-017-9790-2>.
Identify 17 Sustainable Development Goals and associated 169 targets in text.
Functions to filter GPS/Argos locations, as well as assessing the sample size for the analysis of animal distributions. The filters remove temporal and spatial duplicates, fixes located at a given height from estimated high tide line, and locations with high error as described in Shimada et al. (2012) <doi:10.3354/meps09747> and Shimada et al. (2016) <doi:10.1007/s00227-015-2771-0>. Sample size for the analysis of animal distributions can be assessed by the conventional area-based approach or the alternative probability-based approach as described in Shimada et al. (2021) <doi:10.1111/2041-210X.13506>.
An extensible framework for developing species distribution
    models using individual and community-based approaches, generate ensembles of
    models, evaluate the models, and predict species potential distributions in
    space and time. For more information, please check the following paper:
    Naimi, B., Araujo, M.B. (2016) <doi:10.1111/ecog.01881>.
A 'shiny' 
    application that allows non-expert 'R' users to easily model
    species distribution. It offers a reproducible work flow for
    species distribution modeling into a single and user friendly environment.
    'sdmApp' takes 'raster' data (in format supported by the 'raster package') 
    and species occurrence data (several format supported) as input argument.
    It provides an interactive graphical user interface (GUI).
Species distribution modelling (SDM) has been developed for several years to address conservation issues, assess the direct impact of human activities on ecosystems and predict the potential distribution shifts of invasive species (see Elith et al. 2006, Pearson 2007, Elith and Leathwick 2009). SDM relates species occurrences with environmental information and can predict species distribution on their entire occupied space. This approach has been increasingly applied to Southern Ocean case studies, but requires corrections in such a context, due to the broad scale area, the limited number of presence records available and the spatial and temporal aggregations of these datasets. SDMPlay is a pedagogic package that will allow you to compute SDMs, to understand the overall method, and to produce model outputs. The package, along with its associated vignettes, highlights the different steps of model calibration and describes how to choose the best methods to generate accurate and relevant outputs. SDMPlay proposes codes to apply a popular machine learning approach, BRT (Boosted Regression Trees) and introduces MaxEnt (Maximum Entropy). It contains occurrences of marine species and environmental descriptors datasets as examples associated to several vignette tutorials available at <https://github.com/charleneguillaumot/THESIS/tree/master/SDMPLAY_R_PACKAGE>.
Terrestrial and marine predictors for species distribution modelling
    from multiple sources, including WorldClim <https://www.worldclim.org/>,,
    ENVIREM <https://envirem.github.io/>, Bio-ORACLE <https://bio-oracle.org/>
    and MARSPEC <http://www.marspec.org/>.
Implements spatial and spatiotemporal predictive-process GLMMs
    (Generalized Linear Mixed Effect Models) using 'TMB', 'INLA', and the SPDE
    (Stochastic Partial Differential Equation) approximation to Gaussian random
    fields. One common application is for spatially explicit (and optionally
    dynamic) species distribution models (SDMs). See Anderson et al. (2022)
    <doi:10.1101/2022.03.24.485545>.
User-friendly framework that enables the training and the
    evaluation of species distribution models (SDMs). The package implements
    functions for data driven variable selection and model tuning and includes
    numerous utilities to display the results. All the functions used to select
    variables or to tune model hyperparameters have an interactive real-time
    chart displayed in the 'RStudio' viewer pane during their execution.
A software package help user to create virtual species for species distribution modelling. It includes
    several methods to help user to create virtual species distribution map.
    Those maps can be used for Species Distribution Modelling (SDM) study. SDM use
    environmental data for sites of occurrence of a species to predict all the sites
    where the environmental conditions are suitable for the species to persist, and
    may be expected to occur.
Spatial model calculation for static and dynamic panel data models, weights matrix creation and  Bayesian model comparison.
  Bayesian model comparison methods were described by 'LeSage' (2014) <doi:10.1016/j.spasta.2014.02.002>.
  The 'Lee'-'Yu' transformation approach is described in 'Yu', 'De Jong' and 'Lee' (2008) <doi:10.1016/j.jeconom.2008.08.002>, 'Lee' and 'Yu' (2010) <doi:10.1016/j.jeconom.2009.08.001> and 'Lee' and 'Yu' (2010) <doi:10.1017/S0266466609100099>.
M-estimator for threshold and non-threshold spatial dynamic panel data model. Yang, Z (2018) <doi:10.1016/j.jeconom.2017.08.019>. Wu, J., Matsuda, Y (2021) <doi:10.1007/s43071-021-00008-1>.
Utility functions for scale-dependent and alternative hyperpriors. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
  on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. Hyperpriors for all effects can be elicitated within the package. Including complex tensor product interaction terms and variable selection priors. The basic model is explained in in Klein and Kneib (2016) <doi:10.1214/15-BA983>.
Based on the compound Poisson risk process that is perturbed by
    a Brownian motion, saddlepoint approximations to some measures of risk are
    provided. Various approximation methods for the probability of ruin are
    also included. Furthermore, exact values of both the risk measures as well
    as the probability of ruin are available if the individual claims follow
    a hypo-exponential distribution (i. e., if it can be represented as a sum
    of independent exponentially distributed random variables with different
    rate parameters). For more details see Gatto and Baumgartner (2014)
    <doi:10.1007/s11009-012-9316-5>.
We visualize the standard deviation of a data set as the size of a prism whose volume equals the total volume of several prisms made from the Empirical Cumulative Distribution Function.
Solves the general Semi-Definite Linear Programming formulation using an R implementation of SDPT3 (K.C. Toh, M.J. Todd, and R.H. Tutuncu (1999) <doi:10.1080/10556789908805762>). This includes problems such as the nearest correlation matrix problem (Higham (2002) <doi:10.1093/imanum/22.3.329>), D-optimal experimental design (Smith (1918) <doi:10.2307/2331929>), Distance Weighted Discrimination (Marron and Todd (2012) <doi:10.1198/016214507000001120>), as well as graph theory problems including the maximum cut problem. Technical details surrounding SDPT3 can be found in R.H Tutuncu, K.C. Toh, and M.J. Todd (2003) <doi:10.1007/s10107-002-0347-5>.
Functions for post processing the outcomes of ecological niche models. It further include methods to build stability maps based on species distribution models under climate change projections and to reduce environmental data.
Functions for self-determination motivation theory (SDT) to compute
    measures of motivation internalization, motivation simplex structure, and of
    the original and adjusted self-determination or relative autonomy index. SDT
    was introduced by Deci and Ryan (1985) <doi:10.1007/978-1-4899-2271-7>. See
    package?SDT for an overview.
Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties. More details about the methodology SDWD is seen on Wang and Zou (2016) (<doi:10.1080/10618600.2015.1049700>).
Implements the SE-test for equivalence
    according to Hoffelder et al. (2015) <DOI:10.1080/10543406.2014.920344>.
    The SE-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the sum of standardized differences
    between the expected values or in other words: the sum of effect sizes (SE)
    of all components of the two multivariate samples. 
    The test is an asymptotically valid test for normally distributed data 
    (see Hoffelder et al.,2015).
    The function SE.EQ() implements the SE-test for equivalence 
    according to Hoffelder et al. (2015).
    The function SE.EQ.dissolution.profiles() implements a variant 
    of the SE-test for equivalence for similarity analyses of dissolution 
    profiles as mentioned in Suarez-Sharp et al.(2020) 
    <DOI:10.1208/s12248-020-00458-9>). The equivalence margin used in 
    SE.EQ.dissolution.profiles() is analogically defined as for the T2EQ
    approach according to Hoffelder (2019) <DOI:10.1002/bimj.201700257>) 
    by means of a systematic shift in location
    of 10 [\% of label claim] of both dissolution profile populations. 
    SE.EQ.dissolution.profiles() checks whether the weighted mean of the 
    differences of the expected values of both dissolution profile populations 
    is statistically significantly smaller than 10 [\% of label claim]. The 
    weights are built up by the inverse variances.
A few major genes and a series of polygene are responsive for each quantitative trait. Major genes are individually identified while polygene is collectively detected. This is mixed major genes plus polygene inheritance analysis or segregation analysis (SEA). In the SEA, phenotypes from a single or multiple bi-parental segregation populations along with their parents are used to fit all the possible models and the best model of the trait for population phenotypic distributions is viewed as the model of the trait. There are fourteen types of population combinations available. Zhang Yuan-Ming, Gai Jun-Yi, Yang Yong-Hua (2003, <doi:10.1017/S0016672303006141>).
Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.
The explosion of biobank data offers immediate opportunities for 
    gene-environment (GxE) interaction studies of complex diseases because of the 
    large sample sizes and rich collection in genetic and non-genetic information. 
    However, the extremely large sample size also introduces new computational 
    challenges in GxE assessment, especially for set-based GxE variance component (VC) 
    tests, a widely used strategy to boost overall GxE signals and to evaluate the 
    joint GxE effect of multiple variants from a biologically meaningful unit 
    (e.g., gene). 
    We present 'SEAGLE', a Scalable Exact AlGorithm for Large-scale Set-based 
    GxE tests, to permit GxE VC test scalable to biobank data. 'SEAGLE' employs modern 
    matrix computations to achieve the same “exact” results as the original GxE VC 
    tests, and does not impose additional assumptions nor relies on approximations. 
    'SEAGLE' can easily accommodate sample sizes in the order of 10^5, is implementable 
    on standard laptops, and does not require specialized equipment. 
    The accompanying manuscript for this package can be found at Chi, Ipsen, Hsiao, 
    Lin, Wang, Lee, Lu, and Tzeng. (2021+) <arXiv:2105.03228>.
Proximal gradient descent solver for the operators lasso, (fitted)
    group lasso, and (fitted) sparse-group lasso. The implementation involves
    backtracking line search and warm starts. Input data needs to be clustered/
    grouped for each group lasso variant before calling these algorithms.
An R 'Shiny' application dedicated to the intra-site spatial analysis of piece-plotted archaeological remains, making the two and three-dimensional spatial exploration of archaeological data as user-friendly as possible.  Documentation about 'SEAHORS' is provided by the vignette included in this package and by the companion scientific paper: Royer, Discamps, Plutniak, Thomas (2023, PCI Archaeology, <doi:10.5281/zenodo.7674698>).
Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive lasso, which incorporates OLS standard error to the L1 penalty weight. This method is intended for variable selection under linear regression settings (n > p). This new weight assignment strategy is especially useful when the collinearity of the design matrix is a concern.
Provides an interface with the Google Search Console,
    formally called Google Webmaster Tools.
Provides a search interface to look up terms
    on 'Google', 'Bing', 'DuckDuckGo', 'Startpage', 'Ecosia', 'rseek',
    'Twitter', 'StackOverflow', 'RStudio Community', 'GitHub', and 'BitBucket'.
    Upon searching, a browser window will open with the aforementioned search
    results.
The QuadTree data structure is useful for fast,
	     neighborhood-restricted lookups. We use it to implement fast k-Nearest
        Neighbor and Rectangular range lookups in 2 dimenions. The
        primary target is high performance interactive graphics.
Capable of deriving seasonal statistics, such as "normals", and
  analysis of seasonal data, such as departures. This package also has
  graphics capabilities for representing seasonal data, including boxplots for
  seasonal parameters, and bars for summed normals. There are many specific
  functions related to climatology, including precipitation normals,
  temperature normals, cumulative precipitation departures and precipitation
  interarrivals. However, this package is designed to represent any
  time-varying parameter with a discernible seasonal signal, such as found
  in hydrology and ecology.
Routines for the seasonal analysis of health data, including regression models, time-stratified case-crossover, plotting functions and residual checks, see Barnett and Dobson (2010) ISBN 978-3-642-10748-1. Thanks to Yuming Guo for checking the case-crossover code.
Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment
    software by the US Census Bureau. It offers full access to almost all
    options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model
    search, outlier detection and support for user defined holiday variables,
    such as Chinese New Year or Indian Diwali. A graphical user interface can be
    used through the 'seasonalview' package. Uses the X-13-binaries from the
    'x13binary' package.
Compiles a set of functions and dummy data that simplify reconstructions of seasonal temperature
	 variability in the geological past from stable isotope and clumped isotope records in sub–annually resolved
	 carbonate archives (e.g. mollusk shells, corals and speleothems). For more information, see
	 de Winter et al., 2020 (Climate of the Past Discussions, <doi:10.5194/cp-2020-118>).
The price action at any given time is determined by investor 
  sentiment and market conditions. Although there is no established principle, 
  over a long period of time, things often move with a certain periodicity.
  This is sometimes referred to as anomaly. 
  The seasonPlot() function in this package calculates and visualizes the 
  average value of price movements over a year for any given period. 
  In addition, the monthly increase or decrease in price movement is 
  represented with a colored background. 
  This seasonPlot() function can use the same symbols as the 'quantmod' package 
  (e.g. ^IXIC, ^DJI, SPY, BTC-USD, and ETH-USD etc).
A graphical user interface to the 'seasonal' package and
  'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software. 
  Unifies the code base of <http://www.seasonal.website> and the GUI in the
  'seasonal' package.
An overall test for seasonality of a given time
    series in addition to a set of individual seasonality tests as
    described by Ollech and Webel (forthcoming): An overall seasonality
    test. Bundesbank Discussion Paper.
A model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables. See Ryberg and York, 2020, <doi:10.3133/ofr20201082>.
Most of the current methods explore spatial association using 
             observations at sample locations, which are defined as the first 
             dimension of spatial association (FDA). The proposed concept of
             the second dimension of spatial association (SDA), 
             as described in Yongze Song (2022) 
             <doi:10.1016/j.jag.2022.102834>, aims to extract
             in-depth information about the geographical environment from 
             locations outside sample locations for exploring spatial
             association.
These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries  data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying  correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from  fleet segment to metier  level.
Density, distribution function, quantile function, random
    generation and survival function for the Secant Kumaraswamy Weibull Distribution
    as defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
Estimating parameters of site clusters on 2D & 3D square lattice with various lattice sizes, relative fractions of open sites (occupation probability), iso- & anisotropy, von Neumann & Moore (1,d)-neighborhoods, described by Moskalev P.V. et al. (2011) <arXiv:1105.2334v1>.
Functions to estimate the density and size of a spatially 
  distributed animal population sampled with an array of passive detectors, 
  such as traps, or by searching polygons or transects. Models incorporating 
  distance-dependent detection are fitted by maximizing the likelihood. 
  Tools are included for data manipulation and model selection.
Tools for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'. Extensions in version 2.5.0 include costing and evaluation of detector spacing.
Allow sharing sensitive information, for example passwords,
    'API' keys, etc., in R packages, using public key cryptography.
Encrypt text using a simple shifting substitution cipher with setcode(), providing two numeric keys used to define the encryption algorithm. The resulting text can be decoded using decode() function and the two numeric keys specified during encryption.
Tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package 'secr'.
Sequential factor extraction via co-sparse unit-rank estimation (SeCURE).
Seamless and standardized interaction with data
    exported from the clinical data management system (CDMS)
    'secuTrial'<https://www.secutrial.com>.
    The primary data export the package works with is a standard non-rectangular export.
Proxy forward modelling for sediment archived climate proxies such 
  as Mg/Ca, d18O or Alkenones. The user provides a hypothesised "true" past climate, 
  such as output from a climate model, and details of the sedimentation rate and 
  sampling scheme of a sediment core. Sedproxy returns simulated proxy records. 
  Implements the methods described in Dolman and Laepple (2018) 
  <doi:10.5194/cp-14-1851-2018>.
Provides plotting utilities supporting packages in the 'easystats'
    ecosystem (<https://github.com/easystats/easystats>) and some extra themes,
    geoms, and scales for 'ggplot2'. Color scales are based on
    <https://materialui.co/colors>. 
    References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.
Provides a wrapper to access data from the SeeClickFix
  web API for R. SeeClickFix is a central platform employed by many cities
  that allows citizens to request their city's services. This package
  creates several functions to work with all the built-in calls to the
  SeeClickFix API. Allows users to download service request data from
  numerous locations in easy-to-use dataframe format manipulable in
  standard R functions.
Output colors used in literal vectors, palettes and plot objects (ggplot).
Functions to calculate seed germination and seedling emergence and growth indexes. The main indexes for germination and seedling emergence, considering the time for seed germinate are: T10, T50 and T90, in Farooq et al. (2005) <10.1111/j.1744-7909.2005.00031.x>; and MGT, in Labouriau (1983). Considering the germination speed are: Germination Speed Index, in Maguire (1962), Mean Germination Rate, in Labouriau (1983); considering the homogeneity of germination are: Coefficient of Variation of the Germination Time, in Carvalho et al. (2005) <10.1590/S0100-84042005000300018>, and Variance of Germination, in Labouriau (1983); Uncertainty, in Labouriau and Valadares (1976) <ISSN:0001-3765>; and Synchrony, in Primack (1980). The main seedling indexes are Growth, in Sako (2001), Uniformity, in Sako (2001) and Castan et al. (2018) <doi:10.1590/1678-992x-2016-0401>; and Vigour, in Medeiros and Pereira (2018) <doi:10.1590/1983-40632018v4852340>.
Functions for dimension reduction through the seeded canonical correlation analysis are provided. A classical canonical correlation analysis (CCA) is one of useful statistical methods in multivariate data analysis, but it is limited in use due to the matrix inversion for large p small n data. To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo (2015) \doi{10.1002/cem.2691}. The seeded CCA is a two-step procedure. The sets of variables are initially reduced by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on canonical correlation analysis, following Cook, Li and Chiaromonte (2007) \doi{10.1093/biomet/asm038} and Lee and Yoo (2014) \doi{10.1111/anzs.12057}. Then, the canonical correlation is finalized with the initially-reduced two sets of variables.
Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) <doi:10.1177/08944393231178605>.
    Implements Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012) <doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing.
On-target gene knockdown using siRNA ideally results from binding 
    fully complementary regions in mRNA transcripts to induce cleavage. 
    Off-target siRNA gene knockdown can occur through several modes, one being 
    a seed-mediated mechanism mimicking miRNA gene regulation. Seed-mediated 
    off-target effects occur when the ~8 nucleotides at the 5’ end of the guide 
    strand, called a seed region, bind the 3’ untranslated regions of mRNA, 
    causing reduced translation. Experiments using siRNA knockdown paired with 
    RNA-seq can be used to detect siRNA sequences with potential off-target 
    effects driven by the seed region. 'SeedMatchR' provides tools for exploring 
    and detecting potential seed-mediated off-target effects of siRNA in RNA-seq 
    experiments. 'SeedMatchR' is designed to extend current differential 
    expression analysis tools, such as 'DESeq2', by annotating results with 
    predicted seed matches. Using publicly available data, we demonstrate the 
    ability of 'SeedMatchR' to detect cumulative changes in differential gene 
    expression attributed to siRNA seed regions.
Analysis of seed germination data
    using the physiological time modelling approach. Includes functions
    to fit hydrotime and thermal-time models with the traditional approaches
    of Bradford (1990) <doi:10.1104/pp.94.2.840>
    and Garcia-Huidobro (1982) <doi:10.1093/jxb/33.2.288>. 
    Allows to fit models to grouped datasets,
    i.e. datasets containing multiple species, seedlots or experiments.
Regression analysis using common models in seed temperature studies, such as the Gaussian model (Martins, JF, Barroso, AAM, & Alves, PLCA (2017) <doi:10.1590/s0100-83582017350100039>), quadratic (Nunes, AL, Sossmeier, S, Gotz, AP, & Bispo, NB (2018) <doi: 10.17265/2161-6264/2018.06.002>) and others with potential for use, such as those implemented in the 'drc' package (Ritz, C, Baty, F, Streibig, JC, & Gerhard, D (2015). <doi:10.1371/journal.pone.0146021>), in the estimation of the ideal and cardinal temperature for the occurrence of plant seed germination. The functions return graphs with the equations automatically.
Algorithms to calculate the hidden inputs of systems of differential equations. 
  These hidden inputs can be interpreted as a control that tries to minimize the
  discrepancies between a given model and taken measurements. The idea is 
  also called the Dynamic Elastic Net, as proposed in the paper "Learning (from) the errors of a systems biology model" 
  (Engelhardt, Froelich, Kschischo 2016) <doi:10.1038/srep20772>.
  To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: <https://bioconductor.org/packages/release/bioc/html/rsbml.html>.
Wrapper around various existing tools and command-line interfaces,
  providing a standard interface, simple parallelization, and detailed logging.
  For microarray data, maps probe sets to standard gene IDs, building on
  'GEOquery' Davis and Meltzer (2007) <doi:10.1093/bioinformatics/btm254>,
  'ArrayExpress' Kauffmann et al. (2009) <doi:10.1093/bioinformatics/btp354>,
  Robust multi-array average 'RMA' Irizarry et al. (2003) <doi:10.1093/biostatistics/4.2.249>,
  and 'BrainArray' Dai et al. (2005) <doi:10.1093/nar/gni179>.
  For RNA-seq data, fetches metadata and raw reads from National Center for Biotechnology
  Information (NCBI) Sequence Read Archive (SRA), performs standard adapter and
  quality trimming using 'TrimGalore' Krueger <https://github.com/FelixKrueger/TrimGalore>,
  performs quality control checks using 'FastQC' Andrews <https://github.com/s-andrews/FastQC>,
  quantifies transcript abundances using 'salmon' Patro et al. (2017) <doi:10.1038/nmeth.4197> and potentially
  'refgenie' Stolarczyk et al. (2020) <doi:10.1093/gigascience/giz149>,
  aggregates the results using 'MultiQC' Ewels et al. (2016) <doi:10.1093/bioinformatics/btw354>,
  maps transcripts to genes using 'biomaRt' Durinkck et al. (2009) <doi:10.1038/nprot.2009.97>,
  and summarizes transcript-level quantifications for gene-level analyses using
  'tximport' Soneson et al. (2015) <doi:10.12688/f1000research.7563.2>.
A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.
Creates SEER (Surveillance, Epidemiology and End Results) and 
    A-bomb data binaries from ASCII sources and provides tools for estimating
    SEER second cancer risks. Methods are described in <doi:10.1038/leu.2015.258>.
Provides an easy way to map seer registry area rate data on a U.S. map.  
   The U.S. data may be mapped at the state or state/county.
   U.S. Seer registry data may be mapped at the Seer registry area, Seer Registry 
   area/county or Seer Registry area/county/census tract level. The function uses 
   a calculated default categorization breakpoint list for 5 categories, 
   when not breakpoint list is provided or the number of categories is specified 
   by the user. A user provided break point list is limited to containing 5 values.  
   The number of calculated categories may be from 3 to 11. The user provide the p-value 
   for each area and request hatching over any areas with a p-value > 0.05. 
   Other types of comparisons can be specified.  If states or 
   state/counties are used, the area identifier is the U.S. FIPS codes for states and 
   counties, 2 digits and 5 digits respectfully.  If the data is for U.S. Seer Registry 
   areas, the Seer Registry area identifier used to link the data to the geographical 
   area is a Seer Registry area abbreviation.  See documentation for the list of 
   acceptable Seer Registry area names and abbreviations. The state boundaries are 
   overlaid all rate maps. The package contains the boundary data for state, county, 
   Seer Registry areas, and census tracts for counties within a Seer registry area.   
   The package support boundary data from the 2000 and 2010 U.S. Census.  
   The SeerMapper package version contains the U.S. Census 2000 and 2010 boundary data
   for the regional, state, Seer Registry, and county levels.  Six supplement packages 
   contain the census tract boundary data.
   Copyrighted 2014, 2015, 2016, 2017, 2018 and 2019 by Pearson and Pickle.
Provides supplemental 2010 census tract boundary package for 20 states
   without Seer Registries that are east of the Mississippi river 
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. Census data and is in public domain.
Provides supplemental 2010 census tract boundaries of the 19 states 
   containing Seer Registries for use with the 'SeerMapper' package.
   The data contained in this 
   package is derived from U.S. 2010 Census data and is in public domain.
Provides supplemental 2010 census tract boundaries for the 13 states
   without Seer Registries that are west of the Mississippi river 
   for use with the 'SeerMapper' package.
   The data contained in this 
   package is derived from U.S. 2010 Census data and is in public domain.
Provides supplemental 2000 census tract boundaries for the 20 states, district and territory
   without Seer Registries that are east of the Mississippi river 
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. Census data and is in the public domain.
Provides supplemental 2000 census tract boundaries for the 19 states
   containing Seer Registries for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. Census data and is in the public domain.
Provides supplemental 2000 census tract boundaries for the 13 states
   without Seer Registries that are west of the Mississippi river
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. 2000 Census data and is in the public domain. 
   Updated to Alber Equal Area projection.
Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound).  This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) <doi:10.1080/09524622.2008.9753600> and Sueur (2018) <doi:10.1007/978-3-319-77647-7>.
Measuring spatial segregation. The methods implemented in this 
        package include White's P index (1983) <doi:10.1086/227768>, 
        Morrill's D(adj) (1991), Wong's D(w) and D(s) (1993) 
        <doi:10.1080/00420989320080551>, and Reardon and O'Sullivan's set of 
        spatial segregation measures (2004) 
        <doi:10.1111/j.0081-1750.2004.00150.x>.
Provides two methods for segmentation and joint segmentation/clustering of
    bivariate time-series. Originally intended for ecological segmentation
    (home-range and behavioural modes) but easily applied on other series,
    the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'.
    The segmentation method is a bivariate extension of  Lavielle's method available in 'adehabitatLT' 
    (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>).
    This method rely on dynamic programming for efficient segmentation.
    The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm.
    This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method 
    (formerly available in 'cghseg' package) to the bivariate case.
    The method is fully described in Patin et al (2018) <doi:10.1101/444794>.
Performs correlation matrix segmentation and applies a test
    procedure to detect highly correlated regions in gene expression.
Proposes an application for sequence prediction generalizing the similarity within the network of previous sequences.
A set of segregation-based indices and randomization methods to 
            make robust environmental inequality assessments, as described in 
            Schaeffer and Tivadar (2019) "Measuring Environmental Inequalities:
            Insights from the Residential Segregation Literature" 
            <doi:10.1016/j.ecolecon.2019.05.009>.
Contains functions that help to determine event
    boundaries in event segmentation experiments by bootstrapping a critical
    segmentation magnitude under the null hypothesis that all key presses were
    randomly distributed across the experiment. Segmentation magnitude is
    defined as the sum of Gaussians centered at the times of the segmentation
    key presses performed by the participants. Within a participant, the maximum
    of the overlaid Gaussians is used to prevent an excessive influence of a
    single participant on the overall outcome (e.g. if a participant is pressing
    the key multiple times in succession). Further functions are included, such
    as plotting the results.
Given a regression model, segmented `updates' it by adding one or more segmented 
  (i.e., piece-wise linear) relationships. Several variables with multiple breakpoints are allowed. The estimation method is discussed in Muggeo (2003, <doi:10.1002/sim.1545>) and 
  illustrated in Muggeo (2008, <https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented 
  in Muggeo (2016, <doi:10.1080/00949655.2016.1149855>), and interval estimation for the breakpoint is discussed in Muggeo (2017, <doi:10.1111/anzs.12200>). 
  Segmented mixed models, i.e. random effects in the change point, are discussed in
  in Muggeo (2014, <doi:10.1177/1471082X13504721>).
A dynamic programming solution to segmentation based on
        maximization of arbitrary similarity measures within segments.
	The general idea, theory and this implementation are described in
	Machne, Murray & Stadler (2017) <doi:10.1038/s41598-017-12401-8>.
	In addition to the core algorithm, the package provides time-series
	processing and clustering functions as described in the publication.
	These are generally applicable where a `k-means` clustering yields
	meaningful results, and have been specifically developed for
	clustering of the Discrete Fourier Transform of periodic gene
	expression data (`circadian' or `yeast metabolic oscillations').
	This clustering approach is outlined in the supplemental material of
	Machne & Murray (2012) <doi:10.1371/journal.pone.0037906>), and here
	is used as a basis of segment similarity measures. Notably, the
	time-series processing and clustering functions can also be used as
	stand-alone tools, independent of segmentation, e.g., for 
        transcriptome data already mapped to genes.
Given a likelihood provided by the user, this package applies it
    to a given matrix dataset in order to find change points in the data that
    maximize the sum of the likelihoods of all the segments. This package provides
    a handful of algorithms with different time complexities and assumption compromises
    so the user is able to choose the best one for the problem at hand. The implementation
    of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro,
	Florencia Leonardi (2018) <arXiv:1501.01756>. The Berlin
	weather sample dataset was provided by Deutscher Wetterdienst <https://dwd.de/>.
	You can find all the references in the Acknowledgments section of this package's
	repository via the URL below.
A system that computes metrics to assess the segmentation 
    accuracy of geospatial data. These metrics calculate the discrepancy 
    between segmented and reference objects, and indicate the segmentation 
    accuracy. For more details on choosing evaluation metrics, we 
    suggest seeing Costa et al. (2018) <doi:10.1016/j.rse.2017.11.024> and 
    Jozdani et al. (2020) <doi:10.1016/j.isprsjprs.2020.01.002>.
Implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying 'common' change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure.
Tools for modeling non-continuous linear responses of ecological communities to environmental data. The package is straightforward through three steps: (1) data ordering (function OrdData()), (2) split-moving-window analysis (function SMW()) and (3) piecewise redundancy analysis (function pwRDA()). Relevant references include Cornelius and Reynolds (1991) <doi:10.2307/1941559> and Legendre and Legendre (2012, ISBN: 9780444538697).
Computes segregation indices, including the Index of Dissimilarity,
    as well as the information-theoretic indices developed by
    Theil (1971) <isbn:978-0471858454>, namely
    the Mutual Information Index (M) and Theil's Information Index (H).
    The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x>
    and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>,
    is a measure of segregation that is highly decomposable. The package provides
    tools to decompose the index by units and groups (local segregation),
    and by within and between terms. The package also provides a method to decompose
    differences in segregation as described by Elbers (2021) <doi:10.1177/0049124121986204>.
    The package includes standard error estimation by bootstrapping, which also corrects for
    small sample bias.
An implementation of the full-likelihood Bayes factor (FLB) 
    for evaluating segregation evidence in clinical medical genetics. The method 
    was introduced by Thompson et al. (2003) <doi:10.1086/378100>, and further 
    popularised by Bayrak-Toydemir et al. (2008) <doi:10.1016/j.yexmp.2008.03.006>.
    This implementation allows custom penetrance values and liability classes, and 
    includes specialised pedigree visualisations.
A very nice interface to Princeton's 'WordNet' without 'rJava' dependency. 'WordNet' data is not included. Princeton University makes 'WordNet' available to research and commercial users free of charge provided the terms of their license (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and proper reference is made to the project using an appropriate citation (<https://wordnet.princeton.edu/citing-wordnet>).
Extended Susceptible-Exposed-Infected-Recovery Model for 
    handling high false negative rate and symptom based administration of 
    diagnostic tests. <doi:10.1101/2020.09.24.20200238>.
An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See <http://snap.stanford.edu/seismic/> for more information and datasets.
Fast versions of seismic analysis functions that 'roll' over a
    vector of values. See the 'RcppRoll' package for alternative
    versions of basic statistical functions such as rolling mean,
    median, etc.
Set of functions that access information about deputies and votings
    in Polish diet from webpage <http://www.sejm.gov.pl>. The package was developed
    as a result of an internship in MI2 Group - <http://mi2.mini.pw.edu.pl>, Faculty
    of Mathematics and Information Science, Warsaw University of Technology.
Sejong(http://www.sejong.or.kr/) corpus and
        Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum)
        dictionaries for KoNLP
Implements a method for fitting a bounded probability
        distribution to quantiles (for example stated by an expert),
        see Bornkamp and Ickstadt (2009) for details.  For this
        purpose B-splines are used, and the density is obtained by
        penalized least squares based on a Brier entropy penalty.  The
        package provides methods for fitting the distribution as well
        as methods for evaluating the underlying density and cdf. In
        addition methods for plotting the distribution, drawing random
        numbers and calculating quantiles of the obtained distribution
        are provided.
Calculates (unconditional) post-selection confidence intervals
    and p-values for the coefficients of (generalized) linear models.
The objective of these functions is to derive a species assemblage
    that satisfies a functional trait profile. Restoring resilient ecosystems
    requires a flexible framework for selecting assemblages that are based on the
    functional traits of species. However, current trait-based models have been
    limited to algorithms that can only select species by optimising specific trait
    values, and could not elegantly accommodate the common desire among restoration
    ecologists to produce functionally diverse assemblages. We have solved this
    problem by applying a non-linear optimisation algorithm that optimises Rao Q,
    a closed-form functional trait diversity index that incorporates species
    abundances, subject to other linear constraints. This framework generalises
    previous models that only optimised the entropy of the community, and can
    optimise both functional diversity and entropy simultaneously. This package
    can also be used to generate experimental assemblages to test the effects
    of community-level traits on community dynamics and ecosystem function. The
    method is based on theory discussed in Laughlin (2014, Ecology Letters)
    <doi.org/10.1111/ele.12288>.
Provides indices such as Manly's alpha, foraging ratio, and Ivlev's selectivity to allow for analysis of dietary selectivity and preference. Can accommodate multiple experimental designs such as constant prey number of prey depletion. Please contact the package maintainer with any publications making use of this package in an effort to maintain a repository of dietary selections studies.
An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.
The aim of most plant breeding programmes is simultaneous improvement of several characters. An objective method involving simultaneous selection for several attributes then becomes necessary. It has been recognised that most rapid improvements in the economic value is expected from selection applied simultaneously to all the characters which determine the economic value of a plant, and appropriate assigned weights to each character according to their economic importance, heritability and correlations between characters. So the selection for economic value is a complex matter. If the component characters are combined together into an index in such a way that when selection is applied to the index, as if index is the character to be improved, most rapid improvement of economic value is expected. Such an index was first proposed by Smith (1937 <doi:10.1111/j.1469-1809.1936.tb02143.x>) based on the Fisher's (1936 <doi:10.1111/j.1469-1809.1936.tb02137.x>) "discriminant function" Dabholkar (1999 <https://books.google.co.in/books?id=mlFtumAXQ0oC&lpg=PA4&ots=Xgxp1qLuxS&dq=elements%20of%20biometrical%20genetics&lr&pg=PP1#v=onepage&q&f=false>). In this package selection index is calculated based on the Smith (1937) selection index method.
Computes bounds and sensitivity parameters as part of sensitivity
    analysis for selection bias. Two bounds are provided: the SV (Smith and
    VanderWeele) bound and the AF (assumption-free) bound. The SV bound assumes
    an additional dependence structure in form of a generalized M-structure
    whereas the AF bound holds for general assumptions in the potential
    outcomes framework. See Smith and VanderWeele (2019)
    <doi:10.1097/EDE.0000000000001032> and Zetterstrom and Waernbaum (2022)
    <doi:10.1515/em-2022-0108>.
Multi-stage selection is practiced in numerous fields of life
    and social sciences and particularly in breeding. A special characteristic of
    multi-stage selection is that candidates are evaluated in successive stages
    with increasing intensity and effort, and only a fraction of the superior
    candidates is selected and promoted to the next stage. For the optimum design
    of such selection programs, the selection gain plays a crucial role. It can be
    calculated by integration of a truncated multivariate normal (MVN) distribution.
    While mathematical formulas for calculating the selection gain and the variance
    among selected candidates were developed long time ago, solutions for numerical
    calculation were not available. This package can also be used for optimizing
    multi-stage selection programs for a given total budget and different costs of
    evaluating the candidates in each stage.
New tools for post-selection inference, for use with forward
    stepwise regression, least angle regression, the lasso, and the many means
    problem. The lasso function implements Gaussian, logistic and Cox survival
    models.
Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).
Translates a CSS3 selector into an equivalent XPath
  expression. This allows us to use CSS selectors when working with
  the XML package as it can only evaluate XPath expressions. Also
  provided are convenience functions useful for using CSS selectors on
  XML nodes. This package is a port of the Python package 'cssselect'
  (<https://cssselect.readthedocs.io/>).
Fit and selects point pattern models based on minimum contrast, AIC and and goodness of fit.
Detection of outliers and influential errors using a latent variable model.
The W3C WebDriver specification defines a way for out-of-process
    programs to remotely instruct the behaviour of web browsers. It is detailed
    at <https://w3c.github.io/webdriver/webdriver-spec.html>. This package provides
    an R client implementing the W3C specification.
Provides the SELF criteria to learn causal structure. Please cite "Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao. SELF: Structural Equational Embedded Likelihood Framework for Causal Discovery. AAAI. 2018."
A probability tree allows to compute probabilities of
	     complex events, such as genotype probabilities in intermediate generations of inbreeding
	     through recurrent self-fertilization (selfing). This package implements functionality to compute
	     probability trees for two- and three-marker genotypes in the F2 to F7 selfing
	     generations. The conditional probabilities are derived automatically
	     and in symbolic form. The package also provides functionality to
	     extract and evaluate the relevant probabilities.
Functions for fitting general linear structural
    equation models (with observed and latent variables) using the RAM approach, 
    and for fitting structural equations in observed-variable models by two-stage least squares.
It offers functions for creating dashboard with Fomantic UI.
We present a rank-based Mercer kernel to compute a pair-wise similarity metric corresponding to informative representation of data. We tailor the development of a kernel to encode our prior knowledge about the data distribution over a probability space. The philosophical concept behind our construction is that objects whose feature values fall on the extreme of that feature’s probability mass distribution are more similar to each other, than objects whose feature values lie closer to the mean. Semblance emphasizes features whose values lie far away from the mean of their probability distribution. The kernel relies on properties empirically determined from the data and does not assume an underlying distribution. The use of feature ranks on a probability space ensures that Semblance is computational efficacious, robust to outliers, and statistically stable, thus making it widely applicable algorithm for pattern analysis. The output from the kernel is a square, symmetric matrix that gives proximity values between pairs of observations.
Interactive 'shiny' application for working with Structural Equation Modelling technique. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/semwebappk/> .
Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric and three-way input dissimilarities. It assumes that the dissimilarities are measured with errors. The latent dissimilarities are estimated as factor scores within an SEM framework while the objects are represented in a low-dimensional space as in MDS.
Automatically calculate direct, indirect, and total effects for 
    piecewise structural equation models, comprising lists of fitted models 
    representing structured equations (Lefcheck, 2016 <doi:10/f8s8rb>). 
    Confidence intervals are provided via bootstrapping.
Sensitivity analysis in structural equation modeling using
    influence measures and diagnostic plots. Support leave-one-out casewise
    sensitivity analysis presented by Pek and MacCallum (2011)
    <doi:10.1080/00273171.2011.561068> and approximate casewise influence
    using scores and casewise likelihood.
A framework for extracting semantic motifs around entities in textual data. It implements an entity-centered semantic grammar that distinguishes six classes of motifs: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity. Motifs are identified by applying a set of extraction rules to a parsed text object that includes part-of-speech tags and dependency annotations - such as those generated by 'spacyr'. For further reference, see: Stuhler (2022) <doi: 10.1177/00491241221099551>.
Estimate networks and causal relationships in complex systems through
  Structural Equation Modeling. This package also includes functions to import,
  weight, manipulate, and fit biological network models within the
  Structural Equation Modeling framework described in
  Grassi M, Palluzzi F, Tarantino B (2022) <doi:10.1093/bioinformatics/btac567>.
An assortment of helper functions for doing structural equation
  modeling, mainly by 'lavaan' for now. Most of them are time-saving functions
  for common tasks in doing structural equation modeling and reading the
  output. This package is not for functions that implement advanced statistical
  procedures. It is a light-weight package for simple functions that do simple
  tasks conveniently, with as few dependencies as possible.
Contains methods to generate and evaluate semi-artificial data sets. 
 Based on a given data set different methods learn data properties using machine learning algorithms and
 generate new data with the same properties.
 The package currently includes the following data generators:
  i) a RBF network based generator using rbfDDA() from package 'RSNNS',
  ii) a Random Forest based generator for both classification and regression problems
  iii) a density forest based generator for unsupervised data
 Data evaluation support tools include:
  a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance
  b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM
  c) evaluation based on classification performance with various learning models, e.g., random forests.
Use inverse probability weighting methods to estimate treatment effect under marginal structure model (MSM) for the transition hazard of semi competing risk data, i.e. illness death model. We implement two specific such models, the usual Markov illness death structural model and the general Markov illness death structural model. We also provide the predicted three risks functions from the marginal structure models. Zhang, Y. and Xu, R. (2022) <arXiv:2204.10426>.
Hierarchical multistate models are considered to perform the analysis of independent/clustered semi-competing risks data. The package allows to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions and cluster-specific random effects distribution; a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation approach for several parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.
Provides routines to check identifiability or non-identifiability
    of linear structural equation models as described in Drton, Foygel, and
    Sullivant (2011) <doi:10.1214/10-AOS859>, Foygel, Draisma, and Drton (2012) 
    <doi:10.1214/12-AOS1012>, and other works. The routines are based on the graphical 
    representation of structural equation models.
Semi-parametric estimation problem can be solved by two-step Newton-Raphson iteration. The implicit profiling method<arXiv:2108.07928> is an improved method of two-step NR iteration especially for the implicit-bundled type of the parametric part and non-parametric part. This package provides a function semislv() supporting the above two methods and numeric derivative approximation for unprovided Jacobian matrix.
Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) 	<doi:10.18637/jss.v066.i06>.
A powerful, easy to syntax for specifying and estimating complex 
    Structural Equation Models. Models can be estimated using Partial 
    Least Squares Path Modeling or Covariance-Based Structural Equation 
    Modeling or covariance based Confirmatory Factor Analysis. Methods described in Ray, Danks, and Valdez (2021).
Functions for semiparametric regression analysis, to
        complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J.
        (2003). Semiparametric Regression. Cambridge University Press.
Forms likelihood-based confidence intervals
    (LBCIs) for parameters in structural equation modeling,
    introduced in Cheung and Pesigan (2023)
    <doi:10.1080/10705511.2023.2183860>. Currently
    implements the algorithm illustrated by Pek and Wu
    (2018) <doi:10.1037/met0000163>, and supports the robust
    LBCI proposed by Falk (2018)
    <doi:10.1080/10705511.2017.1367254>.
Monte Carlo confidence intervals for free and defined parameters
    in models fitted in the structural equation modeling package 'lavaan'
    can be generated using the 'semmcci' package.
    'semmcci' has two main functions, namely, MC() and MCStd().
    The output of 'lavaan' is passed as the first argument
    to the MC() function to generate Monte Carlo confidence intervals.
    Monte Carlo confidence intervals for the standardized estimates
    can also be generated by passing the output of the MC() function
    to the MCStd() function.
    Preacher and Selig (2012) <doi:10.1080/19312458.2012.679848>.
Provides Markov Chain Monte Carlo (MCMC) routine for the 
             structural equation modelling described in 
             Maity et. al. (2020) <doi:10.1093/bioinformatics/btaa286>. This MCMC sampler is 
             useful when one attempts to perform an integrative survival analysis for multiple 
             platforms of the Omics data where the response is time to event and the 
             predictors are different omics expressions for different platforms.
Provides methods for constructing and maintaining a database of presentations in R. The presentations are either ones that the user gives or gave or presentations at a particular event or event series. The package also provides a plot method for the interactive mapping of the presentations using 'leaflet' by grouping them according to country, city, year and other presentation attributes. The markers on the map come with popups providing presentation details (title, institution, event, links to materials and events, and so on).
Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible.
Implements dictionaries that can be used in the 'SemNetCleaner' package. Also includes several functions aimed at facilitating the text cleaning analysis in the 'SemNetCleaner' package. This package is designed to integrate and update word lists and dictionaries based on each user's individual needs by allowing users to store and save their own dictionaries. Dictionaries can be added to the 'SemNetDictionaries' package by submitting user-defined dictionaries to <https://github.com/AlexChristensen/SemNetDictionaries>.
Latent repeated measures ANOVA (L-RM-ANOVA) is a structural 
    equation modeling based alternative to traditional repeated measures ANOVA.
    L-RM-ANOVA extends the latent growth components approach by 
    Mayer et al. (2012) <doi:10.1080/10705511.2012.713242> and introduces
    latent variables to repeated measures analysis.
Path diagrams and visual analysis of various SEM packages' output.
Provides a-priori, post-hoc, and compromise power-analyses
    for structural equation models (SEM).
Most function focus on specific ways to customize a graph.
             They use a 'qgraph' output  as the first argument, and return a
             modified 'qgraph' object. This allows the functions to be chained
             by a pipe operator.
Perform sensitivity analysis in structural equation modeling using 
    meta-heuristic optimization methods (e.g., ant colony optimization and others). 
    The references for the proposed methods are: 
    (1) Leite, W., & Shen, Z., Marcoulides, K., Fish, C., & Harring, J. (2022). 
    <doi:10.1080/10705511.2021.1881786>
    (2) Harring, J. R., McNeish, D. M., & Hancock, G. R. (2017)
    <doi:10.1080/10705511.2018.1506925>;
    (3) Fisk, C., Harring, J., Shen, Z., Leite, W., Suen, K., & Marcoulides, K. 
    (2022). <doi:10.1177/00131644211073121>;
    (4) Socha, K., & Dorigo, M. (2008) <doi:10.1016/j.ejor.2006.06.046>.
    We also thank Dr. Krzysztof Socha for sharing his research on ant colony optimization
    algorithm with continuous domains and associated R code,
    which provided the base for the development of this package.
Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments.
For confirmatory factor analysis ('CFA') and structural equation
             models ('SEM') estimated with the 'lavaan' package,
             this package provides functions to create model summary tables and model
             comparison tables for hypothesis testing. Tables can be produced in
             'LaTeX', 'HTML', or comma separated variables ('CSV').
Provides tools for structural equation modeling, many of which extend the 'lavaan' package; for example, to pool results from multiple imputations, probe latent interactions, or test measurement invariance.
SEM Trees and SEM Forests -- an extension of model-based decision
    trees and forests to Structural Equation Models (SEM). SEM trees hierarchically
    split empirical data into homogeneous groups each sharing similar data patterns
    with respect to a SEM by recursively selecting optimal predictors of these
    differences. SEM forests are an extension of SEM trees. They are ensembles of
    SEM trees each built on a random sample of the original data. By aggregating
    over a forest, we obtain measures of variable importance that are more robust
    than measures from single trees. A description of the method was published by
    Brandmaier, von Oertzen, McArdle, & Lindenberger (2013) <doi:10.1037/a0030001> 
    and Arnold, Voelkle, & Brandmaier (2020) <doi:10.3389/fpsyg.2020.564403>.
Tools and functions for parsing, rendering and operating on
    semantic version strings. Semantic versioning is a simple set of rules
    and requirements that dictate how version numbers are assigned and
    incremented as outlined at <http://semver.org>.
Semantic Versions allow for standardized management versions. 
    This package implements semantic versioning handling in R. using R6 to 
    create a mutable object that can handle deciphering and checking versions.
Functions to download Sentinel-2 optical images
 and perform preliminary processing operations.
 'sen2r' provides the instruments required to easily perform
 (and eventually automate) the steps necessary to build a complete
 Sentinel-2 processing chain.
 A Graphical User Interface to facilitate data processing is also provided.
 For additional documentation refer to the following article: 
 Ranghetti et al. (2020) <doi:10.1016/j.cageo.2020.104473>.
Send email using 'Sendgrid' <https://sendgrid.com/> 
            mail API(v3) <https://docs.sendgrid.com/api-reference/how-to-use-the-sendgrid-v3-api/authentication>.
A system enables cross study Analysis by extracting and filtering study data for 
    control animals from  'CDISC' 'SEND' Study Repository. 
    These data types are supported: Body Weights, Laboratory test results and Microscopic findings. 
    These database types are supported: 'SQLite' and 'Oracle'.
Package contains a simple SMTP client with minimal dependencies which 
        provides a portable solution for sending email, including file attachments and inline html reports, 
        from within R. SMTP Authentication and SSL/STARTTLS is implemented using curl.
Stacked ensemble for regression tasks based on 'mlr3' framework with a pipeline for preprocessing numeric and factor features and hyper-parameter tuning using grid or random search.
Implements a suite of sensitivity analysis tools 
  that extends the traditional omitted variable bias framework and makes it easier 
  to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.
Provides an interface to the 'Sensibo Sky' API which allows to remotely control non-smart air conditioning units.
  See <https://sensibo.com> for more informations.
An implementation of sensitivity analysis for phylogenetic comparative
 methods. The package is an umbrella of statistical and graphical methods that 
 estimate and report different types of uncertainty in PCM:
 (i) Species Sampling uncertainty (sample size; influential species and clades).
 (ii) Phylogenetic uncertainty (different topologies and/or branch lengths).
 (iii) Data uncertainty (intraspecific variation and measurement error).
A collection of functions for sensitivity analysis of model outputs (factor screening, global sensitivity analysis and robustness analysis), as well as for interpretability of machine learning models. Most of the functions have to be applied on scalar output, but several functions support multi-dimensional outputs.
Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.
Implements the calibrated sensitivity analysis approach for matched observational studies. Our sensitivity analysis framework views matched sets as drawn from a super-population. The unmeasured confounder is modeled as a random variable. We combine matching and model-based covariate-adjustment methods to estimate the treatment effect. The hypothesized unmeasured confounder enters the picture as a missing covariate. We adopt a state-of-art Expectation Maximization (EM) algorithm to handle this missing covariate problem in generalized linear models (GLMs). As our method also estimates the effect of each observed covariate on the outcome and treatment assignment, we are able to calibrate the unmeasured confounder to observed covariates.
      Zhang, B., Small, D. S. (2018). <arXiv:1812.00215>.
Sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition.  The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468).  The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al.  The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al.  The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test.  The other functions in the package are internal functions.
Sensitivity to unmeasured biases in an observational study that is a full match.  Function senfm() performs tests and function senfmCI() creates confidence intervals.  The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) <DOI:10.1111/j.1541-0420.2006.00717.x>.
Sensitivity analysis for multiple outcomes in observational studies.  For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>.  Alternatively, attention may focus on a few principal components in the principal() function.  The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.
The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean.  The main function in the package is senmv(), but amplify() and truncatedP() are also useful.  The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.
Sensitivity analysis for tests, confidence intervals and estimates in matched observational studies with one or more controls using weighted or unweighted Huber-Maritz M-tests (including the permutational t-test).   The method is from Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls JASA, 109(507), 1145-1158 <doi:10.1080/01621459.2013.879261>.
Provides Sensory and Consumer Data 
    mapping and analysis <doi:10.14569/IJACSA.2017.081266>. The mapping visualization is made available
    from several features : options in dimension reduction methods and prediction models ranging from 
    linear to non linear regressions. A smoothed version of the map performed using locally weighted regression algorithm 
    is available. A selection process of map stability is provided. A 'shiny' application is included. 
    It presents an easy GUI for the implemented functions as well as a comparative tool of fit models
    using several criteria. Basic analysis such as characterization of products,
    panelists and sessions likewise consumer segmentation are also made available.
We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) <doi:10.1002/sim.7620>. The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.
It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based sensitivity indices using several state-of-the-art first and total-order estimators. Sobol' indices can be computed either for models that yield a scalar as a model output or for systems of differential equations. The package also provides a suit of benchmark tests functions and several options to obtain publication-ready figures of the model output uncertainty and sensitivity-related analysis. An overview of the package can be found in Puy et al. (2022) <doi:10.18637/jss.v102.i05>.
Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008) <DOI:10.1111/j.1745-459X.2007.00137.x>.
Contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.
Perform biomarker evaluation and comparison in terms of specificity at a controlled sensitivity level, or sensitivity at a controlled specificity level. Point estimation and exact bootstrap of Huang, Parakati, Patil, and Sanda (2023) <doi:10.5705/ss.202021.0020> for the one- and two-biomarker problems are implemented.
Collection of datasets from Sen & Srivastava: "Regression
        Analysis, Theory, Methods and Applications", Springer.  Sources
        for individual data files are more fully documented in the
        book.
Sensitivity analysis in unmatched observational studies, with or without strata.  The main functions are sen2sample() and senstrat().  See Rosenbaum, P. R. and Krieger, A. M. (1990), JASA, 85, 493-498, <doi:10.1080/01621459.1990.10476226> and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545–555 <doi:10.1111/1467-9868.00249> .
Provides access and analytic functions for Sensus data.
Unsupervised text tokenizer allowing to perform byte pair encoding and unigram modelling. 
    Wraps the 'sentencepiece' library <https://github.com/google/sentencepiece> which provides a language independent tokenizer to split text in words and smaller subword units. 
    The techniques are explained in the paper "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing" by Taku Kudo and John Richardson (2018) <doi:10.18653/v1/D18-2012>.
    Provides as well straightforward access to pretrained byte pair encoding models and subword embeddings trained on Wikipedia using 'word2vec', 
    as described in "BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages" by Benjamin Heinzerling and Michael Strube (2018) <http://www.lrec-conf.org/proceedings/lrec2018/pdf/1049.pdf>.
Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. 
  In addition to out-performing traditional, lexicon-based sentiment analysis (see <https://benwiseman.github.io/sentiment.ai/#Benchmarks>),
  it also allows the user to create embedding vectors for text which can be used in other analyses.
  GPU acceleration is supported on Windows and Linux.
Performs a sentiment analysis of textual contents in R. This implementation
    utilizes various existing dictionaries, such as Harvard IV, or finance-specific 
    dictionaries. Furthermore, it can also create customized dictionaries. The latter 
    uses LASSO regularization as a statistical approach to select relevant terms based on 
    an exogenous response variable.
Calculate text polarity sentiment at the sentence level and
         optionally aggregate by rows or grouping variable(s).
Implements parsimonious mixtures of MSEN and MTIN distributions via expectation-
    maximization based algorithms for model-based clustering. For each mixture
    component, parsimony is reached via the eigen-decomposition of the scale 
    matrices and by imposing a constraint on the tailedness parameter. This produces
    a family of 28 parsimonious mixture models for each distribution.
Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) <doi:10.18637/jss.v099.i02>.
A framework that joins topic modeling and sentiment analysis of
  textual data. The package implements a fast Gibbs sampling estimation of
  Latent Dirichlet Allocation (Griffiths and Steyvers (2004)
  <doi:10.1073/pnas.0307752101>) and Joint Sentiment/Topic Model (Lin, He,
  Everson and Ruger (2012) <doi:10.1109/TKDE.2011.48>). It offers a variety of
  helpers and visualizations to analyze the result of topic modeling. The
  framework also allows enriching topic models with dates and externally
  computed sentiment measures. A flexible aggregation scheme enables the
  creation of time series of sentiment or topical proportions from the enriched
  topic models. Moreover, a novel method jointly aggregates topic proportions
  and sentiment measures to derive time series of topical sentiment.
Unofficial client for 'Sentry' <https://sentry.io>,
  a self-hosted or cloud-based error-monitoring service. It will inform about
  errors in real-time, and includes integration with the 'Plumber' package.
To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation).
    The methods used in this package are referenced from Zoran B. Popović (2017) <doi:10.21037/cdt.2017.03.12>.
Maximum likelihood estimation of the parameters
    of matrix and 3rd-order tensor normal distributions with unstructured
    factor variance covariance matrices, two procedures, and for unbiased
    modified likelihood ratio testing of simple and double separability
    for variance-covariance structures, two procedures.
Visual representations of model fit or predictive success in the form of "separation plots."  See Greenhill, Brian, Michael D. Ward, and Audrey Sacks. "The separation plot: A new visual method for evaluating the fit of binary models." American Journal of Political Science 55.4 (2011): 991-1002.
Stratigraphic ranges of fossil marine animal genera from Sepkoski's
    (2002) published compendium. No changes have been made to any taxonomic 
    names. However, first and last appearance intervals have been updated to be
    consistent with stages of the International Geological Timescale. 
    Functionality for generating a plot of Sepkoski's evolutionary fauna is also
    included. For specific details on the compendium see:
    Sepkoski, J. J. (2002). A compendium of fossil marine animal genera.
    Bulletins of American Paleontology, 363, pp. 1–560 (ISBN 0-87710-450-6).
    Access: <https://www.biodiversitylibrary.org/item/40634#page/5/mode/1up>.
This software is useful for loading '.fasta' or '.gbk' files, and for retrieving sequences from 'GenBank' dataset <https://www.ncbi.nlm.nih.gov/genbank/>. This package allows  to detect differences or asymmetries based on nucleotide composition by using local linear kernel smoothers. Also, it is possible to draw inference about critical points (i. e. maximum or minimum points) related with the derivative curves. Additionally, bootstrap methods have been used  for estimating confidence intervals and speed computational techniques (binning techniques) have been implemented in 'seq2R'.
Potential randomization schemes are prospectively evaluated when
    units are assigned to treatment arms upon entry into the experiment. The schemes
    are evaluated for balance on covariates and on predictability (i.e., how well
    could a site worker guess the treatment of the next unit enrolled).
This is a method for DNA Copy Number Profiling using
        Next-Generation Sequencing. It has new model and test
        statistics based on non-homogeneous Poisson Processes with
        change point models. It uses an adaptation of Circular Binary
        Segmentation. Also included are methods for point-wise Bayesian
        Confidence Interval and model selection method for the
        change-point model. A case and a control sample reads (normal
        and tumor) are required.
A modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.
Sequence detector in this package contains a specific automaton model that can be used to learn and detect data and process sequences.
    Automaton model in this package is capable of learning and tracing sequences. Automaton model can be found in Krleža, Vrdoljak, Brčić (2019) <doi:10.1109/ACCESS.2019.2955245>.
    This research has been partly supported under Competitiveness and Cohesion Operational Programme from the European Regional and Development Fund, as part of the Integrated Anti-Fraud System project no. KK.01.2.1.01.0041. This research has also been partly supported by the European Regional Development Fund under the grant KK.01.1.1.01.0009.
Sequential method to solve the the binary classification problem 
    by Wang (2019) <arXiv:arXiv:1901.10079>, multi-class 
    classification problem by Li (2020) <doi:10.1016/j.csda.2020.106911>
    and the highly stratified multiple-response problem by Chen (2019)
    <doi:10.1111/biom.13160>.
Generates the following sequential two-arm experimental designs:
    (1) completely randomized (Bernoulli)
    (2) balanced completely randomized
    (3) Efron's (1971) Biased Coin
    (4) Atkinson's (1982) Covariate-Adjusted Biased Coin
    (5) Kapelner and Krieger's (2014) Covariate-Adjusted Matching on the Fly
    (6) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (Naive)
    (7) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (Stepwise)
    and also provides the following types of inference:
    (1) estimation (with both Z-style estimators and OLS estimators), 
    (2) frequentist testing (via asymptotic distribution results and via employing the nonparametric randomization test) and
    (3) frequentist confidence intervals (only under the superpopulation sampling assumption currently). Details can be found
    in our publication: Kapelner and Krieger "A Matching Procedure for Sequential Experiments that Iteratively Learns which 
    Covariates Improve Power" (2020) <arXiv:2010.05980>.
Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.
Generates/modifies RNA-seq data for use in simulations. We provide
    a suite of functions that will add a known amount of signal to a real 
    RNA-seq dataset. The advantage of using this approach over simulating under
    a theoretical distribution is that common/annoying aspects of the data
    are more preserved, giving a more realistic evaluation of your method. 
    The main functions are select_counts(), thin_diff(), thin_lib(), 
    thin_gene(), thin_2group(), thin_all(), and effective_cor(). See
    Gerard (2020) <doi:10.1186/s12859-020-3450-9> for details on the
    implemented methods.
It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) <doi:10.1177/0049124115591013>), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) <doi:10.1177/0081175015570976>), smoothing sequences for index plots (see Piccarreta (2012) <doi:10.1177/0049124112452394>), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) <doi:10.1111/j.1467-985X.2009.00606.x>), symmetrical (or canonical) Partial Least Squares (see Bry (1996)).
Designed for fitting hidden (latent) Markov models and mixture
    hidden Markov models for social sequence data and other categorical time series.
    Also some more restricted versions of these type of models are available: Markov
    models, mixture Markov models, and latent class models. The package supports
    models for one or multiple subjects with one or multiple parallel sequences
    (channels). External covariates can be added to explain cluster membership in
    mixture models. The package provides functions for evaluating and comparing
    models, as well as functions for visualizing of multichannel sequence data and
    hidden Markov models. Models are estimated using maximum likelihood via the EM
    algorithm and/or direct numerical maximization with analytical gradients. All
    main algorithms are written in C++ with support for parallel computation. 
    Documentation is available via several vignettes in this page, and the  
    paper by Helske and Helske (2019, <doi:10.18637/jss.v088.i03>).
Contains an implementation of invariant causal prediction for sequential data. The main function in the package is 'seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method 'seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines 'seqICP.s' and 'seqICPnl.s' corresponding to the respective main methods.
Multiple imputation of missing data present in a dataset through the prediction based on either a random forest or a multinomial regression model. Covariates and time-dependant covariates can be included in the model. The prediction of the missing values is based on the method of Halpin (2012) <https://researchrepository.ul.ie/articles/report/Multiple_imputation_for_life-course_sequence_data/19839736>.
Exploratory data analysis and data visualization
  for biological sequence (DNA and protein) data. Seqinr includes
  utilities for sequence data management under the ACNUC system
  described in Gouy, M. et al. (1984) Nucleic Acids Res.
  12:121-127 <doi:10.1093/nar/12.1Part1.121>.
Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 <doi:10.1101/287839>.
A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.
Supports reading and writing sequences for different formats (currently interleaved and sequential formats for 'FASTA' and 'PHYLIP'), file conversion, and manipulation (e.g. filter sequences that contain specify pattern, export consensus sequence from an alignment).
Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R. This package can help you (1) read VCF/BCF/BGEN files by chromosomal ranges (e.g. 1:100-200); (2) read RareMETAL summary statistics files; (3) read tables from a tabix-indexed files; (4) annotate VCF/BCF files; (5) create customized workflow based on Makefile.
S4 class object for creating and managing group sequential designs. It calculates the efficacy and futility boundaries at each look. It allows modifying the design and tracking the design update history.
Methods to generate random gene-gene association networks and simulate RNA-seq data from them, as described in Grimes and Datta (2021) <doi:10.18637/jss.v098.i12>. Includes functions to generate random networks of any size and perturb them to obtain differential networks. Network objects are built from individual, overlapping modules that represent pathways. The resulting network has various topological properties that are characteristic of gene regulatory networks. RNA-seq data can be generated such that the association among gene expression profiles reflect the underlying network. A reference RNA-seq dataset can be provided to model realistic marginal distributions. Plotting functions are available to visualize a network, compare two networks, and compare the expression of two genes across multiple networks.
Sequential triangular test for the arithmetic mean in one- and two-
    samples, proportions in one- and two-samples, and the Pearson's correlation
    coefficient.
Educational package used in R courses to illustrate
       	     object-oriented programming and package
       	     development. Using biological sequences (DNA and RNA) as
       	     a working example.
Contains fast functions to calculate the exact Bayes posterior
    for the Sparse Normal Sequence Model, implementing the algorithms
    described in Van Erven and Szabo (2021,
    <doi:10.1214/20-BA1227>). For general hierarchical
    priors, sample sizes up to 10,000 are feasible within half an hour
    on a standard laptop. For beta-binomial spike-and-slab priors, a
    faster algorithm is provided, which can handle sample sizes of
    100,000 in half an hour. In the implementation, special care has
    been taken to assure numerical stability of the methods even for
    such large sample sizes.
Functions to calculate exact critical values, statistical power, expected time to signal, and required sample sizes for performing exact sequential analysis. All these	calculations can be done for either Poisson or binomial data, for continuous or group sequential analyses, and for different types of rejection boundaries. In case of group sequential analyses, the group sizes do not have to be specified in advance and the alpha spending can be arbitrarily settled.
Functions to be used in conjunction with the 'Sequential' package that allows for planning of observational database studies that will be analyzed with exact sequential analysis. This package supports Poisson- and binomial-based data. The primary function, seq_wrapper(...), accepts parameters for simulation of a simple exposure pattern and for the 'Sequential' package setup and analysis functions. The exposure matrix is used to simulate the true and false positive and negative populations (Green (1983) <doi:10.1093/oxfordjournals.aje.a113521>, Brenner (1993) <doi:10.1093/oxfordjournals.aje.a116805>). Functions are then run from the 'Sequential' package on these populations, which allows for the exploration of outcome misclassification in data.
Multi-generational pedigree inference from incomplete data on
    hundreds of SNPs, including parentage assignment and sibship clustering.
    See Huisman (2017) (<DOI:10.1111/1755-0998.12665>) for more information.
Apache Drill is a low-latency distributed query engine designed to enable 
    data exploration and analysis on both relational and non-relational data stores, 
    scaling to petabytes of data. Methods are provided that enable working with Apache 
    Drill instances via the REST API, DBI methods
    and using 'dplyr'/'dbplyr' idioms. Helper functions are included to facilitate
    using official Drill Docker images/containers.
Enables reading and writing binary and ASCII data to 
             RS232/RS422/RS485 or any other virtual serial interface of the
             computer.
Infrastructure for ordering objects with an implementation of several
    seriation/sequencing/ordination techniques to reorder matrices, dissimilarity
    matrices, and dendrograms. Also provides (optimally) reordered heatmaps,
    color images and clustering visualizations like dissimilarity plots, and
    visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) <doi:10.18637/jss.v025.i03>.
Calculate and compare lower confidence bounds for binomial series system reliability. The R 'shiny' application, launched by the function launch_app(), weaves together a workflow of customized simulations and delta coverage calculations to output recommended lower confidence bound methods.
A regularization method for the cumulative link
    models.  The smooth-effect-on-response penalty (SERP) provides
    flexible modelling of the ordinal model by enabling the smooth
    transition from the general cumulative link model to a coarser form of
    the same model. In other words, as the tuning parameter goes from zero
    to infinity, the subject-specific effects associated with each
    variable in the model tend to a unique global effect. The parameter
    estimates of the general cumulative model are mostly unidentifiable or
    at least only identifiable within a range of the entire parameter
    space. Thus, by maximizing a penalized rather than the usual
    non-penalized log-likelihood, this and other numerical problems common
    with the general model are to a large extent eliminated. Fitting is
    via a modified Newton's method. Several standard model performance and
    descriptive methods are also available. For more details on the penalty
    implemented here, see, Ugba (2021) <doi:10.21105/joss.03705> and 
    Ugba et al. (2021) <doi:10.3390/stats4030037>.
The primary goal of 'Serpstat' API <https://serpstat.com/api/> 
    is to reduce manual SEO (search engine optimization) and PPC (pay-per-click) 
    tasks. You can automate your keywords research or competitors analysis 
    with this API wrapper.
Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive
    mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS),
    using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate
    observations of SERRS are highly collinear and lend themselves to a reduced-rank
    representation. The GAMM separates the SERRS signal into three components: a
    sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline;
    and additive white noise. The parameters of each component of the model are estimated
    iteratively using SMC. The posterior distributions of the parameters given the observed
    spectra are represented as a population of weighted particles.
Functions that facilitate and speed up the analysis of data
    produced by a Syntech servosphere <http://www.ockenfels-syntech.com/products/locomotion-compensation/>,
    which is equipment for studying the movement behavior of arthropods.
    This package is designed to make working with data produced from a 
    servosphere easy for someone new to or unfamiliar with R. The functions
    provided in this package fall into three broad-use categories: functions for
    cleaning raw data produced by the servosphere software, functions for
    deriving movement variables based on position data, and functions for 
    summarizing movement variables for easier analysis. These functions are
    built with functions from the tidyverse package to work efficiently, as a
    single servosphere file may consist of hundreds of thousands of rows of data
    and a user may wish to analyze hundreds of files at a time. Many of the 
    movement variables derivable through this package are described in the 
    following papers:
    Otálora-Luna, Fernando; Dickens, Joseph C. (2011) <doi:10.1371/journal.pone.0020990>
    Party, Virginie; Hanot, Christophe; Busser, Daniela Schmidt; Rochat, Didier; Renou, Michel (2013) <doi:10.1371/journal.pone.0052897>
    Bell, William J.; Kramer, Ernest (1980) <doi:10.1007/BF01402908>
    Becher, Paul G; Guerin, Patrick M. (2009) <doi:10.1016/j.jinsphys.2009.01.006>.
Start an HTTP server in R to serve static files, or dynamic
    documents that can be converted to HTML files (e.g., R Markdown) under a
    given directory.
Structural equation modeling is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with inter-correlated dependent and independent variables. Here we implement a simple method for spatially explicit structural equation modeling based on the analysis of variance co-variance matrices calculated across a range of lag distances. This method provides readily interpreted plots of the change in path coefficients across scale.
Tools to compute and analyze the set of statistically-equivalent (Gaussian, linear) path models which generate the input precision or (partial) correlation matrix. 
             This procedure is useful for understanding how statistical network models such as the Gaussian Graphical Model (GGM) perform as causal discovery tools. 
             The statistical-equivalence set of a given GGM expresses the uncertainty we have about the sign, size and direction of directed relationships based on the weights matrix of the GGM alone. 
             The derivation of the equivalence set and its use for understanding GGMs as causal discovery tools is described by Ryan, O., Bringmann, L.F., & Schuurman, N.K. (2022) <doi: 10.31234/osf.io/ryg69>.
Randomization of presence/absence species distribution raster data 
    with or without including spatial structure for calculating standardized effect
    sizes and testing null hypothesis.
    The spatially unstructured randomization algorithms are based on fixed-fixed
    algorithms for matrices (Gotelli 2000, <doi:10.2307/177478>).
    The spatially structured randomization algorithm is based on the preserved richness
    null model (Laffan & Crisp 2003, <doi:10.1046/j.1365-2699.2003.00875.x>).
Query and print information about the current R
    session.  It is similar to 'utils::sessionInfo()', but includes more
    information about packages, and where they were installed from.
More easy to get intersection, union or complementary set and 
    combinations.
The implementation of a forecasting-specific tree-based model that is in particular suitable for global time series forecasting, as proposed in Godahewa et al. (2022) <arXiv:2211.08661v1>. The model uses the concept of Self Exciting Threshold Autoregressive (SETAR) models to define the node splits and thus, the model is named SETAR-Tree. The SETAR-Tree uses some time-series-specific splitting and stopping procedures. It trains global pooled regression models in the leaves allowing the models to learn cross-series information. The depth of the tree is controlled by conducting a statistical linearity test as well as measuring the error reduction percentage at each node split. Thus, the SETAR-Tree requires minimal external hyperparameter tuning and provides competitive results under its default configuration. A forest is developed by extending the SETAR-Tree. The SETAR-Forest combines the forecasts provided by a collection of diverse SETAR-Trees during the forecasting process.
Functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the 	examples in the books by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press.
Implements an algorithm to conduct advanced
    gene set enrichment analysis on the results of genomics experiments.
Provides utilities to help set and record the setting of
	the seed and the uniform and normal generators used when a random
	experiment is run. The utilities can be used in other functions 
	that do random experiments to simplify recording and/or setting all the 
	necessary information for reproducibility. 
	See the vignette and reference manual for examples.
Data structures and basic operations for ordinary sets,
             generalizations such as fuzzy sets, multisets, and
             fuzzy multisets, customizable sets, and intervals.
Mutators to set attributes of variables, that work well in a pipe
    (much like stats::setNames()).
It provides cumulative distribution function (CDF),
    quantile, p-value, statistical power calculator and random number generator
    for a collection of group-testing procedures, including the Higher Criticism
    tests, the one-sided Kolmogorov-Smirnov tests, the one-sided Berk-Jones tests,
    the one-sided phi-divergence tests, etc. The input are a group of p-values.
    The null hypothesis is that they are i.i.d. Uniform(0,1). In the context of
    signal detection, the null hypothesis means no signals. In the context of the
    goodness-of-fit testing, which contrasts a group of i.i.d. random variables to
    a given continuous distribution, the input p-values can be obtained by the CDF
    transformation. The null hypothesis means that these random variables follow the
    given distribution. For reference, see Hong Zhang, Jiashun Jin and Zheyang Wu. 
    "Distributions and Statistical Power of Optimal Signal-Detection Methods In Finite Cases",
    submitted.
Provides option settings management that goes
    beyond R's default 'options' function. With this package, users can define
    their own option settings manager holding option names, default values and 
    (if so desired) ranges or sets of allowed option values that will be 
    automatically checked. Settings can then be retrieved, altered and reset 
    to defaults with ease. For R programmers and package developers it offers 
    cloning and merging functionality which allows for conveniently defining 
    global and local options, possibly in a multilevel options hierarchy. See 
    the package vignette for some examples concerning functions, S4 classes, 
    and reference classes. There are convenience functions to reset par() 
    and options() to their 'factory defaults'.
A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
Defines S4 classes for single-cell genomic data and associated
  information, such as dimensionality reduction embeddings, nearest-neighbor
  graphs, and spatially-resolved coordinates. Provides data access methods and
  R-native hooks to ensure the Seurat object is familiar to other R users. See
  Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>,
  Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>,
  and Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031> for
  more details.
Customise 'Shiny' disconnected screens as well as sanitize error messages to make them clearer and friendlier to the user.
Provides a simple interface to developing complex data pipelines which can be executed in a single call.
    'sewage' makes it easy to test, debug, and share data pipelines through it's interface and visualizations.
Support for simple features, a standardized way to
    encode spatial vector data. Binds to 'GDAL' for reading and writing
    data, to 'GEOS' for geometrical operations, and to 'PROJ' for
    projection conversions and datum transformations. Uses by default the 's2'
    package for spherical geometry operations on ellipsoidal (long/lat) coordinates.
Stochastic frontier analysis with advanced methods.
 In particular, it applies the approach proposed by Latruffe et al. (2017) 
 <DOI:10.1093/ajae/aaw077> to estimate a stochastic frontier with technical 
 inefficiency effects when one input is endogenous.
Maximum likelihood estimation for stochastic frontier analysis (SFA) of production 
    (profit) and cost functions. 
    The package includes several distributions for the one-sided error term (i.e. Rayleigh, 
    Gamma, Weibull, lognormal, uniform, generalized exponential and truncated skewed Laplace) 
    as well as the latent class stochastic frontier model (LCM) as described in Dakpo et al. (2021) 
 <doi:10.1111/1477-9552.12422>. 
    Several possibilities in terms of optimization algorithms are proposed.
Support for reading/writing simple feature ('sf') spatial objects from/to 'Parquet' files. 'Parquet' files are an open-source, column-oriented data storage format from Apache (<https://parquet.apache.org/>), now popular across programming languages. This implementation converts simple feature list geometries into well-known binary format for use by 'arrow', and coordinate reference system information is maintained in a standard metadata format.
Provides a function sfc() to compute the substance flow
    with the input files --- "data" and "model". If sample.size is
    set more than 1, uncertainty analysis will be executed while
    the distributions and parameters are supplied in the file "data".
Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205> and Peressini and Sullivan (1988, ISBN:0-387-96614-5).
Build a constrained Delaunay triangulation from simple features
    objects, applying constraints based on input line segments, and triangle
    properties including maximum area, minimum internal angle. The triangulation code
    in 'RTriangle' uses the method of Cheng, Dey and Shewchuk (2012, ISBN:9781584887300). 
    For a low-dependency alternative with low-quality path-based constrained 
    triangulation see <https://CRAN.R-project.org/package=decido> and for high-quality configurable
    triangulation see <https://CRAN.R-project.org/package=anglr>.
An interface to 'spdep' to integrate with 'sf' objects and the 'tidyverse'.
Gives design points from a sequential full factorial-based
 Latin hypercube design, as described in Duan, Ankenman, Sanchez,
 and Sanchez (2015, Technometrics,
 <doi:10.1080/00401706.2015.1108233>).
Converts between R and Simple Feature 'sf' objects, without depending
  on the Simple Feature library. Conversion functions are available at both the R level, 
  and through 'Rcpp'.
Identify and understand clusters of points (typically representing 
    the locations of places or events) stored in simple-features (SF) objects.
    This is useful for analysing, for example, hot-spots of crime events. The 
    package emphasises producing results from point SF data in a single step 
    using reasonable default values for all other arguments, to aid rapid data 
    analysis by users who are starting out. Functions available include kernel
    density estimation (for details, see Yip (2020) 
    <doi:10.22224/gistbok/2020.1.12>), analysis of spatial association (Getis
    and Ord (1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>) and hot-spot
    classification (Chainey (2020) ISBN:158948584X).
The straightforward filtering index (SFINX) identifies true positive
    protein interactions in a fast, user-friendly, and highly accurate way.
    It is not only useful for the filtering of affinity purification -
    mass spectrometry (AP-MS) data, but also for similar types of data
    resulting from other co-complex interactomics technologies, such as TAP-MS,
    Virotrap and BioID. SFINX can also be used via the website interface at
    <http://sfinx.ugent.be>.
Provides a tidy approach to spatial network
    analysis, in the form of classes and functions that enable a seamless
    interaction between the network analysis package 'tidygraph' and the
    spatial analysis package 'sf'.
Provides monthly statistics on the number of monthly air passengers at SFO airport such as operating airline, terminal, geo, etc.
             Data source: San Francisco data portal (DataSF) <https://data.sfgov.org/Transportation/Air-Traffic-Passenger-Statistics/rkru-6vcg>.
An implementation of the Similarity-First Search algorithm (SFS), a combinatorial algorithm which can be used to solve the seriation problem and to recognize some structured weighted graphs. The SFS algorithm represents a generalization to weighted graphs of the graph search algorithm Lexicographic Breadth-First Search (Lex-BFS), a variant of Breadth-First Search. The SFS algorithm reduces to Lex-BFS when applied to binary matrices (or, equivalently, unweighted graphs). Hence this library can be also considered for Lex-BFS applications such as recognition of graph classes like chordal or unit interval graphs. In fact, the SFS seriation algorithm implemented in this package is a multisweep algorithm, which consists in repeating a finite number of SFS iterations (at most n sweeps for a matrix of size n). If the data matrix has a Robinsonian structure, then the ranking returned by the multistep SFS algorithm is a Robinson ordering of the input matrix. Otherwise the algorithm can be used as a heuristic to return a ranking partially satisfying the Robinson property.
Here we provide tools for the estimation of coefficients in 
     penalized regressions when the (co)variance matrix of predictors 
     and the covariance vector between predictors and response, are 
     provided. These methods are extended to the context of a Selection Index
     (commonly used for breeding value prediction). The approaches offer 
     opportunities such as the integration of high-throughput traits in genetic 
     evaluations ('Lopez-Cruz et al., 2020') <doi:10.1038/s41598-020-65011-2> 
     and solutions for training set optimization in Genomic Prediction 
     ('Lopez-Cruz & de los Campos, 2021') <doi:10.1093/genetics/iyab030>.
Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich,
   some of which were ported from S-plus in the 1990s.
 For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe
   plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()',
   pretty arrows, etc.
 For robustness, have a robust F test and robust range().
 For system support, notably on Linux, provides 'Sys.*()' functions with
   more access to system and CPU information.
 Finally, miscellaneous utilities such as simple efficient prime numbers,
   integer codes, Duplicated(), toLatex.numeric() and is.whole().
A series of tools for analyzing Systems Factorial Technology data.  This includes functions for plotting and statistically testing capacity coefficient functions and survivor interaction contrast functions.  Houpt, Blaha, McIntire, Havig, and Townsend (2013) <doi:10.3758/s13428-013-0377-3> provide a basic introduction to Systems Factorial Technology along with examples using the sft R package.
Classes and methods for spatial objects that have a registered time 
    column, in particular for irregular spatiotemporal data. The time column can 
    be of any type, but needs to be ordinal. Regularly laid out spatiotemporal 
    data (vector or raster data cubes) are handled by package 'stars'.
Contains space filling based tools for
    machine learning and data mining. Some functions offer
    several computational techniques and deal with the out of
    memory for large big data by using the ff package.
Modern classes for tracking and movement data, building
    on 'sf' spatial infrastructure, and early theoretical work from
    Turchin (1998, ISBN: 9780878938476), and Calenge et al. (2009)
    <doi:10.1016/j.ecoinf.2008.10.002>. Tracking data are series of
    locations with at least 2-dimensional spatial coordinates (x,y), a
    time index (t), and individual identification (id) of the object
    being monitored; movement data are made of trajectories, i.e. the
    line representation of the path, composed by steps (the
    straight-line segments connecting successive locations). 'sftrack'
    is designed to handle movement of both living organisms and
    inanimate objects.
Once you've identified a real life place, such as a shop, a restaurant, a bar, etc. use this package to simulate a Google search and retrieve its "Popular Times" and geographic location information and save them in Comma-Separated Values files. This package also downloads a list of restaurants and bars of Ushuaia city, Argentina.
Main properties and regression procedures using a generalization of the Dirichlet distribution called Simplicial Generalized Beta distribution. It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation. Graf, M. (2017, ISBN: 978-84-947240-0-8). See also the vignette enclosed in the package.
Implements an extension of the Generalized Berk-Jones (GBJ) statistic for
    survival data, sGBJ. It computes the sGBJ statistic and its p-value for testing 
    the association between a gene set and a time-to-event outcome with possible 
    adjustment on additional covariates. Detailed method is available at Villain L, Ferte T, 
    Thiebaut R and Hejblum BP (2021) <doi:10.1101/2021.09.07.459329>.
A fast and flexible set of tools for large scale estimation. It
    features many stochastic gradient methods, built-in models, visualization
    tools, automated hyperparameter tuning, model checking, interval estimation,
    and convergence diagnostics.
Stagewise techniques implemented with Generalized Estimating Equations to handle individual, group, bi-level, and interaction selection. Stagewise approaches start with an empty model and slowly build the model over several iterations, which yields a 'path' of candidate models from which model selection can be performed. This 'slow brewing' approach gives stagewise techniques a unique flexibility that allows simple incorporation of Generalized Estimating Equations; see Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017) <doi:10.1111/biom.12669> for details.
An Object-oriented Framework for Geostatistical Modeling in S+ 
  containing functions for variogram estimation, variogram fitting and kriging
  as well as some plot functions. Written entirely in S, therefore works only
  for small data sets in acceptable computing time.
Fit a regularized generalized linear model via penalized
        maximum likelihood.  The model is fit for a path of values of
        the penalty parameter. Fits linear, logistic and Cox models.
RCON(V, E) models are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. 'sglasso' package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm.
Set of tools to fit a linear multiple or semi-parametric regression
    models with the possibility of non-informative random right-censoring. 
    Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression
    or semi-parametric functions, whose non-parametric components may be approximated
    by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes
    the generalized extreme value and standard normal distributions as important special cases. Inference is based on penalized likelihood and bootstrap methods.   
    Also, some numerical and graphical devices for diagnostic of the fitted models are offered.
Fast generic solver for sparse group lasso optimization
        problems. The loss (objective) function must be defined in a
        C++ module. The optimization problem is solved using a
        coordinate gradient descent algorithm. Convergence of the
        algorithm is established (see reference) and the algorithm is
        applicable to a broad class of loss functions. Use of parallel
        computing for cross validation and subsampling is supported
        through the 'foreach' and 'doParallel' packages. Development
        version is on GitHub, please report package issues on GitHub.
We provide functions for computing the decision boundaries for pre-licensure vaccine trials using the Generalized Likelihood Ratio tests proposed by Shih, Lai, Heyse and Chen (2010, <doi:10.1002/sim.4036>).
It computes the solutions to a generic stochastic growth model for a given set of user supplied parameters. It includes
    the solutions to the model, plots of the solution, 	
    a summary of the features of the model, a function that covers different types of consumption preferences,
    and a function that computes the moments of a Markov process.	
	Merton, Robert C (1971) <doi:10.1016/0022-0531(71)90038-X>,
	Tauchen, George (1986) <doi:10.1016/0165-1765(86)90168-0>,
	Wickham, Hadley (2009, ISBN:978-0-387-98140-6 ).
An implementation of sparse Gaussian Markov random field mixtures 
  presented by Ide et al. (2016) <doi:10.1109/ICDM.2016.0119>.
  It provides a novel anomaly detection method for multivariate noisy sensor data.
  It can automatically handle multiple operational modes.
  And it can also compute variable-wise anomaly scores.
Methods focused in performing the OSGB36/ETRS89 transformation 
  (Great Britain and the Isle of Man only) by using the Ordnance Survey's 
  OSTN15/OSGM15 transformation model. Calculation of distances and areas from 
  sets of points defined in any of the supported Coordinated Systems is also
  available.
Seven different methods for multiple testing problems. The SGoF-type methods (see for example, Carvajal Rodríguez et al., 2009 <doi:10.1186/1471-2105-10-209>;  de Uña Álvarez, 2012 <doi:10.1515/1544-6115.1812>; Castro Conde et al., 2015 <doi:10.1177/0962280215597580>) and the BH and BY false discovery rate controlling procedures.
Smoothing signals and computing their derivatives is a common
 requirement in signal processing workflows. Savitzky-Golay filters are a
 established method able to do both (Savitzky and Golay, 1964 <doi:10.1021/ac60214a047>).
 This package implements one dimensional Savitzky-Golay filters that can be applied to
 vectors and matrices (either row-wise or column-wise).
 Vectorization and memory allocations have been profiled to reduce computational
 fingerprint. Short filter lengths are implemented in the direct space, while
 longer filters are implemented in frequency space, using a Fast Fourier
 Transform (FFT).
An analytic framework for the calculation of norm- and criterion-referenced academic growth estimates using large scale, longitudinal education assessment data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.
Data sets utilized by the 'SGP' package as exemplars for users to conduct their own student growth percentiles (SGP) analyses.
Regularized version of partial least square approaches providing sparse, group, and sparse group versions of partial least square regression models (Liquet, B., Lafaye de Micheaux, P., Hejblum B., Thiebaut, R. (2016) <doi:10.1093/bioinformatics/btv535>). Version of PLS Discriminant analysis is also provided.
Computation of second-generation p-values as described in Blume et al. (2018) <doi:10.1371/journal.pone.0188299> and Blume et al. (2019) <doi:10.1080/00031305.2018.1537893>. There are additional functions which provide power and type I error calculations, create graphs (particularly suited for large-scale inference usage), and a function to estimate false discovery rates based on second-generation p-value inference.
Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.
Implementation of Sparse-group SLOPE: Adaptive bi-level with FDR-control (Feser et al. (2023) <arXiv:2305.09467>). Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general adaptive three operator splitting (ATOS) implementation is provided.
Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means 
    to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information 
    about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management
    practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data
    distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, 
    enabling data-driven stratifications and sampling approaches.
A set of function that implements for seasonal multivariate time series analysis based on Seasonal Generalized Space
            Time Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by Setiawan(2016)<https://www.researchgate.net/publication/316517889_S-GSTAR-SUR_model_for_seasonal_spatio_temporal_data_forecasting>.
Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation.
Functions for easily manipulating colours, creating colour scales and calculating colour distances.
Functions for calculating: (1) shadow height, (2) logical shadow flag, (3) shadow footprint, (4) Sky View Factor and (5) radiation load. Basic required inputs include a polygonal layer of obstacle outlines along with their heights (i.e. "extruded polygons"), sun azimuth and sun elevation. The package also provides functions for related preliminary calculations: breaking polygons into line segments, determining azimuth of line segments, shifting segments by azimuth and distance, constructing the footprint of a line-of-sight between an observer and the sun, and creating a 3D grid covering the surface area of extruded polygons.
Shadow Document Object Model is a web standard that offers component style and markup encapsulation. 
  It is a critically important piece of the Web Components story as it ensures that a component will work in any environment even if other CSS or JavaScript is at play on the page.
  Custom HTML tags can't be directly identified with selenium tools, because Selenium doesn't provide any way to deal with shadow elements. 
  Using this plugin you can handle any custom HTML tags.
Implement shadowtextGrob() for 'grid' and geom_shadowtext() layer for 'ggplot2'.
             These functions create/draw text grob with background shadow.
Implementations are provided for the models described in the paper D. B. Dahl, R. Day, J. Tsai (2017) <DOI:10.1080/01621459.2016.1165103>. The Ewens, Ewens-Pitman, Ewens attraction, Ewens-Pitman attraction, and ddCRP distributions are available for prior and posterior simulation. Posterior simulation is based on a user-supplied likelihood. Supporting functions for partition estimation and plotting are also provided.
Functions for plotting graphical shapes
  such as ellipses, circles, cylinders, arrows, ...
In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine.
Functions to read and write ESRI shapefiles.
M-estimators of location and shape following the power family 
    (Frahm, Nordhausen, Oja (2020) <doi:10.1016/j.jmva.2019.104569>) are 
    provided in the case of complete data and also when observations have 
    missing values together with functions aiding their visualization.
This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) <doi:10.1111/cag.12222>, (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) <doi:10.1007/s10980-013-9905-x>, (4) counting the number of each first-order pattern element and converting that information into both frequency and empirical probability vectors (Remmel 2020) <doi:10.3390/e22040420>, and (5) computing the porosity of raster patches <doi:10.3390/su10103413>. NOTE: This is a consolidation of existing packages ('PatternClass', 'ShapePattern') to begin warehousing all shape and pattern code in a common package. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented.  Note that all future developments will appear in this package and that 'PatternClass' will eventually be archived.
Studies otolith shape variation among fish populations.
  Otoliths are calcified structures found in the inner ear of teleost fish and their shape has
  been known to vary among several fish populations and stocks, making them very useful in taxonomy,
  species identification and to study geographic variations. The package extends previously described
  software used for otolith shape analysis by allowing the user to automatically extract closed
  contour outlines from a large number of images, perform smoothing to eliminate pixel noise described in Haines and Crampton (2000) <doi:10.1111/1475-4983.00148>,
  choose from conducting either a Fourier or wavelet see Gençay et al (2001) <doi:10.1016/S0378-4371(00)00463-5> transform to the outlines and visualize
  the mean shape. The output of the package are independent Fourier or wavelet coefficients
  which can be directly imported into a wide range of statistical packages in R. The package
  might prove useful in studies of any two dimensional objects.
Here we describe a simple geometric rigid rotation approach that removes the effect of random translation and rotation, enabling the morphological analysis of 3D articulated structures. Our method is based on Cartesian coordinates in 3D space so it can be applied to any morphometric problem that also uses 3D coordinates. See Vidal-García, M., Bandara, L., Keogh, J.S. (2018) <doi:10.1002/ece3.4018>.
Routines for the statistical analysis of landmark 
  shapes, including Procrustes analysis, graphical displays, principal
  components analysis, permutation and bootstrap tests, thin-plate 
  spline transformation grids and comparing covariance matrices. 
     See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, 
     with Applications in R (2nd Edition), John Wiley and Sons.
Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site <https://landsat.gsfc.nasa.gov/>. By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery.
Aid in visual data investigations
 using SHAP (SHapley Additive exPlanation) visualization plots for 'XGBoost' and 'LightGBM'. 
 It provides summary plot, dependence plot, interaction plot, and force plot and relies on
 the SHAP implementation provided by 'XGBoost' and 'LightGBM'.
 Please refer to 'slundberg/shap' for the original implementation of SHAP in 'Python'.
Based on Shapley values to explain multivariate outlyingness and to detect and impute cellwise outliers.
    Includes implementations of methods described in Mayrhofer and Filzmoser (2022) <doi:10.48550/ARXIV.2210.10063>.
Shapley Value Regression for calculating the relative importance of independent variables in linear regression with avoiding the collinearity.
Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <arXiv:1705.07874> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.
Complex machine learning models are often hard to interpret. However, in 
  many situations it is crucial to understand and explain why a model made a specific 
  prediction. Shapley values is the only method for such prediction explanation framework 
  with a solid theoretical foundation. Previously known methods for estimating the Shapley 
  values do, however, assume feature independence. This package implements the method 
  described in Aas, Jullum and Løland (2019) <arXiv:1903.10464>, which accounts for any feature 
  dependence, and thereby produces more accurate estimates of the true Shapley values.
Visualizations for SHAP (SHapley Additive exPlanations), such
    as waterfall plots, force plots, various types of importance plots,
    dependence plots, and interaction plots.  These plots act on a
    'shapviz' object created from a matrix of SHAP values and a
    corresponding feature dataset. Wrappers for the R packages 'xgboost',
    'lightgbm', 'fastshap', 'shapr', 'h2o', 'treeshap', 'DALEX', and
    'kernelshap' are added for convenience.  By separating visualization
    and computation, it is possible to display factor variables in graphs,
    even if the SHAP values are calculated by a model that requires
    numerical features. The plots are inspired by those provided by the
    'shap' package in Python, but there is no dependency on it.
Analyse species-habitat associations in R. Therefore, information about the location 
  of the species (as a point pattern) is needed together with environmental conditions 
  (as a categorical raster). To test for significance habitat associations, one of 
  the two components is randomized. Methods are mainly based on Plotkin et al. (2000) 
  <doi:10.1006/jtbi.2000.2158> and Harms et al. (2001) <doi:10.1111/j.1365-2745.2001.00615.x>.
In stability selection (N Meinshausen, P Bühlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>) and consensus clustering (S Monti et al (2003) <doi:10.1023/A:1023949509487>), resampling techniques are used to enhance the reliability of the results. In this package, hyper-parameters are calibrated by maximising model stability, which is measured under the null hypothesis that all selection (or co-membership) probabilities are identical (B Bodinier et al (2021) <arXiv:2106.02521>). Functions are readily implemented for the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO in stability selection, and hierarchical clustering, partitioning around medoids, K means or Gaussian mixture models in consensus clustering.
Functions and data sets inspired by data sharpening -
             data perturbation to achieve improved performance in
             nonparametric estimation, as described in Choi, E., Hall, P.
             and Rousson, V. (2000).  
             Capabilities for enhanced local linear regression function
             and derivative estimation are included, as well as an
             asymptotically correct iterated data sharpening estimator
             for any degree of local polynomial regression estimation.
             A cross-validation-based bandwidth selector is included which,
             in concert with the iterated sharpener, will often provide
             superior performance, according to a median integrated squared
             error criterion.  Sample data sets are provided to illustrate
             function usage.
A collection of tools for analyzing significance of assets,
    funds, and trading strategies, based on the Sharpe ratio and overfit 
    of the same. Provides density, distribution, quantile and random generation 
    of the Sharpe ratio distribution based on normal returns, as well
    as the optimal Sharpe ratio over multiple assets. Computes confidence intervals
    on the Sharpe and provides a test of equality of Sharpe ratios based on 
    the Delta method. The statistical foundations of the Sharpe can be found in
    the author's Short Sharpe Course  <doi:10.2139/ssrn.3036276>.
An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see <arXiv:1505.01333>).
Functions and data sets for penalized data sharpening.
    Nonparametric regressions are computed subject to smoothness
    and other kinds of penalties.
An algorithm for identifying high-resolution driver elements for datasets from a high-definition reporter assay library. Xinchen Wang, Liang He, Sarah Goggin, Alham Saadat, Li Wang, Melina Claussnitzer, Manolis Kellis (2017) <doi:10.1101/193136>.
Miscellaneous soil data management, summary, visualization, and conversion utilities to support soil survey.
Hierarchical models for the analysis of species-area 
  relationships (SARs) by combining several data sets and covariates; 
  with a global data set combining individual SAR studies; 
  as described in Solymos and Lele 
  (2012, Global Ecology and Biogeography 21, 109-120).
The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Reference: de Mello, R.F. (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" <arXiv:1911.05461>; de Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory".
Provides a computational framework for analyzing mutations in
    immunoglobulin (Ig) sequences. Includes methods for Bayesian estimation of
    antigen-driven selection pressure, mutational load quantification, building of
    somatic hypermutation (SHM) models, and model-dependent distance calculations.
    Also includes empirically derived models of SHM for both mice and humans.
    Citations: 
    Gupta and Vander Heiden, et al (2015) <doi:10.1093/bioinformatics/btv359>, 
    Yaari, et al (2012) <doi:10.1093/nar/gks457>, 
    Yaari, et al (2013) <doi:10.3389/fimmu.2013.00358>, 
    Cui, et al (2016) <doi:10.4049/jimmunol.1502263>.
Uses C++ via the 'Rcpp' package to parse modern Excel files ('.xlsx').
    Memory usage is kept minimal by decompressing only parts of the file at a time,
    while employing multiple threads to achieve significant runtime reduction.
    Uses <https://github.com/richgel999/miniz> and <https://github.com/lemire/fast_double_parser>.
Implements various methods for eliciting a probability
    distribution for a single parameter from an expert or a group of
    experts. The expert provides a small number of probability judgements,
    corresponding to points on his or her cumulative distribution
    function. A range of parametric distributions can then be fitted and
    displayed, with feedback provided in the form of fitted probabilities
    and percentiles. For multiple experts, a weighted linear pool can be
    calculated. Also includes functions for eliciting beliefs about
    population distributions, eliciting multivariate distributions using a
    Gaussian copula, eliciting a Dirichlet distribution, and eliciting
    distributions for variance parameters in a random effects
    meta-analysis model. R Shiny apps for most of the methods are
    included.
Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D)
	through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality
	in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of
	the data (see Judd et al., (2018) <doi:10.1016/j.palaeo.2017.09.034>). This modeling is carried out along a sliding window through the data and yields estimates of
	the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling
	routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of
	each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell
	formation relative to the annual cycle with their uncertainties. All formulae in the package serve this
	purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the
	shell carbonate) through input parameters.
Contains all the formulae of the growth and trace element uptake model described in the equally-named
    Geoscientific Model Development paper (de Winter, 2017, <doi:10.5194/gmd-2017-137>). The model takes as input a file with X- and Y-coordinates of digitized
    growth increments recognized on a longitudinal cross section through the bivalve shell, as well as a BMP file of an elemental map of the
    cross section surface with chemically distinct phases separated by phase analysis. It proceeds by a step-by-step process described in
    the paper, by which digitized growth increments are used to calculate changes in shell height, shell thickness, shell volume, shell mass
    and shell growth rate through the bivalve's life time. Then, results of this growth modelling are combined with the trace element mapping
    results to trace the incorporation of trace elements into the bivalve shell. Results of various modelling parameters can be exported in
    the form of XLSX files.
Powerful graphical displays and statistical tools for structured problem solving and diagnosis. 
    The functions of the 'sherlock' package are especially useful for applying the process of elimination as a problem diagnosis technique.
    The 'sherlock' package was designed to seamlessly work with the 'tidyverse' set of packages and provides a collection of graphical displays 
    built on top of the 'ggplot' and 'plotly' packages, such as different kinds of small multiple plots as well as helper functions such as 
    adding reference lines, normalizing observations, reading in data or saving analysis results in an Excel file.
    References:
    David Hartshorne (2019, ISBN: 978-1-5272-5139-7).
    Stefan H. Steiner, R. Jock MacKay (2005, ISBN: 0873896467).
Compute the frequency distribution of a search term in a series of texts. For example, Arthur Conan Doyle wrote a total of 60 Sherlock Holmes stories, comprised of 54 short stories and 4 longer novels. I wanted to test my own subjective impression that, in many of the stories, Sherlock Holmes' popularity was used as bait to induce the reader to read a story that is essentially not primarily a Sherlock Holmes story. I used the term "Holmes" as a search pattern, since Watson would frequently address him by name, or use his name to describe something that he was doing. My hypothesis is that the frequency distribution of the search pattern "Holmes" is a good proxy for the degree to which a story is or is not truly a Sherlock Holmes story. The results are presented in a manuscript that is available as a vignette and online at <https://barryzee.github.io/Concordance/index.html>.
A template system based on 'AdminLTE3' 
    (<https://adminlte.io/themes/v3/>)
    theme. Comes with default theme that can be easily customized. 
    Developers can upload modified templates on 'Github', and users can 
    easily download templates with 'RStudio' project wizard.
    The key features of the default template include light and dark theme 
    switcher, resizing graphs, synchronizing inputs across sessions, 
    new notification system, fancy progress bars, and card-like flip 
    panels with back sides, as well as various of 'HTML' tool widgets.
An exact method for computing the Poisson-Binomial Distribution (PBD). The package provides a function for generating a random sample from the PBD, as well as two distinct approaches for computing the density, distribution, and quantile functions of the PBD. The first method uses direct-convolution, or a dynamic-programming approach which is numerically stable but can be slow for a large input due to its quadratic complexity. The second method is much faster on large inputs thanks to its use of Fast Fourier Transform (FFT) based convolutions. Notably in this case the package uses an exponential shift to practically guarantee the relative accuracy of the computation of an arbitrarily small tail of the PBD -- something that FFT-based methods often struggle with. This ShiftConvolvePoiBin method is described in Peres, Lee and Keich (2020) <arXiv:2004.07429> where it is also shown to be competitive with the fastest implementations for exactly computing the entire Poisson-Binomial distribution.
Fast enrichment analysis for locally correlated statistics
        via circular permutations.
        The analysis can be performed at multiple significance thresholds
        for both primary and auxiliary data sets with
        efficient correction for multiple testing.
Provides confidence intervals in least-squares regressions when the
  variable of interest has a shift-share structure, and in instrumental
  variables regressions when the instrument has a shift-share structure. The
  confidence intervals implement the AKM and AKM0 methods developed in Adão,
  Kolesár, and Morales (2019) <doi:10.1093/qje/qjz025>.
Prototype your 'shiny' apps quickly with these
    Lorem-Ipsum helper functions. Generate random elements
    for 'shiny' outputs that can be used as placeholder in your application.
Makes it incredibly easy to build interactive web
    applications with R. Automatic "reactive" binding between inputs and
    outputs and extensive prebuilt widgets make it possible to build
    beautiful, responsive, and powerful applications with minimal effort.
Compare performance between different versions of a 'shiny' application based on 'git' references.
Easily use 'Blueprint', the popular 'React' library from Palantir, in your 'Shiny' app.
  'Blueprint' provides a rich set of UI components for creating visually appealing applications
  and is optimized for building complex, data-dense web interfaces.
  This package provides most components from the underlying library,
  as well as special wrappers for some components
  to make it easy to use them in 'R' without writing 'JavaScript' code.
Launch an application by a simple click without opening R or RStudio. The package has 3 functions of which only one is essential in its use, `shiny.exe()`. It generates a script in the open shiny project then create a shortcut in the same folder that allows you to launch the app by clicking.If you set `host = 'public'`, the application will be launched on the public server to which you are connected. Thus, all other devices connected to the same server will be able to access the application through the link of your `IPv4` extended by the port. You can stop the application by leaving the terminal opened by the shortcut.
A rich set of UI components for building Shiny applications,
  including inputs, containers, overlays, menus, and various utilities.
  All components from Fluent UI (the underlying JavaScript library)
  are available and have usage examples in R.
It provides easy internationalization of Shiny
    applications. It can be used as standalone translation package
    to translate reports, interactive visualizations or
    graphical elements as well.
Displays simple diagnostic information of the 'shiny' project in the user interface of the app.
Adds Progressive Web App support for Shiny apps, including
  desktop and mobile installations.
A toolbox for defining React component wrappers which can be used seamlessly in Shiny apps.
RegLog system provides a set of shiny modules to handle register
   procedure for your users, alongside with login, edit credentials and
   password reset functionality. 
   It provides support for popular SQL databases
   and optionally googlesheet-based database for easy setup. For email sending
   it provides support for 'emayili' and 'gmailr' backends. Architecture makes 
   customizing usability pretty straightforward.
   The authentication system created 
   with shiny.reglog is designed to be optional: user don't need to be logged-in 
   to access your application, but when logged-in the user data can be used 
   to read from and write to relational databases.
It is a simple router for your Shiny apps.
    The router allows you to create dynamic web applications with real-time
    User Interface and easily share url to pages within your Shiny apps.
Creating a great user interface for your Shiny apps
    can be a hassle, especially if you want to work purely in R
    and don't want to use, for instance HTML templates. This
    package adds support for a powerful UI library Fomantic UI -
    <https://fomantic-ui.com/> (before Semantic). It also supports
    universal UI input binding that works with various DOM elements.
Allows 'TailwindCSS' to be used in Shiny apps with just-in-time 
  compiling, custom css with '@apply' directive, and custom tailwind 
  configurations.
Enables instrumentation of 'Shiny' apps for tracking user session events
  such as input changes, browser type, and session duration.
  These events can be sent to any of the available storage backends
  and analyzed using the included 'Shiny' app to gain insights about app usage and adoption.
It allows you to delegate heavy computation tasks
    to a separate process, such that it does not freeze your Shiny app.
Ace editor bindings to enable a rich text editing environment
    within Shiny.
Make R data available in Web-based virtual reality experiences
  for immersive, cross-platform data visualizations. Includes the 'gg-aframe'
  JavaScript package for a Grammar of Graphics declarative HTML syntax to create
  3-dimensional data visualizations with 'Mozilla A-Frame' <https://aframe.io>.
Easily create pretty popup messages (modals) in 'Shiny'. A modal can
    contain text, images, OK/Cancel buttons, an input to get a response from the
    user, and many more customizable options.
An extension of 'animate.css' that allows user to easily add animations to any UI element in 'shiny' app using the elements id.
Add in-app user authentication to 'shiny', 
    allowing you to secure publicly hosted apps and 
    build dynamic user interfaces from user information.
A graphical user interface (GUI) for fitting Bayesian regression
    models using the package 'brms' which in turn relies on 'Stan'
    (<https://mc-stan.org/>). The 'shinybrms' GUI is a 'shiny' app.
Sometimes it's useful to know some information about your user in a 'Shiny' app. The available
    information is: browser name (such as 'Chrome' or 'Safari') and version, device type 
    (mobile or desktop), operating system (such as 'Windows' or 'Mac' or 'Android') and version,
    and browser dimensions.
Adds additional Twitter Bootstrap components to Shiny.
Add indicators (spinner, progress bar, gif) in your 'shiny'
  applications to show the user that the server is busy. And other tools to let
  your users know something is happening (send notifications, reports, ...).
Provides a combined slider and numeric input for usage in a 'Shiny' app. The slider and the numeric input are linked together: each one is updated when the other one changes. Many styling properties are customizable (e.g. colors and size).
Makes the 'React' library 'Chakra UI' usable in 'Shiny' apps. 'Chakra UI' components include alert dialogs, drawers (sliding panels), menus, modals, popovers, sliders, and more.
Provides an easy-to-use module for adding a chat to a Shiny app. Allows
    users to send messages and view messages from other users. 
    Messages can be stored in a database or a .rds file.
You can easily add advanced cohort-building component
    to your analytical dashboard or simple 'Shiny' app.
    Then you can instantly start building cohorts using multiple
    filters of different types, filtering datasets, and filtering steps.
    Filters can be complex and data-specific, and together
    with multiple filtering steps you can use complex filtering rules.
    The cohort-building sidebar panel allows you to easily
    work with filters, add and remove filtering steps.
    It helps you with handling missing values during filtering,
    and provides instant filtering feedback with filter feedback plots.
    The GUI panel is not only compatible with native shiny
    bookmarking, but also provides reproducible R code.
When a 'Shiny' output (such as a plot, table, map, etc.) is recalculating, it remains 
    visible but gets greyed out. Using 'shinycssloaders', you can add a loading animation ("spinner")
    to outputs instead. By wrapping a 'Shiny' output in 'withSpinner()', a spinner will automatically
    appear while the output is recalculating. See the demo online at
    <https://daattali.com/shiny/shinycssloaders-demo/>.
A custom css/html or gif/image file for the loading screen in R 'shiny'. It also can use the marquee to have custom text loading screen.
Create Interactive Graph (Network) Visualizations. 
  'shinyCyJS' can be used in 'Shiny' apps or viewed from 'Rstudio' Viewer.
  'shinyCyJS' includes API to build Graph model like node or edge with customized attributes for R. 
  'shinyCyJS' is built with 'cytoscape.js' and 'htmlwidgets' R package.
Create dashboards with 'Shiny'. This package provides
    a theme on top of 'Shiny', making it easy to create attractive dashboards.
Extend 'shinydashboard' with 'AdminLTE2' components. 
             'AdminLTE2' is a free 'Bootstrap 3' dashboard template available
             at <https://adminlte.io>. Customize boxes, add timelines and a lot more.
Provides three types of datetime pickers for usage in a 'Shiny' UI. A datetime picker is an input field for selecting both a date and a time.
A 'Shiny' app can disconnect for a variety of reasons: an unrecoverable error occurred in
    the app, the server went down, the user lost internet connection, or any other reason
    that might cause the 'Shiny' app to lose connection to its server. With 'shinydisconnect', you can
    call disonnectMessage() anywhere in a Shiny app's UI to add a nice message when this happens. 
    Works locally (running Shiny apps within 'RStudio') and on Shiny servers (such as shinyapps.io,
    'RStudio Connect', 'Shiny Server Open Source', 'Shiny Server Pro'). See demo online at 
    <https://daattali.com/shiny/shinydisconnect-demo/>.
Add a download button to a 'shiny' plot or 'plotly' that appears when
  the plot is hovered. A tooltip, styled to resemble 'plotly' buttons, is displayed 
  on hover of the download button. The download button can be used to allow users
  to download the dataset used for a plot.
Add functionality to create drag and drop div elements in shiny.
Shiny module for easily sharing files between users. Admin can add, remove, edit and download file.
    User can only download file. It's also possible to manage files using R functions directly.
Add fancy CSS effects to your 'shinydashboards' or 'shiny' apps.
             100% compatible with 'shinydashboardPlus' and 'bs4Dash'.
Easily display user feedback in Shiny apps.
Provides functionality for client-side navigation of
    the server side file system in shiny apps. In case the app is running
    locally this gives the user direct access to the file system without the
    need to "download" files to a temporary location. Both file and folder
    selection as well as file saving is available.
Allows to connect 'selectizeInputs' widgets as filters to a 'reactable' table. 
  As known from spreadsheet applications, column filters are interdependent, so each 
  filter only shows the values that are really available at the moment based on 
  the current selection in other filters. Filter values currently not available 
  (and also those being available) can be shown via popovers or tooltips.
In 'Shiny' apps, it is sometimes useful to see a plot or a
    table in full screen. Using 'Shinyfullscreen', you can easily designate
    the 'HTML' elements that can be displayed on fullscreen and use buttons to 
    trigger the fullscreen view.
Provides useful UI components and input widgets for 'Shiny'
    applications.  The offered components allow to apply non-standard
    operations and view to your 'Shiny' application, but also help to
    overcome common performance issues.
Insert Glide JavaScript component into Shiny applications for
    carousel or assistant-like user interfaces.
Collection of 'shiny' application styling that are the based on 
  the GOV.UK Design System.  See 
  <https://design-system.service.gov.uk/components/> for details.
Access functionality of the 'heatmaply' package through 'Shiny UI'.
Creates a lightweight way to add markdown helpfiles to 'shiny' apps,
    using modal dialog boxes, with no need to observe each help button separately.
Modifies the progress() function from 'httr' package to let it 
      send output to progressBar() function from 'shinyWidgets' package. 
      It is just a tweak at the original functions from 'httr' package to 
      make it smooth for 'shiny' developers.
A tool to plot data with a large sample size using 'shiny' and 'plotly'.
    Relatively small samples are obtained from the original data using a specific algorithm.
    The samples are updated according to a user-defined x range.
    Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost (2022) <https://github.com/predict-idlab/plotly-resampler>.
Performing Item Response Theory analysis such as parameter estimation, ability estimation, item and model fit analyse, local independence assumption, dimensionality assumption, characteristic and information curves under various models with a user friendly 'shiny' interface.
Package including functions and interactive shiny application
    for the psychometric analysis of educational tests, psychological
    assessments, health-related and other types of multi-item
    measurements, or ratings from multiple raters.
An extension to shiny that brings interactions and animation effects from
    'jQuery UI' library.
Perform common useful JavaScript operations in Shiny apps that will
    greatly improve your apps without having to know any JavaScript. Examples
    include: hiding an element, disabling an input, resetting an input back to
    its original value, delaying code execution by a few seconds, and many more
    useful functions for both the end user and the developer. 'shinyjs' can also
    be used to easily call your own custom JavaScript functions from R.
An interactive Shiny application to perform fast parameter inference on
    dynamical systems (described by ordinary differential equations) using gradient matching.
    Please see the project page for more details.
A collection of highly configurable, touch-enabled knob input controls for 'shiny'. These components can be styled to fit in perfectly in any app, and allow users to set precise values through many input modalities. Users can touch-and-drag, click-and-drag, scroll their mouse wheel, double click, or use keyboard input.
Web front end for your 'R' functions producing plots or tables.
    If you have a function or set of related functions, you can make them
    available over the internet through a web browser. This is the same
    motivation as the 'shiny' package, but note that the development of
    'shinylight' is not in any way linked to that of 'shiny' (beyond the use of
    the 'httpuv' package). You might prefer 'shinylight' to 'shiny' if you want
    a lighter weight deployment with easier horizontal scaling, or if you want
    to develop your front end yourself in JavaScript and HTML just using
    a lightweight remote procedure call interface to your R code on the
    server.
A bridge is created between existing robust open-source record linkage algorithms and an urgently needed user-friendly platform that removes financial and technical barriers, setting a new standard for data interoperability in public health and bioinformatics. The 'fastLink' algorithms are used for matching. Ted Enamorado et al. (2019) <doi:10.1017/S0003055418000783>.
Assesses the number of concurrent users 'shiny'
  applications are capable of supporting, and for directing application changes
  in order to support a higher number of users. Provides facilities for recording
  'shiny' application sessions, playing recorded sessions against a target
  server at load, and analyzing the resulting metrics.
Track and record the use of applications and the user's interactions with 'Shiny' inputs.
  Allows to trace the inputs with which the user interacts, the outputs generated, 
  as well as the errors displayed in the interface.
Provides functions that wrap HTML Bootstrap
    components code to enable the design and layout of informative landing home
    pages for Shiny applications. This can lead to a better user experience for
    the users and writing less HTML for the developer.
Simple and secure authentification mechanism for single 'Shiny' applications.
    Credentials are stored in an encrypted 'SQLite' database. Source code of main application
    is protected until authentication is successful.
Allows shiny developers to incorporate UI elements based on Google's Material design. See <https://material.io/guidelines/> for more information.
A 'Shiny' app allowing to compare and merge two files, with syntax highlighting for several coding languages.
Provides tools for capturing logic in a Shiny app and exposing it as code that can be run outside of Shiny (e.g., from an R console). It also provides tools for bundling both the code and results to the end user.
Implementation of a shiny app to easily compare supervised machine learning model performances. 
    You provide the data and configure each model parameter directly on the shiny app.  
    Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks.
    Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148).
Develop outstanding 'shiny' apps for 'iOS', 'Android', desktop as well as beautiful 'shiny' gadgets.
    'shinyMobile' is built on top of the latest 'Framework7' template <https://framework7.io>.
    Discover 14 new input widgets (sliders, vertical sliders, stepper, 
    grouped action buttons, toggles, picker, smart select, ...), 2 themes (light and dark), 
    12 new widgets (expandable cards, badges, chips, timelines, gauges, progress bars, ...) 
    combined with the power of server-side notifications such as alerts, modals, toasts,
    action sheets, sheets (and more) as well as 3 layouts (single, tabs and split).
Launch a 'shiny' application for 'tidymodels' results. For
    classification or regression models, the app can be used to determine
    if there is lack of fit or poorly predicted points.
Interactive visualization of 'RDML' files via 'shiny' apps. 
  Package provides (1) PCR plate interface with ability to select
  individual tubes and (2) amplification/melting plots with fast hiding and 
  highlighting individual curves.
A 'Shiny' app including the 'Monaco' editor. The 'Monaco' editor is the code editor which powers 'VS Code'. It is particularly well developed for 'JavaScript'. In addition to the 'Monaco' editor features, the app provides prettifiers and minifiers for multiple languages, 'SCSS' and 'TypeScript' compilers, code checking for 'C' and 'C++' (requires 'cppcheck').
The computer program is an efficient igneous norm algorithm and rock classification system written in R but run as shiny app.
An enterprise-targeted scalable and customizable 'shiny' module providing an easy way to incorporate free-form note taking or discussion boards into applications.
    The package includes a 'shiny' module that can be included in any 'shiny' application to create a panel containing searchable, editable text broken down by section headers.
    Can be used with a local 'SQLite' database, or a compatible remote database of choice.
Troubleshooting reactive data in 'shiny' can be difficult. These functions will convert reactive data frames into functions and load all assigned objects into your local environment. If you create a dummy input object, as the function will suggest, you will be able to test your server and ui functions interactively.
Add shiny inputs with one or more inline buttons that grow and shrink with inputs. 
 Also add tool tips to input buttons and styling and messages for input validation.
Create 'Shiny Apps' with collapsible vertical panels. 
    This package provides a new visual arrangement for elements on top of 'Shiny'. 
    Use the expand and collapse capabilities to leverage web applications with
    many elements to focus the user attention on the panel of interest.
Shiny Module to create, visualize, customize and export Excel-like pivot table.
Creates 'shiny' application ('app.R') for making predictions based on lm(), glm(), or coxph() models.
This 'RStudio' addin makes the creation of 'Shiny' and 'ShinyDashboard' apps more efficient.  Besides the necessary folder structure, entire apps can be created using a drag and drop interface and customized with respect to a specific use case. The addin allows the export of the required user interface and server code at any time. By allowing the creation of modules, the addin can be used throughout the entire app development process.
It builds dynamic R shiny based dashboards to analyze any CSV files. It provides simple dashboard design to subset the data, perform exploratory data analysis and preliminary machine learning (supervised and unsupervised). It also provides filters based on columns of interest.
An input controller for R Shiny: a matrix with radio buttons, 
    where only one option per row can be selected.
This gadget allows you to use the 'recipes' package belonging to 'tidymodels' to carry 
    out the data preprocessing tasks in an interactive way. Build your 'recipe' by dragging the variables, 
    visually analyze your data to decide which steps to use, add those steps and preprocess your data.
Allows to create modular, reusable 'HTML'
    forms which can be embedded in your 'shiny' app with minimal effort.
    Features include conditional code execution on form submission,
    automatic input validation and input tooltips.
Shiny wrappers for the RGL package. This package exposes RGL's
    ability to export WebGL visualization in a shiny-friendly format.
Capture screenshots in 'Shiny' applications. Screenshots can either be
    of the entire viewable page, or a specific section of the page. The captured
    image is automatically downloaded as a PNG image, or it can also be saved on
    the server. Powered by the 'html2canvas' JavaScript library.
Add a searchbar widget to your 'Shiny' application. The widget
  quickly integrates with any existing element containing text to highlight
  matches. Highlighting is done with the 'JavaScript' library 'mark.js'. The
  widget includes buttons to cycle through multiple instances of the match and
  automatically scroll to the matches in an overflow element (or window). The
  widget also displays the total number of matches and which match is currently
  being cycled through. The widget is structured as a 'Bootstrap 3' input
  group.
Provides a select control widget for 'Shiny'. It is easily
    customizable, and one can easily use HTML in the items and KaTeX to
    type mathematics.
Create in-app purchasing and subscriptions through 'Servicebot' payment using the 'Stripe' framework.
Provides function shinyShortcut() that, 
    when given the base directory of a shiny application, will produce an
    executable file that runs the shiny app directly in the user's
    default browser. Tested on both windows and unix machines. Inspired
    by and borrowing from 
    <http://www.mango-solutions.com/wp/2017/03/shiny-based-tablet-or-desktop-app/>.
Provides interactive plotting for mathematical models of infectious disease spread. Users can choose from a variety of common built-in ordinary differential equation (ODE) models (such as the SIR, SIRS, and SIS models), or create their own. This latter flexibility allows 'shinySIR' to be applied to simple ODEs from any discipline. The package is a useful teaching tool as students can visualize how changing different parameters can impact model dynamics, with minimal knowledge of coding in R. The built-in models are inspired by those featured in Keeling and Rohani (2008) <doi:10.2307/j.ctvcm4gk0> and Bjornstad (2018) <doi:10.1007/978-3-319-97487-3>.
A graphical user interface for interactive Markov chain Monte
    Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a
    posterior sample. The interface is powered by the 'Shiny' web
    application framework from 'RStudio' and works with the output of MCMC 
    programs written in any programming language (and has extended 
    functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' 
    packages).
Store persistent and synchronized data from 'Shiny' inputs within the browser in a secure format. Refresh 'Shiny' applications and preserve user-inputs over multiple sessions. A database-like storage format is implemented using 'Dexie.js' <https://dexie.org>, a minimal wrapper for 'IndexedDB'. Transfer browser link parameters to 'Shiny' input or output values.
Easily create and deploy surveys in 'Shiny'. This package includes
    a minimalistic framework similar to 'Google Forms' that allows for url-based
    user tracking, customizable submit actions, easy survey-theming, and more.
Sequences sampled at different time points can be used to infer molecular phylogenies on natural time scales, but if the sequences records inaccurate sampling times, that are not the actual sampling times, then it will affect the molecular phylogenetic analysis. This shiny application helps exploring temporal characteristics of the evolutionary trees through linear regression analysis and with the ability to identify and remove incorrect labels.
For automated testing of Shiny applications, using
    a headless browser, driven through 'WebDriver'.
Automated unit testing of Shiny applications through a headless 'Chromium' browser.
It's my experience that working with 'shiny' is intuitive once you're
    into it, but can be quite daunting at first. Several common mistakes are fairly
    predictable, and therefore we can control for these. The functions in this
    package help match up the assets listed in the UI and the SERVER files, and
    Visualize the ad hoc structure of the 'shiny' App.
Themes for use with Shiny. Includes several Bootstrap themes
    from <https://bootswatch.com/>, which are packaged for use with Shiny
    applications.
Provides a time input widget for Shiny. This widget allows intuitive time input in the
    '[hh]:[mm]:[ss]' or '[hh]:[mm]' (24H) format by using a separate numeric input for each time
    component. The interface with R uses date-time objects. See the project page for more
    information and examples.
Enables the ability to change or flash the title of the browser window during a 'shiny' session.
This is a wrapper of the 'React' library 'React-Toastify'. It allows to show some notifications (toasts) in 'Shiny' applications. There are options for the style, the position, the transition effect, and more.
Browser notifications in 'Shiny' apps, using
    'toastr': <https://github.com/CodeSeven/toastr#readme>.
Exposes bindings to jsTree -- a JavaScript library
    that supports interactive trees -- to enable a rich, editable trees in
    Shiny.
Improves the user experience of Shiny apps by helping to
    provide feedback when required inputs are missing, or input values
    are not valid.
Collection of custom input controls and user interface components for 'Shiny' applications. 
  Give your applications a unique and colorful style !
Provides an XY pad input for the 'Shiny' framework. An XY pad is like a bivariate slider. It allows to pick up a pair of numbers.
A collection of functions for data manipulation, plotting and statistical computing,
 to use separately or with the book "Visual Statistics. Use R!":
 Shipunov (2020) <http://ashipunov.info/shipunov/software/r/r-en.htm>.
 Dr Alexey Shipunov died in December 2022.
 Most useful functions:
 Bclust(), Jclust() and BootA() which bootstrap hierarchical clustering;
 Recode() which does multiple recoding in a fast, simple and flexible way;
 Misclass() which outputs confusion matrix even if classes are not concerted;
 Overlap() which measures group separation on any projection;
 Biarrows() which converts any scatterplot into biplot;
 and Pleiad() which is fast and flexible correlogram.
Block-diagonal covariance selection for high dimensional Gaussian
    graphical models. The selection procedure is based on the slope heuristics.
Collect  your data on digital marketing campaigns from 'Shopify' Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
An interface to the Admin API of the E-commerce service Shopify,
    (<https://help.shopify.com/en/api/reference>).
Provides tools for shoreline dating coastal Stone Age sites. The 
  implemented method was developed in Roalkvam (2023) 
  <doi:10.1016/j.quascirev.2022.107880> for the Norwegian Skagerrak coast. 
  Although it can be extended to other areas, this also forms the core area for 
  application of the package. Shoreline dating is based on the present-day 
  elevation of a site, a reconstruction of past relative sea-level change, and
  empirically derived estimates of the likely elevation of the sites above the 
  contemporaneous sea-level when they were in use. The geographical and temporal 
  coverage of the method thus follows from the availability of local geological 
  reconstructions of shoreline displacement and the degree to which the 
  settlements to be dated have been located on or close to the shoreline when 
  they were in use. Methods for numerical treatment and visualisation of the 
  dates are provided, along with basic tools for visualising and evaluating the 
  location of sites.
Integrates clipboard copied data in R Studio, loads and installs libraries within a R script and returns all valid arguments of a selected function.
Performs automatic creation of short forms of scales with an 
    ant colony optimization algorithm and a Tabu search. As implemented in the 
    package, the ant colony algorithm randomly selects items to build a model of 
    a specified length, then updates the probability of item selection according 
    to the fit of the best model within each set of searches. The algorithm 
    continues until the same items are selected by multiple ants a given number 
    of times in a row. On the other hand, the Tabu search changes one parameter at
    a time to be either free, constrained, or fixed while keeping track of the
    changes made and putting changes that result in worse fit in a "tabu" list
    so that the algorithm does not revisit them for some number of searches. 
    See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for
    an applied example of the ant colony algorithm, and Marcoulides & Falk (2018)
    <doi:10.1080/10705511.2017.1409074> for an applied example of the Tabu search.
Create short sprint (<6sec) profiles using the split times or the radar gun data.
    Mono-exponential equation is used to estimate maximal sprinting speed (MSS), relative acceleration (TAU),
    and other parameters such us maximal acceleration (MAC) and maximal relative power (PMAX). These parameters 
    can be used to predict kinematic and kinetics variables and to compare individuals. The modeling method utilized
    in this package is based on the works of Chelly SM, Denis C. (2001) <doi: 10.1097/00005768-200102000-00024>,
    Clark KP, Rieger RH, Bruno RF, Stearne DJ. (2017) <doi: 10.1519/JSC.0000000000002081>, 
    Furusawa K, Hill AV, Parkinson JL (1927) <doi: 10.1098/rspb.1927.0035>, 
    Greene PR. (1986) <doi: 10.1016/0025-5564(86)90063-5>, Samozino P. and Peyrot N., et al (2022)
    <doi: 10.1111/sms.14097>, and Samozino P. (2018) <doi: 10.1007/978-3-319-05633-3_11>.
Analyzes shooting data with respect to group shape,
        precision, and accuracy. This includes graphical methods,
        descriptive statistics, and inference tests using standard,
        but also non-parametric and robust statistical methods.
        Implements distributions for radial error in bivariate normal
        variables. Works with files exported by 'OnTarget PC/TDS',
        'Silver Mountain' e-target, 'ShotMarker' e-target, or 'Taran',
        as well as with custom data files in text format.
        Supports inference from range statistics such as extreme
        spread. Includes a set of web-based graphical user interfaces.
Sometimes it is handy to be able to view an image file on an
    'R' graphics device. This package just does that. Currently it supports
    'PNG' files.
Making it easy to use various types of fonts ('TrueType',
    'OpenType', Type 1, web fonts, etc.) in R graphs, and supporting most output
    formats of R graphics including PNG, PDF and SVG. Text glyphs will be converted
    into polygons or raster images, hence after the plot has been created, it no
    longer relies on the font files. No external software such as 'Ghostscript' is
    needed to use this package.
Providing font files that can be used by the 'showtext' package.
Functions for converting network data from a
        SpatialLinesDataFrame object to an 'igraph'-Class object.
The predictive value of a statistical model can often be improved
    by applying shrinkage methods. This can be achieved, e.g., by regularized
    regression or empirical Bayes approaches. Various types of shrinkage factors can
    also be estimated after a maximum likelihood. While global shrinkage modifies
    all regression coefficients by the same factor, parameterwise shrinkage factors
    differ between regression coefficients. With variables which are either highly
    correlated or associated with regard to contents, such as several columns of a
    design matrix describing a nonlinear effect, parameterwise shrinkage factors are
    not interpretable and a compromise between global and parameterwise shrinkage,
    termed 'joint shrinkage', is a useful extension. A computational shortcut to
    resampling-based shrinkage factor estimation based on DFBETA residuals can be
    applied. Global, parameterwise and joint shrinkage for models fitted by lm(),
    glm(), coxph(), or mfp() is available.
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.
Efficient Markov chain Monte Carlo (MCMC) algorithms for fully 
    Bayesian estimation of dynamic survival models with shrinkage priors. 
    Details on the algorithms used are provided in Wagner (2011) <doi:10.1007/s11222-009-9164-5>, 
    Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and
    Cadonna et al. (2020) <doi:10.3390/econometrics8020020>.
Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and  
  Cadonna et al. (2020) <doi:10.3390/econometrics8020020>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.
Programmatic access to Flipside Crypto data via the Compass RPC API: <https://api-docs.flipsidecrypto.xyz/>. As simple as auto_paginate_query() but with core functions as needed for troubleshooting. Note, 0.1.1 support deprecated 2023-05-31.
We provide a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenario. For the general treatment of statistical hypothesis testing, see the book by Lehmann and Romano (2005) <doi:10.1007/0-387-27605-X>.
Implementation of the shuffle estimator, a non-parametric estimator for signal and noise variance under mild noise correlations.
Scripts and exercises that use card shuffling to teach confidence interval comparisons for different estimators.
Shows the scatter plot along with the fitted regression lines. It 
  depicts min, max, the three quartiles, mean, and sd for each variable.
  It also depicts sd-line, sd-box, r, r-square, prediction boundaries, and regression outliers.
An implementation of four stochastic methods of integrating in R, including:
    1. Stochastic Point Method (or Monte Carlo Method); 
    2. Mean Value Method; 
    3. Important Sampling Method; 
    4. Stratified Sampling Method.
    It can be used to estimate one-dimension or multi-dimension integration by Monte Carlo methods. And the estimated variance (precision) is given.
    Reference: Caflisch, R. E. (1998) <doi:10.1017/S0962492900002804>.
Package including additional modules for interactive
    'ShinyItemAnalysis' application for the psychometric analysis of
    educational tests, psychological assessments, health-related and other
    types of multi-item measurements, or ratings from multiple raters.
Fits bi-variate ellipses to stable isotope data using Bayesian
    inference with the aim being to describe and compare their isotopic
    niche.
Provides models to identify bimodally expressed genes from
 RNAseq data based on the Bimodality Index. SIBERG models the  RNAseq data in
 the finite mixture modeling framework and incorporates mechanisms  for
 dealing with RNAseq normalization. Three types of mixture models are
 implemented,  namely, the mixture of log normal, negative binomial, or 
 generalized Poisson distribution. See Tong et al. (2013)
 <doi:10.1093/bioinformatics/bts713>.
Aims to quantify time intensity data by using sigmoidal and
    double sigmoidal curves. It fits straight lines, sigmoidal, 
    and double sigmoidal curves on to time vs intensity data. 
    Then all the fits are used to make decision on which model 
    best describes the data. This method was first developed 
    in the context of single-cell viral growth analysis (for
    details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>),
    and the package name stands for "SIngle CEll Growth Analysis in R".
Fetch data on targeted public investments from Plataforma +Brasil (SICONV) <http://plataformamaisbrasil.gov.br/>, 
    the responsible system for requests, execution, and monitoring of federal discretionary transfers in Brazil.
Provides function to apply "Subgroup Identification based on Differential Effect Search" (SIDES) method proposed by Lipkovich et al. (2011) <doi:10.1002/sim.4289>.
Evolutionary reconstruction based on substitutions and insertion-deletion (indels) analyses in a distance-based framework.
Allows the user to connect with IBGE's (Instituto Brasileiro de 
    Geografia e Estatistica, see <https://www.ibge.gov.br/> for more information)
    SIDRA API in a flexible way. SIDRA is the acronym to "Sistema IBGE de 
    Recuperacao Automatica" and is the system where IBGE turns available 
    aggregate data from their researches.
We provide functions for estimation and inference of locally-stationary time series using the sieve methods and bootstrapping procedure. In addition, it also contains functions to generate Daubechies and Coiflet wavelet by Cascade algorithm and to process data visualization.
Allows to retrieve time series of all indicators available in the Bank of Mexico's Economic Information System (<http://www.banxico.org.mx/SieInternet/>).
Methods for regression with high-dimensional predictors and  univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.
Performs multivariate nonparametric regression/classification by the method of sieves (or using orthogonal series). The method is suitable for continuous/binary problems with multivariate or moderate high-dimensional features (dimension < 100). The main estimator in this package, penalized sieve estimator, is adaptive to the feature dimension with provable theoretical guarantees. Moreover, such a method is computationally tractable in the sense it typically has a polynomial dependence (rather than an exponential one) on the feature dimension and an almost linear dependence on the sample size. Details of the methods and model assumptions can be found in: Tianyu Zhang, and Noah Simon (2022) <arXiv:2206.02994>.
Implements semiparametric estimation and testing procedures for a continuous, possibly multivariate, mark-specific hazard ratio (treatment/placebo) of an event of interest in a randomized treatment efficacy trial with a time-to-event endpoint, as described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328 337 <doi:10.1111/biom.12016>, and in Juraska M and Gilbert PB (2016), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4): 606-25 <doi:10.1007/s10985-015-9353-9>. The former considers continuous multivariate marks fully observed in all subjects who experience the event of interest, whereas the latter extends the previous work to allow multivariate marks that are subject to missingness-at-random. For models with missing marks, two estimators are implemented based on (i) inverse probability weighting (IPW) of complete cases, and (ii) augmentation of the IPW estimating functions by leveraging correlations between the mark and auxiliary data to 'impute' the expected profile score vectors for subjects with missing marks. The augmented IPW estimator is doubly robust and recommended for use with incomplete mark data. The methods make two key assumptions: (i) the time-to-event is assumed to be conditionally independent of the mark given treatment, and (ii) the weight function in the semiparametric density ratio/biased sampling model is assumed to be exponential. Diagnostic testing procedures for evaluating validity of both assumptions are implemented. Summary and plotting functions are provided for estimation and inferential results.
Functions for making particle-size analysis. Sieve tests are widely used to obtain particle-size distribution of powders or granular materials.
Functions for estimating catalytic constant and Michaelis-Menten constant (MM constant) of 
             stochastic Michaelis-Menten enzyme kinetics model are provided. The likelihood functions 
             based on stochastic simulation approximation (SSA), diffusion approximation (DA), and 
             Gaussian processes (GP) are provided to construct posterior functions for the Bayesian 
             estimation. All functions utilize Markov Chain Monte Carlo (MCMC) methods with Metropolis-
             Hastings algorithm with random walk chain and robust adaptive Metropolis-Hastings 
             algorithm based on Bayesian framework.
Facilitate extraction of key information from common datasets.
Analysts who change projects frequently know that it can be hard
    to find the right column in an unfamiliar dataframe, especially when the
    dataframe spans hundreds of columns and millions of rows. 'siftr' is an
    interactive tool that finds relevant columns by fuzzily searching through
    a dataframe's column names, labels, factor levels, and unique values.
Print function signatures and find overly complicated code.
SigClust is a statistical method for testing the
        significance of clustering results. SigClust can be applied to
        assess the statistical significance of splitting a data set
        into two clusters. For more than two clusters, SigClust can be
        used iteratively.
Uses logistic regression to model the probability of detection as a function of covariates. 
             This model is then used with observational survey data to estimate population size, while
             accounting for uncertain detection.  See Steinhorst and Samuel (1989).
Provides pseudo-likelihood methods for empirically analyzing common signaling games in international relations as described in Crisman-Cox and Gibilisco (2019) <doi:10.1017/psrm.2019.58>.
Interface to 'sigma.js' graph visualization library including animations, plugins and shiny proxies.
Genomic alterations including single nucleotide substitution,
    copy number alteration, etc. are the major force for cancer
    initialization and development. Due to the specificity of molecular
    lesions caused by genomic alterations, we can generate characteristic
    alteration spectra, called 'signature' (Wang, Shixiang, et al. (2021)
    <DOI:10.1371/journal.pgen.1009557> & Alexandrov, Ludmil B., et al.
    (2020) <DOI:10.1038/s41586-020-1943-3> & Steele Christopher D., et al.
    (2022) <DOI:10.1038/s41586-022-04738-6>).  This package helps users to
    extract, analyze and visualize signatures from genomic alteration
    records, thus providing new insight into cancer study.
Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.
Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways.
A framework for the analysis and exploration of single-cell chromatin data.
    The 'Signac' package contains functions for quantifying single-cell chromatin data,
    computing per-cell quality control metrics, dimension reduction
    and normalization, visualization, and DNA sequence motif analysis.
    Reference: Stuart et al. (2021) <doi:10.1038/s41592-021-01282-5>.
An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.
A set of signal processing functions originally written for 'Matlab' and 'Octave'.
  Includes filter generation utilities, filtering functions,
  resampling routines, and visualization of filter models. It also
  includes interpolation functions.
Predicts the presence of signal peptides in eukaryotic protein
    using hidden semi-Markov models. The implemented algorithm can be accessed from
    both the command line and GUI.
Extract the signed backbones of intrinsically dense weighted networks based on the significance filter and vigor filter as described in the following paper. Please cite it if you find this software useful in your work.	
	Furkan Gursoy and Bertan Badur. "Extracting the signed backbone of intrinsically dense weighted networks." Journal of Complex Networks. <arXiv:2012.05216>.
Add significance marks to any R Boxplot, including a given significance niveau.
Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.
Methods for the analysis of signed networks. This includes
    several measures for structural balance as introduced by Cartwright
    and Harary (1956) <doi:10.1037/h0046049>, blockmodeling algorithms
    from Doreian (2008) <doi:10.1016/j.socnet.2008.03.005>, various
    centrality indices, and projections of signed two-mode networks
    introduced by Schoch (2020) <doi:10.1080/0022250X.2019.1711376>.
Provides convenience functions to replace hyphen-minuses (ASCII 45)
    with proper minus signs (Unicode character 2212). The true minus matches
    the plus symbol in width, line thickness, and height above the baseline.
    It was designed for mathematics, looks better in presentation,
    and is understood properly by screen readers.
Interfaces with the 'SigOpt' API. More info at <https://sigopt.com>.
Pathway Analysis is statistically linking observations on 
     the molecular level to biological processes or pathways on 
     the systems(i.e., organism, organ, tissue, cell) level.  
     Traditionally, pathway analysis methods regard pathways 
     as collections of single genes and treat all genes in a pathway
     as equally informative. However, this can lead to identifying
     spurious pathways as statistically significant since components
     are often shared amongst pathways. SIGORA seeks to avoid this
     pitfall by focusing on genes or gene pairs that are (as a combination)
     specific to a single pathway.
     In relying on such pathway gene-pair signatures (Pathway-GPS),
     SIGORA inherently uses the status of other genes in the
     experimental context to identify the most relevant pathways.
     The current version allows for pathway analysis of human
     and mouse datasets. In addition, it contains pre-computed
     Pathway-GPS data for pathways in the KEGG and  Reactome
     pathway repositories and mechanisms for extracting GPS
     for user-supplied repositories.
Provides gene signature quality control metrics in publication ready plots. Namely, enables the visualization of properties such as expression, variability, correlation, and comparison of methods of standardisation and scoring metrics.
Succinctly and correctly format statistical summaries of
    various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance). 
    This package also includes empirical tests, such as Monte Carlo and bootstrap distribution estimates.
Provides tools to identify and visualize branches in a phylogenetic tree that are significantly responsive to some intervention, taking as primary inputs a phylogenetic tree (of class phylo) and a data frame (or matrix) of corresponding tip (OTU) labels and p-values.
The goal of SIHR is to provide inference procedures in the high-dimensional setting for 
    (1) linear functionals in generalized linear regression ('Cai et al.' (2019) <arXiv:1904.12891>, 'Guo et al.' (2020) <arXiv:2012.07133>, 'Cai et al.' (2021)),
    (2) conditional average treatment effects in generalized linear regression,
    (3) quadratic functionals in generalized linear regression ('Guo et al.' (2019) <arXiv:1909.01503>).
    (4) inner product in generalized linear regression
    (5) distance in generalized linear regression.
Calculate superior identification index and its extensions.
    Measure the performance of journals based on how well they could 
    identify the top papers by any index (e.g. citation indices) according to Huang & Yang. 
    (2022) <doi:10.1007/s11192-022-04372-z>. These methods could be extended to 
    evaluate other entities such as institutes, countries, etc.
Method to estimate the spatial influence scales of landscape variables on a response variable. The method is based on Chandler and Hepinstall-Cymerman (2016) Estimating the spatial scales of landscape effects on abundance, Landscape ecology, 31: 1383-1394, <doi:10.1007/s10980-016-0380-z>.
Provides a general framework to perform statistical inference of each gene pair 
        and global inference of whole-scale gene pairs in gene networks using the well known 
        Gaussian graphical model (GGM) in a time-efficient manner. We focus on the high-dimensional 
        settings where p (the number of genes) is allowed to be far larger than n (the number of subjects). 
        Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso 
        (Ren et al (2015) <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled Lasso 
        (Jankova and van de Geer (2017) <doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified 
        graphical Lasso (Jankova and van de Geer (2015) <doi:10.1214/15-EJS1031>) (4) the GGM 
        estimation with false discovery rate control (FDR) using scaled Lasso or Lasso 
        (Liu (2013) <doi:10.1214/13-AOS1169>). Windows users should install 'Rtools' before the 
        installation of this package.
Generate common data forms for complex data suitable for conversions and
 transmission by decomposition as paths or primitives. Paths are sequentially-linked records, 
 primitives are basic atomic elements and both can model many forms and be grouped into hierarchical 
 structures.  The universal models 'SC0' (structural) and 'SC' (labelled, relational) are composed of 
 edges and can represent any hierarchical form. Specialist models 'PATH', 'ARC' and 'TRI' provide the 
 most common intermediate forms used for converting from one form to another. The methods are
 inspired by the simplicial complex <https://en.wikipedia.org/wiki/Simplicial_complex> and 
 provide intermediate forms that relate spatial data structures to this mathematical construct.
Simultaneous inference procedures for high-dimensional linear models as described by Zhang, X., and Cheng, G. (2017) <doi:10.1080/01621459.2016.1166114>.
It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.
The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of 'Sim.PLFN' package are:  (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that 'Sim.PLFN' package works on the basis of 'FuzzyNumbers' package.
Generates realistic simulated genetic data in families or unrelated individuals.
Provides tools for simulating spatially dependent predictors (continuous or binary),
    which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous
    predictors are generated using traditional multivariate normal distributions or Gauss Markov random
    fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288>
    and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using
    a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors 
	exhibiting spatial clustering can also be easily specified by the user.
Monitoring of Adverse Event (AE) reporting in clinical trials is 
  important for patient safety. Sites that are under-reporting AEs can be detected 
  using Bootstrap-based simulations that simulate overall AE reporting. Based on the 
  simulation an AE under-reporting probability is assigned to each site in a 
  given trial (Koneswarakantha 2021 <doi:10.1007/s40264-020-01011-5>).
Provides some code to run simulations of state-space models, and then
             use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) 
             algorithm of Toni et al. (2009) <doi:10.1098/rsif.2008.0172> and a bootstrap particle
             filter based particle Markov chain Monte Carlo (PMCMC) algorithm 
             (Andrieu et al., 2010 <doi:10.1111/j.1467-9868.2009.00736.x>). 
             Also provides functions to plot and summarise the outputs.
Provides estimation of simultaneous bootstrap and asymptotic confidence intervals for diversity indices, namely the Shannon and the Simpson index. Several pre--specified multiple comparison types are available to choose. Further user--defined contrast matrices are applicable. In addition, simboot estimates adjusted as well as unadjusted p--values for two of the three proposed bootstrap methods. Further simboot allows for comparing biological diversities of two or more groups while simultaneously testing a user-defined selection of Hill numbers of orders q, which are considered as appropriate and useful indices for measuring diversity.
A flexible tool for simulating complex longitudinal data using
    structural equations, with emphasis on problems in causal inference.
    Specify interventions and simulate from intervened data generating
    distributions. Define and evaluate treatment-specific means, the average
    treatment effects and coefficients from working marginal structural models.
    User interface designed to facilitate the conduct of transparent and
    reproducible simulation studies, and allows concise expression of complex
    functional dependencies for a large number of time-varying nodes. See the
    package vignette for more information, documentation and examples.
Provides efficient R and 'C++' routines to simulate cognitive diagnostic
    model data for Deterministic Input, Noisy "And" Gate ('DINA') and
    reduced Reparameterized Unified Model ('rRUM') from 
    Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>,
    Culpepper (2015) <doi:10.3102/1076998615595403>, and
    de la Torre (2009) <doi:10.3102/1076998607309474>.
Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 <doi:10.2202/1557-4679.1258>) or ratios (Hasler and Hothorn, 2012 <doi:10.1080/19466315.2011.633868>) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 <doi:10.1002/bimj.200710466>) or covariance matrices (Hasler, 2014 <doi:10.1515/ijb-2012-0015>).
Provides a framework to generating random variates from
             arbitrary multivariate copulae, while concentrating on
             (bivariate) extreme value copulae.  Particularly useful if
             the multivariate copulae are not available in closed
             form.
Simulates correlated multinomial responses conditional on a marginal model specification.
Simulate and plot general experimental crosses. The focus is on simulating genotypes with an aim towards flexibility rather than speed. Meiosis is simulated following the Stahl model, in which chiasma locations are the superposition of two processes: a proportion p coming from a process exhibiting no interference, and the remainder coming from a process following the chi-square model.
Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.
Simulation methods for the Fisher Bingham distribution on the unit sphere, the matrix Bingham distribution on a Grassmann manifold, the matrix Fisher distribution on SO(3), and the bivariate von Mises sine model on the torus.
 The methods use the first ever general purpose acceptance/rejection simulation algorithm for the Bingham distribution and are described fully by Kent, Ganeiber and Mardia (2018) <doi:10.1080/10618600.2017.1390468>.
 These methods superseded earlier MCMC simulation methods and are more general than earlier simulation methods.
 The methods can be slower in specific situations where there are existing non-MCMC simulation methods (see Section 8 of Kent, Ganeiber and Mardia (2018) <doi:10.1080/10618600.2017.1390468> for further details).
Provides tools to safely and efficiently organize and execute 
    Monte Carlo simulation experiments in R.
    The package controls the structure and back-end of Monte Carlo simulation experiments
    by utilizing a generate-analyse-summarise workflow. The workflow safeguards against 
    common simulation coding issues, such as automatically re-simulating non-convergent results, 
    prevents inadvertently overwriting simulation files, catches error and warning messages
    during execution, and implicitly supports parallel processing.
    For a pedagogical introduction to the package see
    Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of 
    the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.
Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) <doi:10.1002/sim.7689> for details.
Assessment of the distributions of baseline continuous and categorical 
    variables in randomised trials. This method is based on the Carlisle-Stouffer 
    method with Monte Carlo simulations. It calculates p-values for each trial 
    baseline variable, as well as combined p-values for each trial - these p-values 
    measure how compatible are distributions of trials baseline variables with 
    random sampling. This package also allows for graphically plotting the 
    cumulative frequencies of computed p-values.
    Please note that code was partly adapted from Carlisle JB, Loadsman JA. 
    (2017) <doi:10.1111/anae.13650>.
Mixed DNA profiles can be sampled according to models for probabilistic genotyping. Peak height variability is modelled using a log normal distribution or a gamma distribution. Sample contributors may be related according to a pedigree.
An object oriented framework to simulate
  ecological (and other) dynamic systems. It can be used for
  differential equations, individual-based (or agent-based) and other
  models as well. It supports structuring of simulation scenarios (to avoid copy
  and paste) and aims to improve readability and re-usability of code.
Contains various functions to be used for simulation education,
    including simple Monte Carlo simulation functions, queueing simulation
    functions, variate generation functions capable of producing independent
    streams and antithetic variates, functions for illustrating random variate
    generation for various discrete and continuous distributions, and functions
    to compute time-persistent statistics.  Also contains functions for 
    visualizing: event-driven details of a single-server queue model; a Lehmer
    random number generator; variate generation via acceptance-rejection; and
    of generating a non-homogeneous Poisson process via thinning.  Also
    contains two queueing data sets (one fabricated, one real-world) to
    facilitate input modeling.
An open-source R package for structuring, maintaining, running, and debugging statistical simulations on both local and cluster-based computing environments.See full documentation at <https://avi-kenny.github.io/SimEngine/>.
Data simulator including genotype, phenotype, pedigree, 
    selection and reproduction in R. It simulates most of reproduction process
    of animals or plants and provides data for GS (Genomic Selection),
    GWAS (Genome-Wide Association Study), and Breeding.
    For ADI model, please see Kao C and Zeng Z (2002) <doi:10.1093/genetics/160.3.1243>.
    For build.cov, please see B. D. Ripley (1987) <ISBN:9780470009604>.
Estimation of function and index vector in single index model with and without shape constraints including different smoothness conditions.
Supports the calculation of meteorological characteristics in 
      evapotranspiration research and reference crop evapotranspiration, and offers 
      three models to simulate crop evapotranspiration and soil water balance in 
      the field, including single crop coefficient and dual crop coefficient, 
      as well as the Shuttleworth-Wallace model. These calculations main refer to 
      Allen et al.(1998, ISBN:92-5-104219-5), Teh (2006, ISBN:1-58-112-998-X), and 
      Liu et al.(2006) <doi:10.1016/j.agwat.2006.01.018>.
Monte Carlo simulations of a game-theoretic model for the 
	legal exemption system of the European cartel law are implemented
	in order to estimate the (mean) deterrent effect of this system.
	The input and output parameters of the simulated cartel 
	opportunities can be visualized by three-dimensional projections.
	A description of the model is given in Moritz et al. (2018)
	<doi:10.1515/bejeap-2017-0235>.
Implementation of the SIMEX-Algorithm by Cook & Stefanski (1994) <doi:10.1080/01621459.1994.10476871> and
    MCSIMEX by Küchenhoff, Mwalili & Lesaffre (2006) <doi:10.1111/j.1541-0420.2005.00396.x>.
Implement of the Simulation-Extrapolation (SIMEX) algorithm for the accelerated failure time (AFT) with covariates subject to measurement error.
Generates binary test data based on Item Response Theory using the two-parameter logistic model (Lord, 1980 <doi:10.4324/9780203056615>). Useful functions for test equating are included, e.g. functions for generating internal and external common items between test forms and a function to create a linkage plans between those forms. Ancillary functions for generating true item and person parameters as well as for calculating the probability of a person correctly answering an item are also included.
Implementation of the boosting procedure with the simulation and extrapolation approach to address variable selection and estimation for (ultra)high-dimensional data subject to measurement error in predictors. Some relevant references include Chen, L.-P. and Yi, G. Y. (2021) <doi:10.1111/biom.13331>, Hastie, T., Tibshirani, R. and Friedman, J. (2008, ISBN:978-0387848570), and Yi, G. Y. (2017, ISBN:978-1493966387).
The focus is on simulating and modeling families with founders drawn from a structured population (for example, with different ancestries or other potentially non-family relatedness), in contrast to traditional pedigree analysis that treats all founders as equally unrelated.  Main function simulates a random pedigree for many generations, avoiding close relatives, pairing closest individuals according to a 1D geography and their randomly-drawn sex, and with variable children sizes to result in a target population size per generation.  Auxiliary functions calculate kinship matrices, admixture matrices, and draw random genotypes across arbitrary pedigree structures starting from the corresponding founder values.  The code is built around the plink FAM table format for pedigrees.  Described in Yao and Ochoa (2022) <doi:10.1101/2022.03.25.485885>.
Through simfinapi, you can intuitively access the 'SimFin'
    Web-API (<https://www.simfin.com/>) to make 'SimFin' data easily
    available in R. To obtain an 'SimFin' API key (and thus to use this
    package), you need to register at <https://app.simfin.com/login>.
Simulates data from model objects (e.g., from lm(), glm()),
    and plots this along with the original data to compare how well the
    simulated data matches the original data to determine model fit.
A general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort.  The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results.  It follows a clear object-oriented design and supports parallel computing to increase computational performance.
Simulates regression models,
    including both simple regression and generalized linear mixed
    models with up to three level of nesting. Power simulations that are
    flexible allowing the specification of missing data, unbalanced designs,
    and different random error distributions are built into the package.
Routine that allows the user to run several goodness-of-fit tests.  
    It also combines the tests and returns a properly adjusted family-wise p value.
    Details can be found in <arXiv:2007.04727>.
Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.
Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies. Our derivation and explanation of formulas and our general simulation workflow is closely aligned with the approach described by Morris, White, and Crowther (2019) <DOI:10.1002/sim.8086>.
Contains tests for association between a set of genetic variants and multiple correlated outcomes that are interval censored. Interval-censored data arises when the exact time of the onset of an outcome of interest is unknown but known to fall between two time points.
Based on the illness-death model a large number of clinical
    trials with oncology endpoints progression-free survival (PFS) and
    overall survival (OS) can be simulated, see Meller, Beyersmann and
    Rufibach (2019) <doi:10.1002/sim.8295>.  The simulation set-up allows
    for random and event-driven censoring, an arbitrary number of
    treatment arms, staggered study entry and drop-out.  Exponentially,
    Weibull and piecewise exponentially distributed survival times can be
    generated.
An implementation of a novel method to quantify the similarity
    of the code-base of R functions by means of program dependence graphs.
    Possible use cases include detection of code clones for improving
    software quality and of plagiarism amongst students' assignments.
Functions to run and assist four
  different similarity measures. The similarity
  measures included are: longest common
  subsequence (LCSS), Frechet distance, edit distance
  and dynamic time warping (DTW). Each of these
  similarity measures can be calculated from two
  n-dimensional trajectories, both in matrix form.
Allows a Simile model saved as a compiled binary to be
  loaded, parameterized, executed and interrogated. This version works 
  with Simile v5.97 on.
Provides an efficient and very flexible framework to
    conduct data-driven epidemiological modeling in realistic large
    scale disease spread simulations. The framework integrates
    infection dynamics in subpopulations as continuous-time Markov
    chains using the Gillespie stochastic simulation algorithm and
    incorporates available data such as births, deaths and movements
    as scheduled events at predefined time-points. Using C code for
    the numerical solvers and 'OpenMP' (if available) to divide work
    over multiple processors ensures high performance when simulating
    a sample outcome. One of our design goals was to make the package
    extendable and enable usage of the numerical solvers from other R
    extension packages in order to facilitate complex epidemiological
    research. The package contains template models and can be extended
    with user-defined models. For more details see the paper by
    Widgren, Bauer, Eriksson and Engblom (2019)
    <doi:10.18637/jss.v091.i12>. The package also provides
    functionality to fit models to time series data using the
    Approximate Bayesian Computation Sequential Monte Carlo
    ('ABC-SMC') algorithm of Toni and others (2009)
    <doi:10.1098/rsif.2008.0172>.
Provides tools for the stochastic simulation of effectiveness scores to mitigate
  data-related limitations of Information Retrieval evaluation research, as described in Urbano and
  Nagler (2018) <doi:10.1145/3209978.3210043>. These tools include: fitting, selection and plotting
  distributions to model system effectiveness, transformation towards a prespecified expected value,
  proxy to fitting of copula models based on these distributions, and simulation of new evaluation
  data from these distributions and copula models.
Uses simulation to create prediction intervals for
    post-policy outcomes in interrupted time series (ITS) designs,
    following Miratrix (2020) <arXiv:2002.05746>. This package provides
    methods for fitting ITS models with lagged outcomes and variables to
    account for temporal dependencies.  It then conducts inference via
    simulation, simulating a set of plausible counterfactual post-policy
    series to compare to the observed post-policy series. This package
    also provides methods to visualize such data, and also to incorporate
    seasonality models and smoothing and aggregation/summarization.  This
    work partially funded by Arnold Ventures in collaboration with
    MDRC.
Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach <doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008) <doi:10.1080/00273170802285693>. Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator from <https://www.pcg-random.org>.
A toolbox for constructing potential landscapes for dynamical
    systems using Monte Carlo simulation.  The method is based on the
    potential landscape definition by Wang et al. (2008)
    <doi:10.1073/pnas.0800579105> (also see Zhou & Li, 2016
    <doi:10.1063/1.4943096> for further mathematical discussions) and can
    be used for a large variety of models.
A process-oriented and trajectory-based Discrete-Event Simulation
    (DES) package for R. It is designed as a generic yet powerful framework. The
    architecture encloses a robust and fast simulation core written in 'C++' with
    automatic monitoring capabilities. It provides a rich and flexible R API that
    revolves around the concept of trajectory, a common path in the simulation
    model for entities of the same type. Documentation about 'simmer' is provided
    by several vignettes included in this package, via the paper by Ucar, Smeets
    & Azcorra (2019, <doi:10.18637/jss.v090.i02>), and the paper by Ucar,
    Hernández, Serrano & Azcorra (2018, <doi:10.1109/MCOM.2018.1700960>);
    see 'citation("simmer")' for details.
Provides wrappers for common activity patterns in 'simmer' trajectories.
A set of plotting methods for 'simmer' trajectories and simulations.
Allows users to quickly apply individual or multiple metrics to evaluate Monte Carlo simulation studies.
A major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients' data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> and Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml().
Simulates somatic single base substitutions carried in cancer genomes. By only providing a human reference genome, substitutions that result from mutational processes operative in every cancer genome can be generated.
Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load() and simmr_mcmc(). The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010 <doi:10.1371/journal.pone.0009672>, and Parnell et al 2013 <doi:10.1002/env.2221>.
Algorithms to create prognostic biomarkers using biological genesets or networks.
Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.
Implements statistical inference for systems of ordinary differential equations,
  that uses the integral-matching criterion and takes advantage of the separability of parameters,
  in order to obtain initial parameter estimates for nonlinear least squares optimization. 
  Dattner & Yaari (2018) <arXiv:1807.04202>. 
  Dattner et al. (2017) <doi:10.1098/rsif.2016.0525>.
  Dattner & Klaassen (2015) <doi:10.1214/15-EJS1053>.
Develop spatial interaction models (SIMs).  SIMs predict the
    amount of interaction, for example number of trips per day, between
    geographic entities representing trip origins and destinations.
    Contains functions for creating origin-destination datasets
    from geographic input datasets and calculating movement between
    origin-destination pairs with constrained, production-constrained,
    and attraction-constrained models (Wilson 1979) <doi:10.1068/a030001>.
Simulate data from a Gaussian graphical model or a Gaussian Bayesian network in two conditions. Given a covariance matrix of a reference condition simulate plausible disregulations. See Salviato et al. (2017) <doi:10.1093/bioinformatics/btw642>.
Simulates and plots quantities of interest (relative
    hazards, first differences, and hazard ratios) for linear coefficients,
    multiplicative interactions, polynomials, penalised splines, and
    non-proportional hazards, as well as stratified survival curves from Cox
    Proportional Hazard models. It also simulates and plots marginal effects
    for multiplicative interactions. Methods described in Gandrud (2015)
    <doi:10.18637/jss.v065.i03>.
A tool for simulating rhythmic data: transcriptome data using
  Gaussian or negative binomial distributions, and behavioral activity data
  using Bernoulli or Poisson distributions. See Singer et al. (2019)
  <doi:10.7717/peerj.6985>.
Interface to interact with the modelling framework 'SIMPLACE' and to
    parse the results of simulations.
A simple function that anonymises a list of variables
    in a consistent way: anonymised factors are not recycled and
    the same original levels receive the same anonymised factor
    even if located in different datasets.
Provides SPSS- and SAS-like output for least squares multiple 
    regression and moderated regression, as well as interaction plots and 
    Johnson-Neyman regions of significance for interactions. The output 
    includes standardized coefficients, partial and semi-partial correlations, 
    collinearity diagnostics, plots of residuals, and detailed information 
    about simple slopes for interactions. There are numerous options for 
    designing interaction plots, including plots of interactions for both lm
    and lme models.
Provides intuitive functions for caching R objects, encouraging
    reproducible, restartable, and distributed R analysis. The user selects a
    location to store caches, and then provides  nothing more than a cache name
    and instructions (R code) for how to produce the R object. Also
    provides some advanced options like environment assignments, recreating or
    reloading caches, and cluster compute bindings (using the 'batchtools'
    package) making it flexible enough for use in large-scale data analysis
    projects.
A curated set of colors that are called using
    a standardized syntax: saturation + hue + lightness. For example, 
    "brightblue4" and "mutedred2". Functions exists to return individual colors 
    by name or to build palettes across or within hues. Most functions allow you 
    to visualize the palettes in addition to returning the desired hex codes.
Using the adjustment method from Benjamini & Hochberg (1995) <doi:10.1111/j.2517-6161.1995.tb02031.x>, this package determines which variables are significant under repeated testing with a given dataframe of p values and an user defined "q" threshold.  It then returns the original dataframe along with a significance column where an asterisk denotes a significant p value after FDR calculation, and NA denotes all other p values. This package uses the Benjamini & Hochberg method specifically as described in Lee, S., & Lee, D. K. (2018) <doi:10.4097/kja.d.18.00242>.
Simple classic graph algorithms for simple graph classes.
    Graphs may possess vertex and edge attributes. 'simplegraph' has
    so dependencies and it is written entirely in R, so it is easy to
    install.
This is a graph database in 'SQLite'.  It is inspired by Denis Papathanasiou's Python simple-graph project on 'GitHub'.
A very bare-bones interface to use the Metropolis-Hastings Monte 
    Carlo Markov Chain algorithm. It is suitable for teaching and testing 
    purposes.
Create and train a multilayer perceptron, a type of feedforward, 
    fully connected neural network. Features 2 ReLU hidden layers. Learn more 
    about about the activation functions and backpropagation used by this 
    network in Goodfellow et al. (2016, ISBN: 9780262035613) "Deep Learning".
Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
The number of studies involving correlated traits and the availability of tools to handle this type of data has increased considerably in the last decade. With such a demand, we need tools for testing hypotheses related to single and multi-trait (correlated) phenotypes based on many genetic settings. Thus, we implemented various options for simulation of pleiotropy and Linkage Disequilibrium under additive, dominance and epistatic models. The simulation currently takes a marker data set as an input and then uses it for simulating multiple traits as described in Fernandes and Lipka (2020) <doi:10.1186/s12859-020-03804-y>.
Simple result caching in R based on R.cache. The global environment is not 
    considered when caching results simplifying moving files between multiple instances 
    of R. Relies on more base functions than R.cache (e.g. cached results are saved using 
    saveRDS() and readRDS()).
Runs R-code present in a pandoc markdown file and 
  includes the resulting output in the resulting markdown file. This
  file can then be converted into any of the output formats 
  supported by pandoc. The package can also be used as an engine
  for writing package vignettes.
When working across multiple machines and, similarly for
    reproducible research, it can be time consuming to ensure that you have
    all of the needed packages installed and loaded and that the correct working
    directory is set. 'simpleSetup' provides simple functions for making these
    tasks more straightforward.
Wrapper functions around the amazing 'leaflet' 
    package that aims to simplify 'leaflet' visualisation. 
    See the 'ggblanket' package for 'ggplot2' wrappers.
Simplex density, distribution, quantile functions as well as random variable
   	generation of the simplex distribution are given. Regression analysis of proportional data
   	using various kinds of simplex models is available. In addition, GEE method can be applied 
	to longitudinal data to model the correlation. Residual analysis is also involved. Some 
	subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the 
	computation.
Provides an interface to a Simplex Tree data structure, which is 
  a data structure aimed at enabling efficient manipulation of simplicial complexes 
  of any dimension. The Simplex Tree data structure was originally introduced by 
  Jean-Daniel Boissonnat and Clément Maria (2014) <doi:10.1007/s00453-014-9887-3>.
Provides methods to integrate functions over m-dimensional simplices
 in n-dimensional Euclidean space.  There are exact methods for polynomials and
 adaptive methods for integrating an arbitrary function.
Network sparsification with a variety of novel and known network sparsification 
    techniques. All network sparsification techniques reduce the number of edges, not the number 
    of nodes. Network sparsification is sometimes referred to as network dimensionality reduction. 
    This package is based on the work of Spielman, D., Srivastava, N. (2009)<arXiv:0803.0929>.
    Koutis I., Levin, A., Peng, R. (2013)<arXiv:1209.5821>. Toivonen, H., Mahler, S., Zhou, F. 
    (2010)<doi:10.1007>. Foti, N., Hughes, J., Rockmore, D. (2011)<doi:10.1371>.
Pairwise group comparisons are often performed. While there are many packages that can perform these analyses, often it is the case that only a subset of comparisons are desired. 'SimplifyStats' performs pairwise comparisons and returns the results in a tidy fashion.
Reliability and agreement analyses often have limited software support. Therefore, this package was created to make agreement and reliability analyses easier for the average researcher. The functions within this package include simple tests of agreement, agreement analysis for nested and replicate data, and provide robust analyses of reliability. In addition, this package contains a set of functions to help when planning studies looking to assess measurement agreement. For robust analyses of agreement, limits of agreement through a bootstrap method can also be calculated.
An extension of the 'AlphaSimR' package
  (<https://cran.r-project.org/package=AlphaSimR>) for stochastic simulations of
  honeybee populations and breeding programmes. 'SIMplyBee' enables simulation of
  individual bees that form a colony, which includes a queen, fathers (drones
  the queen mated with), virgin queens, workers, and drones. Multiple colony can
  be merged into a population of colonies, such as an apiary or a whole country
  of colonies. Functions enable operations on castes, colony, or colonies, to
  ease 'R' scripting of whole populations. All 'AlphaSimR' functionality with
  respect to genomes and genetic and phenotype values is available and further
  extended for honeybees, including haplo-diploidy, complementary sex determiner
  locus, colony events (swarming, supersedure, etc.), and colony phenotype values.
Tools and methods to simulate populations for surveys based
    on auxiliary data. The tools include model-based methods, calibration and
    combinatorial optimization algorithms, see Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v079.i10>) and
    Templ (2017) <doi:10.1007/978-3-319-50272-4>. The package was developed with support of
    the International Household Survey Network, DFID Trust Fund TF011722 and funds
    from the World bank.
A general, 'tidyverse'-friendly framework 
  for simulation studies, design analysis, and power analysis. 
  Specify data generation, define varying parameters, generate data, 
  fit models, and tidy model results in a single pipeline, without 
  needing loops or custom functions.
Easy to use interfaces to a number of imputation methods
        that fit in the not-a-pipe operator of the 'magrittr' package.
Calculate power for generalised linear mixed models, using
    simulation. Designed to work with models fit using the 'lme4' package.
    Described in Green and MacLeod, 2016 <doi:10.1111/2041-210X.12504>.
Provides a number of functions to simulate restriction enzyme digestion, library construction and fragments size selection to predict the number of loci expected from most of the Restriction site Associated DNA (RAD) and Genotyping By Sequencing (GBS) approaches. SimRAD estimates the number of loci expected from a particular genome depending on the protocol type and parameters allowing to assess feasibility, multiplexing capacity and the amount of sequencing required.
Simulation of recurrent event data for non-constant baseline
    hazard in the total time model with risk-free intervals and possibly a competing event.
    Possibility to cut the data to an interim data set. Data can be plotted.
    Details about the method can be found in Jahn-Eimermacher, A. et al. (2015) <doi:10.1186/s12874-015-0005-2>.
Similarity regression,
    evaluating the probability of association between sets of ontological terms
    and binary response vector. A no-association model is compared with one in which
    the log odds of a true response is linked to the semantic similarity
    between terms and a latent characteristic ontological profile - 'Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases', Greene et al 2016 <doi:10.1016/j.ajhg.2016.01.008>.
Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts.
    This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an 'RStudio' gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) <doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., Almøy, T., Sæbø, S. (2018) <doi:10.1016/j.chemolab.2018.02.009>.
Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers,
  heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on
  resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to
  linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be
  highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the
  landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in
  homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using
  the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input
  parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as
  mechanistic null models (Potts & Lewis 2014, <DOI:10.1098/rspb.2014.0231>) to test a variety of 'Movement Ecology'
  hypotheses (Nathan et al. 2008, <DOI:10.1073/pnas.0800375105>), including landscape effects (e.g. resources, 
  infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk,
  dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as
  those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species
  spread, and population dynamics.
Routines to simulate and manipulate pedigrees ascertained to
    contain multiple family members affected by a rare disease. 
    Christina Nieuwoudt, Samantha J Jones, Angela Brooks-Wilson, 
    and Jinko Graham (2018) <doi:10.1101/234153>.
Generates data from R or 'JAGS' code for use in simulation
    studies.  The data are returned as an 'nlist::nlists' object and/or
    saved to file as individual '.rds' files.  Parallelization is
    implemented using the 'future' package.  Progress is reported using
    the 'progressr' package.
Tools for setting up ("design"), conducting, and evaluating
  large-scale simulation studies with graphics and tables, including
  parallel computations.
Contains two functions for simulating survival data from piecewise exponential hazards with a proportional hazards adjustment for covariates. The first function SimUNIVPiecewise simulates univariate survival data based on a piecewise exponential hazard, covariate matrix and true regression vector. The second function SimSCRPiecewise semi-competing risks data based on three piecewise exponential hazards, three true regression vectors and three matrices of patient covariates (which can be different or the same). This simulates from the Semi-Markov model of Lee et al (2015) given patient covariates, regression parameters, patient frailties and baseline hazard functions.
Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.
RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard.
An implementation of a single-index regression for optimizing individualized dose rules from an observational study. To model interaction effects between baseline covariates and a treatment variable defined on a continuum, we employ two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear combination of the covariates (a single-index). An unspecified main effect for the covariates is allowed, which can also be modeled through a parametric model. A unique contribution of this work is in the parsimonious single-index parametrization specifically defined for the interaction effect term. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2021) "A single-index model with a surface-link for optimizing individualized dose rules" <arXiv:2006.00267v2> for detail of the method. The model can take a member of the exponential family as a response variable and can also take an ordinal categorical response. The main function of this package is simsl().
Stop signal task  data of go and stop trials is generated per participant. The simulation process is based on the generally non-independent horse race model and fixed stop signal delay or tracking method. Each of go and stop process is assumed having exponentially modified Gaussian(ExG) or Shifted Wald (SW) distributions. The output data can be converted to 'BEESTS' software input data enabling researchers to test and evaluate various brain stopping processes manifested by ExG or SW distributional parameters of interest. Methods are described in: Soltanifar M (2020) <https://hdl.handle.net/1807/101208>, Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) <doi:10.3389/fpsyg.2013.00918>, Logan GD, Van Zandt T, Verbruggen F, Wagenmakers EJ. (2014) <doi:10.1037/a0035230>.
Creates simulated data from structural equation models with standardized loading. Data generation methods are described in Schneider (2013) <doi:10.1177/0734282913478046>.
Simulates data sets in order to explore modeling
    techniques or better understand data generating processes. The user
    specifies a set of relationships between covariates, and generates
    data based on these specifications. The final data sets can represent
    data from randomized control trials, repeated measure (longitudinal)
    designs, and cluster randomized trials. Missingness can be generated
    using various mechanisms (MCAR, MAR, NMAR).
Simulate survival times from standard parametric survival
    distributions (exponential, Weibull, Gompertz), 2-component mixture
    distributions, or a user-defined hazard, log hazard, cumulative hazard,
    or log cumulative hazard function. Baseline covariates can be included
    under a proportional hazards assumption.
    Time dependent effects (i.e. non-proportional hazards) can be included by
    interacting covariates with linear time or a user-defined function of time.
    Clustered event times are also accommodated.
    The 2-component mixture distributions can allow for a variety of flexible
    baseline hazard functions reflecting those seen in practice.
    If the user wishes to provide a user-defined
    hazard or log hazard function then this is possible, and the resulting
    cumulative hazard function does not need to have a closed-form solution.
    For details see the supporting paper <doi:10.18637/jss.v097.i03>.
    Note that this package is modelled on the 'survsim' package available in
    the 'Stata' software (see Crowther and Lambert (2012)
    <https://www.stata-journal.com/sjpdf.html?articlenum=st0275> or
    Crowther and Lambert (2013) <doi:10.1002/sim.5823>).
Simulate age-structured populations that vary in space and time and 
    explore the efficacy of a range of built-in or user-defined sampling 
    protocols to reproduce the population parameters of the known population. 
    (See Regular et al. (2020) <doi:10.1371/journal.pone.0232822> for more
    details).
Provides functions to simulate from joint survival and marker 
    models. The user can specific all basis functions of time, random or 
    deterministic covariates, random or deterministic left-truncation and 
    right-censoring times, and model parameters.
Transforms or simulates data with a target empirical covariance matrix supplied
             by the user. The method to obtain the data with the target empirical covariance
             matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019)
             <arXiv:1812.05678>.
Handles datetimes as integers for the usage inside
        Discrete-Event Simulations (DES). The conversion is made using
        the internally generic function as.numeric() of the base
        package. DES is described in Simulation Modeling and Analysis
        by Averill Law and David Kelton (1999) <doi:10.2307/2288169>.
Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. <arXiv:1709.10074> for methodological details.
Tool for statistical simulations that have two components. 
    One component generates the data and the other one
    analyzes the data. The main aims of the package are the reduction
    of the administrative source code (mainly loops and management code for the
    results) and a simple applicability of the package that allows the user to
    quickly learn how to work with it. Parallel computing is
    also supported. Finally, convenient functions are provided to summarize the
    simulation results.
Simulate complex traits given a SNP genotype matrix and model parameters (the desired heritability, number of causal loci, and either the true ancestral allele frequencies used to generate the genotypes or the mean kinship for a real dataset).  Emphasis on avoiding common biases due to the use of estimated allele frequencies.  The code selects random loci to be causal, constructs coefficients for these loci and random independent non-genetic effects, and can optionally generate random group effects.  Traits can follow three models: random coefficients, fixed effect sizes, and infinitesimal (multivariate normal).  GWAS method benchmarking functions are also provided.  Described in Yao and Ochoa (2022) <doi:10.1101/2022.03.25.485885>.
A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.
Implements two iterative techniques called T3Clus and 3Fkmeans, aimed at simultaneously clustering objects and a factorial dimensionality reduction of variables and occasions on three-mode datasets developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Also, we provide a convex combination of these two simultaneous procedures called CT3Clus and based on a hyperparameter alpha (alpha in [0,1], with 3FKMeans for alpha=0 and T3Clus for alpha= 1) also developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Furthermore, we implemented the traditional tandem procedures of T3Clus (TWCFTA) and 3FKMeans (TWFCTA) for sequential clustering-factorial decomposition (TWCFTA), and vice-versa (TWFCTA) proposed by P. Arabie and L. Hubert (1996) <doi:10.1007/978-3-642-79999-0_1>.
Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017) <doi:10.18637/jss.v021.i07>, Christian P. Robert and George Casella (2010 ISBN:978-1-4419-1575-7) <doi:10.1007/978-1-4419-1576-4>.
A set of tools developed at Simularia for Simularia, to help
    preprocessing and post-processing of meteorological and air quality data.
A framework for performing simulations such as those common in
    methodological statistics papers.  The design principles of this package
    are described in greater depth in Bien, J. (2016) "The simulator: An Engine
    to Streamline Simulations," which is available at <arXiv:1607.00021>.
This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) <DOI:10.1007/s10994-017-5635-7>.
Simulate genotypes in SNP (single nucleotide polymorphisms) Matrix as random numbers from an uniform distribution, for diploid organisms (coded by 0, 1, 2), Sikorska et al., (2013) <doi:10.1186/1471-2105-14-166>, or half-sib/full-sib SNP matrix from real or simulated parents SNP data, assuming mendelian segregation. Simulate phenotypic traits for real or simulated SNP data, controlled by a specific number of quantitative trait loci  and their effects, sampled from a Normal or an Uniform distributions, assuming a pure additive model. This is useful for testing association and genomic prediction models or for educational purposes.
Simulation tools for planning Vitamin D studies. Individual vitamin D status 
             profiles are simulated, modelling population heterogeneity in trial arms. 
             Exposures to infectious agents are generated, with infection depending on vitamin D status.
The sinaplot is a data visualization chart suitable for plotting
    any single variable in a multiclass data set. It is an enhanced jitter strip
    chart, where the width of the jitter is controlled by the density
    distribution of the data within each class.
Implementation of the Conditional Least Square (CLS) estimates and
    its covariance matrix for the first-order spatial integer-valued
    autoregressive model (SINAR(1,1)) proposed by Ghodsi (2012)
    <doi:10.1080/03610926.2011.560739>.
This implements the Brunton et al (2016; PNAS <doi:10.1073/pnas.1517384113>) sparse
    identification algorithm for finding ordinary differential equations for a measured 
    system from raw data (SINDy). The package includes a set of additional tools for 
    working with raw data, with an emphasis on cognitive science applications (Dale 
    and Bhat, in press <doi:10.1016/j.cogsys.2018.06.020>).
Manage package documentation and namespaces from the command line. 
             Programmatically attach namespaces in R and Rmd script, populates 
             'Roxygen2' skeletons with information scraped from within functions and 
             populate the Imports field of the DESCRIPTION file.
When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) <doi:10.1076/clin.12.4.482.7241>, Crawford and Garthwaite (2005) <doi:10.1037/0894-4105.19.3.318>, Crawford and Garthwaite (2007) <doi:10.1080/02643290701290146> and Crawford, Garthwaite and Ryan (2011) <doi:10.1016/j.cortex.2011.02.017>. The package is also equipped with power calculators for each method.
Provides R functions for calculating basic effect size indices for 
  single-case designs, including several non-overlap measures and parametric 
  effect size measures, and for estimating the gradual effects model developed 
  by Swan and Pustejovsky (2018) <DOI:10.1080/00273171.2018.1466681>. 
  Standard errors and confidence intervals (based on the assumption that the outcome 
  measurements are mutually independent) are provided for the subset of effect sizes 
  indices with known sampling distributions.
One key exploratory analysis step in single-cell genomics data analysis
    is the prediction of features with different activity levels. For example, we want 
    to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, 
    spatial DEGs in spatial transcriptomics data, or differentially accessible 
    regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially
    active features in single cell omics datasets without relying on the clustering
    of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler 
    divergence to find features (e.g., genes, genomic regions, etc) that are active
    in subsets of cells that are non-randomly positioned inside an input space (such as 
    1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For 
    the theoretical background of 'singleCellHaystack' we refer to our original paper
    Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3>
    and our update Vandenbon and Diez (bioRxiv, 2022) <doi:10.1101/2022.11.13.516355>.
Implementation of single-source capture-recapture methods for population size estimation using zero-truncated, zero-one truncated and zero-truncated one-inflated Poisson, Geometric and Negative Binomial regression as well as Zelterman's and Chao's regression. Package includes point and interval estimators for the population size with variances estimated using analytical or bootstrap method. Details can be found in: van der Heijden et all. (2003) <doi:10.1191/1471082X03st057oa>, B{\"o}hning and van der Heijden (2019) <doi:10.1214/18-AOAS1232>, B{\"o}hning et al. (2020) Capture-Recapture Methods for the Social and Medical Sciences or B{\"o}hning and Friedl (2021) <doi:10.1007/s10260-021-00556-8>.
Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.
Density, distribution function, quantile function 
	and random generation for the sum of independent non-identical
	binomial distribution with parameters \code{size} and \code{prob}.
Density, distribution function, quantile function, random
    generation and survival function for the Sine Inverse Weibull Distribution as
    defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
Provides access to packages developed for downloading, reading and analyzing microdata 
  from household surveys conducted by Brazilian Institute of Geography and Statistics - IBGE.
	More information can be obtained from the official website <https://www.ibge.gov.br/>.
Processor for selected ion flow tube mass spectrometer
            (SIFT-MS) output file from breath analysis. It allows the filtering of the SIFT output file (i.e., variation
            over time of the target analyte concentration) and the following
            analysis for the determination of: maximum, average, and standard deviation
            value of target concentration measured at each exhalation, and the
            respiratory rate over the measurement. Additionally, it is possible to
            align the SIFT-MS data with other on-line techniques such as 
            cardio pulmonary exercise test (CPET) for a comprehensive 
            characterization of breath samples.
A simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age.
A platform for computing competition indices and experimenting
    with spatially explicit individual-based vegetation models.
Statistical analysis as part of a stable isotope probing (SIP) metagenomics study to identify isotope incorporating taxa recovered as metagenome-assembled genomes (MAGs). 
    Helpful reading and a vignette in bookdown format is provided on the package site <https://zielslab.github.io/SIPmg.github.io/>.
Single-Index Quantile Regression is effective in some scenarios. We provides functions that allow users to fit Single-Index Quantile Regression model. It also provides functions to do prediction, estimate standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. Please see W., Y., Y. (2010) <doi:10.1016/j.jmva.2010.02.003> for details.
Calculating daily global solar radiation at horizontal surface using several well-known models (i.e. Angstrom-Prescott, Supit-Van Kappel, Hargreaves, Bristow and Campbell, and Mahmood-Hubbard), and model calibration based on ground-truth data, and (3) model auto-calibration. The FAO Penmann-Monteith equation to calculate evapotranspiration is also included.
Provides two main functionalities.
    1 - Given a system of simultaneous equation,
    it decomposes  the matrix of coefficients weighting the endogenous variables 
    into three submatrices: one includes the subset of coefficients that have a causal nature
    in the model, two include the subset of coefficients that have a interdependent nature
    in the model, either at systematic level or induced by the correlation between error terms.
    2 - Given a decomposed model,
    it tests for the significance of the interdependent relationships acting in the system, 
    via Maximum likelihood and Wald test, which can be built starting from the function output.
    For theoretical reference see Faliva (1992) <doi:10.1007/BF02589085> and 
    Faliva and Zoia (1994) <doi:10.1007/BF02589041>.
Performs hybrid multi-stage factor analytic procedure for controlling acquiescence in restricted solutions (Ferrando & Lorenzo-Seva, 2000 <https://www.uv.es/revispsi/articulos3.00/ferran7.pdf>).
We build an Susceptible-Infectious-Recovered (SIR) model where the rate of infection is the sum of the household rate and the community rate. We estimate the posterior distribution of the parameters using the Metropolis algorithm. Further details may be found in: F Scott Dahlgren, Ivo M Foppa, Melissa S Stockwell, Celibell Y Vargas, Philip LaRussa, Carrie Reed (2021) "Household transmission of influenza A and B within a prospective cohort during the 2013-2014 and 2014-2015 seasons" <doi:10.1002/sim.9181>.
Supplementary functions for item response models aiming
    to complement existing R packages. The functionality includes among others
    multidimensional compensatory and noncompensatory IRT models
    (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), 
    MCMC for hierarchical IRT models and testlet models
    (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), 
    NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), 
    Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>;
    Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>),
    faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011,
    <doi:10.1111/j.1745-3984.2011.00143.x>),
    ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), 
    DETECT statistic (Stout, Habing, Douglas & Kim, 1996, 
    <doi:10.1177/014662169602000403>), local structural equation modeling 
    (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016,
    <doi:10.1080/00273171.2016.1142856>).
Implements a thresholded version of the Sliced Inverse Regression method (Li, K. C. (1991) <doi:10.2307/2290563>), which allows to do variable selection.
A regression and classification algorithm based on random forests, which takes the form of a short list of rules. SIRUS combines the simplicity of decision trees with a predictivity close to random forests. The core aggregation principle of random forests is kept, but instead of aggregating predictions, SIRUS aggregates the forest structure: the most frequent nodes of the forest are selected to form a stable rule ensemble model. The algorithm is fully described in the following articles: Benard C., Biau G., da Veiga S., Scornet E. (2021), Electron. J. Statist., 15:427-505 <DOI:10.1214/20-EJS1792> for classification, and Benard C., Biau G., da Veiga S., Scornet E. (2021), AISTATS, PMLR 130:937-945 <http://proceedings.mlr.press/v130/benard21a>, for regression. This R package is a fork from the project ranger (<https://github.com/imbs-hl/ranger>).
Variable selection techniques are essential tools for model
    selection and estimation in high-dimensional statistical models. Through this
    publicly available package, we provide a unified environment to carry out
    variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all
    of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards
    model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).
Implements the SISAL algorithm by Tikka and Hollmén. It is
        a sequential backward selection algorithm which uses a linear
        model in a cross-validation setting. Starting from the full
        model, one variable at a time is removed based on the
        regression coefficients. From this set of models, a
        parsimonious (sparse) model is found by choosing the model with
        the smallest number of variables among those models where the
        validation error is smaller than a threshold. Also implements
        extensions which explore larger parts of the search space
        and/or use ridge regression instead of ordinary least squares.
Interval fusion and selection procedures for regression with 
             functional inputs. Methods include a semiparametric approach based
             on Sliced Inverse Regression (SIR), as described in 
             <doi:10.1007/s11222-018-9806-6> (standard ridge and sparse SIR are 
             also included in the package) and a random forest based approach.
Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen").
The Brazilian system for diploma registration and validation on technical and superior
    courses are managing by 'Sistec' platform, see <https://sistec.mec.gov.br/>. This package provides 
    tools for Brazilian institutions to update the student's registration and make data analysis 
    about their situation, retention and drop out.
Functions common to members of the SISTM team.
Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.
Computes the sit coefficient between two vectors x and y, 
  possibly all paired coefficients for a matrix. The reference for the methods implemented here is 
  Zhang, Yilin, Canyi Chen, and Liping Zhu. 2022. "Sliced Independence Test." Statistica Sinica. <doi:10.5705/ss.202021.0203>. 
  This package incorporates the Galton peas example.
Functions for fitting and plotting SITAR (Super
    Imposition by Translation And Rotation) growth curve models. SITAR is
    a shape-invariant model with a regression B-spline mean curve and
    subject-specific random effects on both the measurement and age
    scales.  The model was first described by Lindstrom (1995)
    <doi:10.1002/sim.4780141807> and developed as the SITAR method by Cole
    et al (2010) <doi:10.1093/ije/dyq115>.
The SiteAdapt procedure improves the accuracy of modeled solar irradiance series
    through site-adaptation with coincident ground-based measurements relying on the use of a
    regression preprocessing followed by an empirical quantile mapping (eQM) correction.
    Fernandez-Peruchena et al (2020) <doi:10.3390/rs12132127>.
Carries out a two-level sample selection where the possibility of an initially selected site not wanting to participate is anticipated, and the site is optimally replaced. The procedure aims to reduce bias (and/or loss of external validity) with respect to the target population. In selecting units and sub-units, 'sitepickR' uses the cube method developed by 'Deville & Tillé', (2004) <http://www.math.helsinki.fi/msm/banocoss/Deville_Tille_2004.pdf> and described in Tillé (2011) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2011002/article/11609-eng.pdf?st=5-sx8Q8n>. The cube method is a probability sampling method that is designed to satisfy criteria for balance between the sample and the population. Recent research has shown that this method performs well in simulations for studies of educational programs (see Fay & Olsen (2021, under review). To implement the cube method, 'sitepickR' uses the sampling R package <https://cran.r-project.org/package=sampling>. To implement statistical matching, 'sitepickR' uses the 'MatchIt' R package <https://cran.r-project.org/package=MatchIt>.
Within the area of movement ecology, it can be useful to identify an animal's sites of interest. These will be places that an animal uses a lot and so will spend a proportionately longer time there. The aim of this package is to identify both the position and size of these sites of interest from high resolution (sub-second) positional data.
The goal of 'siteymlgen' is to make it easy to organise
  the building of your 'R Markdown' website.
  The init() function placed within the first code chunk of the index.Rmd
  file of an 'R' project directory will initiate the generation of an automatically
  written _site.yml file. 'siteymlgen' recommends a specific naming
  convention for your 'R Markdown' files. This naming will ensure that
  your navbar layout is ordered according to a hierarchy.
Implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) <doi:10.1038/nature14971>. Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor.
Provided within are two high quality and fast PPRNGs that may be
    used in an 'OpenMP' parallel environment. In addition, there is a generator
    for one dimensional low-discrepancy sequence. The objective of this library
    to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and
    'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the
    header files inside of 'sitmo' instead of including a copy of each engine
    within their individual package. Lastly, the package contains example
    implementations using the 'sitmo' package and three accompanying vignette that
    provide additional information.
Format a number (or a list of numbers) to a string (or a
        list of strings) with SI prefix. Use SI prefixes as constants
        like (4 * milli)^2
Framework to build an individual tree simulator.
Provides extensions for package 'sitree' for allometric variables, growth, mortality, recruitment, management, tree removal and external modifiers functions.
An end-to-end toolkit for land use and land cover classification
    using big Earth observation data, based on machine learning methods 
    applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>.
    Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, 
    Brazil Data Cube, and Digital Earth Africa using the Spatio-temporal Asset Catalog (STAC) 
    protocol (<https://stacspec.org/> and the 'gdalcubes' R package 
    developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>.
    Supports visualization methods for images and time series and 
    smoothing filters for dealing with noisy time series.
    Includes functions for quality assessment of training samples using self-organized maps 
    as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. 
    Provides machine learning methods including support vector machines, 
    random forests, extreme gradient boosting, multi-layer perceptrons,
    temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, 
    residual networks by Fawaz et al (2019) <doi:10.1007/s10618-019-00619-1>, and temporal attention encoders
    by Garnot and Landrieu (2020) <arXiv:2007.00586>.
    Performs efficient classification of big Earth observation data cubes and includes 
    functions for post-classification smoothing based on Bayesian inference, and 
    methods for uncertainty assessment. Enables best
    practices for estimating area and assessing accuracy of land change as 
    recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>.
    Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.
An iterative feature selection method (manuscript submitted) that
    internally utilizes various Machine Learning methods that have embedded
    feature reduction in order to shrink down the feature space into a small
    and yet robust set.
Functions and utilities to perform
    Statistical Analyses in the Six Sigma way.
    Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control),
    you can manage several Quality Management studies:
    Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis,
    etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2]
    and "Quality Control with R" [ISBN 978-3-319-24046-6], 
    are also included in the package.
This function develops an algorithm for presenting a Bayesian hierarchical model for estimating the sizes of local and national drug injected populations in Bangladesh. The model incorporates multiple commonly used data sources including mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and expert opinion.
Estimate morphometric and gonadal size at sexual maturity for organisms, usually fish and invertebrates. It includes methods for classification based on relative growth (using principal components analysis, hierarchical clustering, discriminant analysis), logistic regression (Frequentist or Bayes), parameters estimation and some basic plots.
Calculates and plots the SiZer map for scatterplot data.  A 
  SiZer map is a way of examining when the p-th derivative of a 
  scatterplot-smoother is significantly negative, possibly zero or 
  significantly positive across a range of smoothing bandwidths.
Provides a database-independent JDBC interface.
Collection of functions dealing with labelled data, like reading and 
    writing data between R and other statistical software packages like 'SPSS',
    'SAS' or 'Stata', and working with labelled data. This includes easy ways 
    to get, set or change value and variable label attributes, to convert 
    labelled vectors into factors or numeric (and vice versa), or to deal with 
    multiple declared missing values.
Collection of miscellaneous utility functions, supporting data 
    transformation tasks like recoding, dichotomizing or grouping variables, 
    setting and replacing missing values. The data transformation functions 
    also support labelled data, and all integrate seamlessly into a 
    'tidyverse'-workflow.
Collection of plotting and table output functions for data
    visualization. Results of various statistical analyses (that are commonly used
    in social sciences) can be visualized using this package, including simple and
    cross tabulated frequencies, histograms, box plots, (generalized) linear models,
    mixed effects models, principal component analysis and correlation matrices, 
    cluster analyses, scatter plots, stacked scales, effects plots of regression 
    models (including interaction terms) and much more. This package supports
    labelled data.
A scalable method to estimate joint Species Distribution Models (jSDMs) for big community datasets based on a Monte Carlo approximation of the joint likelihood.  The numerical approximation is based on 'PyTorch' and 'reticulate', and can be run on CPUs and GPUs alike. The method is described in Pichler & Hartig (2021) <doi:10.1111/2041-210X.13687>. The package contains various extensions, including support for different response families, ability to account for spatial autocorrelation, and deep neural networks instead of the linear predictor in jSDMs.
Collection of convenient functions for common statistical computations,
             which are not directly provided by R's base or stats packages.
             This package aims at providing, first, shortcuts for statistical measures, 
             which otherwise could only be calculated with additional effort 
             (like Cramer's V, Phi, or effect size statistics like Eta or Omega squared), 
             or for which currently no functions available. Second, another focus 
             lies on weighted variants of common statistical measures and tests 
             like weighted standard error, mean, t-test, correlation, and more.
A small set of helper functions to convert 'sjPlot'
    HTML-tables to R data.frame objects / knitr::kable-tables.
In forensics, it is common and effective practice to analyse glass fragments from the scene and suspects 
  to gain evidence of placing a suspect at the crime scene. 
  This kind of analysis involves comparing the physical and chemical attributes of glass fragments that exist on both the 
  person and at the crime scene, and assessing the significance in a likeness that they share. 
  The package implements the Scott-Knott Modification 2 algorithm (SKM2) (Christopher M. Triggs and 
  James M. Curran and John S. Buckleton and Kevan A.J. Walsh (1997) <doi:10.1016/S0379-0738(96)02037-3> 
  "The grouping problem in forensic glass analysis: a divisive approach", Forensic Science International, 85(1), 1--14)
  for small sample glass fragment analysis using the refractive index (ri) of a set of glass samples. 
  It also includes an experimental multivariate analog to the Scott-Knott algorithm for similar analysis on glass samples
  with multiple chemical concentration variables and multiple samples of the same item; testing against the Hotellings T^2 
  distribution (J.M. Curran and C.M. Triggs and J.R. Almirall and J.S. Buckleton and K.A.J. Walsh (1997) 
  <doi:10.1016/S1355-0306(97)72197-X> "The interpretation of elemental composition measurements from forensic 
  glass evidence", Science & Justice, 37(4), 241--244).
Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.
Utilities for single nucleotide polymorphism (SNP) based kinship analysis
    testing and evaluation. The 'skater' package contains functions for importing, parsing, 
    and analyzing pedigree data, performing relationship degree inference, benchmarking 
    relationship degree classification, and summarizing identity by descent (IBD) segment data.
    Package functions and methods are described in Turner et al. (2021) "skater: An R package 
    for SNP-based Kinship Analysis, Testing, and Evaluation" <doi:10.1101/2021.07.21.453083>.
A tool for bootstrapping new packages with useful defaults,
    including a test suite outline that passes checks and helpers for running
    tests, checking test coverage, building vignettes, and more. Package
    skeletons it creates are set up for pushing your package to
    'GitHub' and using other hosted services for building and test automation.
Functions for the Skellam distribution, including: density
    (pmf), cdf, quantiles and regression.
Creates static / animated / interactive visualisations embeddable
    in R Markdown documents. It implements an R-to-JavaScript transpiler and
    enables users to write JavaScript applications using the syntax of R.
An implementation of image processing effects that convert a photo into a line drawing image. 
    For details, please refer to Tsuda, H. (2020). sketcher: An R package for converting a photo into a sketch style image. 
    <doi:10.31234/osf.io/svmw5>.
Construct sketches of data via random subspace embeddings. 
  For more details, see the following papers.
  Lee, S. and Ng, S. (2022). "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Proceedings of the 39th International Conference on Machine Learning (ICML22), 162:12498-12520.
  Lee, S. and Ng, S. (2020). "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, 12(1): 45–80.
Provides functions to create and manage research compendiums for data analysis. Research compendiums are a standard and intuitive folder structure for organizing the digital materials of a research project, which can significantly improve reproducibility. The package offers several compendium structure options that fit different research project as well as the ability of duplicating the folder structure of existing projects or implementing custom structures. It also simplifies the use of version control.
Implementations of models which follow the Kolmogorov Birth-Death process framework and functions which utilize these Kolmogorov Birth-Death process models for analysis of skewed distribution functions.
Functions are provided for the density function,
	     distribution function, quantiles and random number
	     generation for the skew hyperbolic
	     t-distribution. There are also functions that fit
	     the distribution to data. There are functions for the
	     mean, variance, skewness, kurtosis and mode of a given
	     distribution and to calculate moments of any order about
	     any centre. To assess goodness of fit, there are
	     functions to generate a Q-Q plot, a P-P plot and a tail plot.
It fits scale mixture of skew-normal linear mixed models using an expectation–maximization (EM) type algorithm, including some possibilities for modeling the within-subject dependence. Details can be found in Schumacher, Lachos and Matos (2021) <doi:10.1002/sim.8870>.
Provide data generation and estimation tools for the multivariate scale mixtures of normal 
             presented in Lange and Sinsheimer (1993) <doi:10.2307/1390698>, the multivariate scale 
             mixtures of skew-normal presented in Zeller, Lachos and Vilca (2011) 
             <doi:10.1080/02664760903406504>, the multivariate skew scale mixtures of normal 
             presented in Louredo, Zeller and Ferreira (2021) <doi:10.1007/s13571-021-00257-y>
             and the multivariate scale mixtures of skew-normal-Cauchy presented in Kahrari et al. (2020)
	     <doi:10.1080/03610918.2020.1804582>.
Estimate necessary sample sizes for comparing the location
    of data from two groups or categories when the distribution of the 
    data is skewed. The package 
    offers a non-parametric method for a Wilcoxon Mann-Whitney test of 
    location shift as well as methods for several generalized linear 
    models, for instance, Gamma regression.
Density, distribution function, quantile function and
        random generation for the skewed t distribution of Fernandez
        and Steel.
A generalization of the statistic used in Friedman's ANOVA method and in Durbin's rank test. This nonparametric statistical test is useful for the data obtained from block designs with missing observations occurring randomly. A resulting p-value is based on the chi-squared distribution and Monte Carlo method.
Functions that simplify calls to the 'Skilljar' API. See 
    <https://api.skilljar.com/docs/> for documentation on the 'Skilljar' API.
    This package is not supported by 'Skilljar'.
A simple to use summary function that can be used with pipes
    and displays nicely in the console. The default summary statistics may
    be modified by the user as can the default formatting.  Support for
    data frames and vectors is included, and users can implement their own
    skim methods for specific object types as described in a vignette.
    Default summaries include support for inline spark graphs.
    Instructions for managing these on specific operating systems are
    given in the "Using skimr" vignette and the README.
Provides tools for applying Sklar's Omega (Hughes, 2018) <arXiv:1803.02734>
    methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e.,
    non-negative real numbers), and balances (i.e., any real number). The framework can
    accommodate any number of units, any number of coders, and missingness; and
    can be used to measure agreement with a gold standard, intra-coder agreement,
    and/or inter-coder agreement. Frequentist inference is supported for all levels
    of measurement. Bayesian inference is supported for continuous scores only.
Algorithms for solving selective k-means problem,
    which is defined as finding k rows in an m x n matrix such that 
    the sum of each column minimal is minimized. 
    In the scenario when m == n and each cell value in matrix is a 
    valid distance metric, this is equivalent to a k-means problem. 
    The selective k-means extends the k-means problem in the sense 
    that it is possible to have m != n, often the case m < n which 
    implies the search is limited within a small subset of rows. 
    Also, the selective k-means extends the k-means problem in the 
    sense that the instance in row set can be instance not seen in 
    the column set, e.g., select 2 from 3 internet service provider
    (row) for 5 houses (column) such that minimize the overall cost 
    (cell value) - overall cost is the sum of the column minimal of
    the selected 2 service provider.
Algorithms to compute spherical k-means partitions.
  Features several methods, including a genetic and a fixed-point
  algorithm and an interface to the CLUTO vcluster program.
It's a Super K-Nearest Neighbor classification method with using kernel density to describe weight of the distance between a training observation and the testing sample.
Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.
Provides an alternative to manually process hemispherical pictures. The algorithm processes each picture one by one to determine the proportion of sky pixel. The algorithm uses the Ridler and Calvard method (Ridler and Calvard 1978).
A tool to calculate sky illuminance values (in lux) for both sun and moon. The
 model is a verbatim translation of the code by Janiczek and DeYoung (1987)
 <https://archive.org/details/DTIC_ADA182110>.
A flexible tool that allows generating bespoke
    air transport statistics for urban studies based on publicly available
    data from the Bureau of Transport Statistics (BTS) in the United States
    <https://www.transtats.bts.gov/databases.asp?Z1qr_VQ=E&Z1qr_Qr5p=N8vn6v10&f7owrp6_VQF=D>.
Data reduction, visualization and statistical analysis of measurements of orientation of archaeological structures, following Silva (2020) <doi:10.1016/j.jas.2020.105138>.
'Slack' <https://slack.com/> provides a service for teams to
    collaborate by sharing messages, images, links, files and more.
    Functions are provided that make it possible to interact with the
    'Slack' platform 'API'. When you need to share information or data
    from R, rather than resort to copy/ paste in e-mails or other services
    like 'Skype' <https://www.skype.com/en/>, you can use this package to
    send well-formatted output from multiple R objects and expressions to
    all teammates at the same time with little effort. You can also send
    images from the current graphics device, R objects, and upload files.
Data structures and algorithms for sparse arrays and matrices,
  based on index arrays and simple triplet representations, respectively.
Slanted matrices and ordered clustering for better visualization of similarity data.
A self-contained hypothesis is tested for a given pathway of longitudinal omics. 'SlaPMEG' is a two-step procedure. First, a shared latent process mixed model is fitted over the longitudinal measures of omics in a pathway. This shared model allows deviation from the shared process at subject level (a random intercept, slope, or both per subject) and also at omic level (a random effect per omic). These random effects summarize the longitudinal trend of the observations which can be used to test for group differences using 'Globaltest' in the second step. If the pathway is large or the shared effect is small, the package fits a series of pairwise models and estimates the shared random effects based on them.
Implements the smooth LASSO estimator for the function-on-function linear regression model described in Centofanti et al. (2020) <arXiv:2007.00529>.
Programs for analyzing large-scale time series data. They include functions for automatic specification and estimation of univariate time series, for clustering time series, for multivariate outlier detections, for quantile plotting of many time series, for dynamic factor models and for creating input data for deep learning programs. Examples of using the package can be found in the Wiley book 'Statistical Learning with Big Dependent Data' by Daniel Peña and Ruey S. Tsay (2021). ISBN 9781119417385.
An easy-to-use tool for latent class analysis for recurrent events. The modeling framework is based on the semiparametric multiplicative modeling in Zhao et al. (2022) <doi:10.1111/rssb.12499>. Our package provides an alternative method to define initial values in the estimation algorithm based on a joint frailty scale-change model described in Wang et al. (2001) <doi:10.1198/016214501753209031> and K-means. Users are also allowed to specify different initial values by themselves. Our package also provides an alternative algorithm to solving the estimating equation for unobservable latent class membership by fitting a "pseudo" weighted multinomial regression which speeds up the rate of convergence.
The skew logistic distribution is a quantile-defined generalisation
 of the logistic distribution (van Staden and King 2015).  Provides random 
 numbers, quantiles, probabilities, densities and density quantiles for the distribution.
 It provides Quantile-Quantile plots and method of L-Moments estimation 
 (including asymptotic standard errors) for the distribution.
Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for concentration of infectious units from serial limiting dilution assays. This package uses the likelihood equation, exact goodness of fit p-values, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>. This software is also implemented as a web application through the Shiny R package <https://iupm.shinyapps.io/sldassay/>.
Compute Time series Resistant Smooth 4253H, twice smoothing method.
Sleep cycles are largely detected according to the originally proposed criteria by Feinberg & Floyd (1979) <doi:10.1111/j.1469-8986.1979.tb02991.x> as described in Blume & Cajochen (2021) <doi:10.1016/j.mex.2021.101318>.
For those wishing to interact with the 'Sleeper' (Fantasy Sports) API (<https://api.sleeper.app/>) without looking too much into its documentation (found at <https://docs.sleeper.app/>), this package offers wrapper functions around the available API calls to make it easier.
Use behavioural variables to score activity and infer sleep from bouts of immobility. 
    It is primarily designed to score sleep in fruit flies from Drosophila Activity Monitor (TriKinetics) and Ethoscope data.
    It implements sleep scoring using the "five-minute rule" (Hendricks et al. (2000) <DOI:10.1016/S0896-6273(00)80877-6>),
    activity classification for Ethoscopes (Geissmann et al. (2017) <DOI:10.1371/journal.pbio.2003026>) 
    and a new algorithm to detect when animals are dead.
A tool to interactively explore the
  embeddings created by dimension reduction methods such as 
  Principal Components Analysis (PCA), Multidimensional Scaling (MDS), 
  T-distributed Stochastic Neighbour Embedding (t-SNE),
  Uniform Manifold Approximation and Projection (UMAP) or any other.
Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset.
The implementation of the algorithm for estimation of mutual information and channel capacity from experimental data by classification procedures (logistic regression). Technically, it allows to estimate information-theoretic measures between finite-state input and multivariate, continuous output. Method described in Jetka et al. (2019) <doi:10.1371/journal.pcbi.1007132>.
A framework for simulating spatially explicit genomic data which
    leverages real cartographic information for programmatic and visual encoding
    of spatiotemporal population dynamics on real geographic landscapes. Population
    genetic models are then automatically executed by the 'SLiM' software by Haller
    et al. (2019) <doi:10.1093/molbev/msy228> behind the scenes, using a custom
    built-in simulation 'SLiM' script. Additionally, fully abstract spatial models
    not tied to a specific geographic location are supported, and users can also
    simulate data from standard, non-spatial, random-mating models. These can be
    simulated either with the 'SLiM' built-in back-end script, or using an efficient
    coalescent population genetics simulator 'msprime' by Baumdicker et al. (2022)
    <doi:10.1093/genetics/iyab229> with a custom-built 'Python' script bundled with the
    R package. Simulated genomic data is saved in a tree-sequence format and can be
    loaded, manipulated, and summarised using tree-sequence functionality via an R
    interface to the 'Python' module 'tskit' by Kelleher et al. (2019)
    <doi:10.1038/s41588-019-0483-y>. Complete model configuration, simulation and
    analysis pipelines can be therefore constructed without a need to leave the R
    environment, eliminating friction between disparate tools for population genetic
    simulations and data analysis.
Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (2nd
    ed)", Duxbury.
Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (3rd
    ed)", Cengage Learning.
Set of tools to find coherent patterns in gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM) <DOI:10.1007/978-3-319-12454-4_15>. Considerable effort has been put to build a fast and memory efficient package, which makes this proposal an interesting and computationally convenient alternative to study patterns of gene expressions exhibited in matrices. The package contains the implementation of two versions of the model based on different mixture priors for the loadings: one relies on a degenerate component at zero and the other uses a small variance normal distribution for the spike part of the mixture.
Implementation for sparse logistic functional principal component analysis (SLFPCA). SLFPCA is specifically developed for functional binary data, and the estimated eigenfunction can be strictly zero on some sub-intervals, which is helpful for interpretation. The crucial function of this package is SLFPCA().
Implements the Bayesian model selection method with suspected latent 
    grouping factor methodology of Metzger and Franck (2020), 
    <doi:10.1080/00401706.2020.1739561>. SLGF detects latent 
    heteroscedasticity or group-based regression effects based on the levels of a 
    user-specified categorical predictor.
Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".
Create and customize interactive carousels
      using the 'Slick' 'JavaScript' library and the
      'htmlwidgets' package. The carousels can contain
      plots produced in R, images, 'iframes', videos and
      other 'htmlwidgets'.  These carousels can be created
      directly from the R console, and viewed in the 'RStudio' 
      internal viewer, in 'Shiny' apps and R Markdown documents.
This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells 
	based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the 
	two cells being compared. Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) <DOI:10.1016/j.ymeth.2015.07.008>.
Provides type-stable rolling window functions over any R data
    type. Cumulative and expanding windows are also supported. For more
    advanced usage, an index can be used as a secondary vector that
    defines how sliding windows are to be created.
Create a side-by-side view of raster(image)s with an interactive 
    slider to switch between regions of the images. This can be especially useful
    for image comparison of the same region at different time stamps.
A collection of functions to perform Detrended Fluctuation Analysis (DFA exponent), GUEDES et al. (2019) <doi:10.1016/j.physa.2019.04.132> , Detrended cross-correlation coefficient (RHODCCA), GUEDES & ZEBENDE (2019) <doi:10.1016/j.physa.2019.121286>, DMCA cross-correlation coefficient and Detrended multiple cross-correlation coefficient (DMC), GUEDES & SILVA-FILHO & ZEBENDE (2018) <doi:10.1016/j.physa.2021.125990>, both with sliding windows approach.
Fits singular linear models to longitudinal data. Singular linear
    models are useful when the number, or timing, of longitudinal observations
    may be informative about the observations themselves. They are described
    in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the
    linear increments model <doi:10.1111/j.1467-9876.2007.00590.x> to general
    longitudinal data.
Sparse Linear Method(SLIM) predicts ratings and top-n
    recommendations suited for sparse implicit positive feedback systems. SLIM
    is decomposed into multiple elasticnet optimization problems which are solved
    in parallel over multiple cores. The package is based on "SLIM: Sparse Linear
    Methods for Top-N Recommender Systems" by Xia Ning and George Karypis <doi:10.1109/ICDM.2011.134>.
Provides functions for performing common tasks when working with
  slippy map tile service APIs e.g. Google maps, Open Street Map, Mapbox, Stamen,
  among others. Functionality includes converting from latitude and longitude to
  tile numbers, determining tile bounding boxes, and compositing tiles to a
  georeferenced raster image.
Provides statistical procedures for linear regression in the general context where the errors are assumed to be correlated. Different ways to estimate the asymptotic covariance matrix of the least squares estimators are available. Starting from this estimation of the covariance matrix, the confidence intervals and the usual tests on the parameters are modified. The functions of this package are very similar to those of 'lm': it contains methods such as summary(), plot(), confint() and predict(). The 'slm' package is described in the paper by E. Caron, J. Dedecker and B. Michel (2019), "Linear regression with stationary errors: the R package slm", arXiv preprint <arXiv:1906.06583>.
Stepwise models for the optimal linear combination of continuous variables in binary classification problems under Youden Index optimisation. Information on the models implemented can be found at Aznar-Gimeno et al. (2021) <doi:10.3390/math9192497>.
A collection of helper functions designed to help
    you to better understand object oriented programming in R,
    particularly using 'S3'.
Efficient implementations for Sorted L-One Penalized Estimation
    (SLOPE): generalized linear models regularized with the sorted L1-norm
    (Bogdan et al. (2015) <doi:10/gfgwzt>). Supported models include ordinary
    least-squares regression, binomial regression, multinomial regression, and
    Poisson regression. Both dense and sparse  predictor matrices are supported.
    In addition, the package features predictor screening rules that enable fast
    and efficient solutions to high-dimensional problems.
Optimal partitioning algorithm for change-in-slope problem with continuity constraint and a finite number of states. Some constraints can be enforced in the inference: isotonic, unimodal or smoothing. With the function slopeSN() (segment neighborhood) the number of segments to infer is fixed by the user and does not depend on a penalty value.
An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.
A mostly pure-R implementation of the RAKE algorithm (Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.
Interface for creation of 'slp' class smoother objects for 
             use in Generalized Additive Models (as implemented by packages 
             'gam' and 'mgcv').
Presidential Election data of "Sri Lanka"" is stored in Pdf
    files, through Pdf scraping they are converted into data-frames and
    stored in this R package.
Conduct latent trajectory class analysis with longitudinal data. Our method supports longitudinal continuous, binary and count data. For more methodological details, please refer to Hart, K.R., Fei, T. and Hanfelt, J.J. (2020), Scalable and robust latent trajectory class analysis using artificial likelihood. Biometrics <doi:10.1111/biom.13366>.
'Slurm', Simple Linux Utility for Resource Management
          <https://slurm.schedmd.com/>, is a popular 'Linux' based software used to 
          schedule jobs in 'HPC' (High Performance Computing) clusters. This R package
          provides a specialized lightweight wrapper of 'Slurm' with a syntax similar to
          that found in the 'parallel' R package. The package also includes a method for
          creating socket cluster objects spanning multiple nodes that can be used with
          the 'parallel' package.
This is software linked to the book
  'Applied Smoothing Techniques for Data Analysis -
  The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods. Tervonen, T. and Figueira,  J. R. (2008) <doi:10.1002/mcda.407>.
Implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well.
Statistical methods for analyzing case-control point data.  Methods include the ratio of kernel densities, the difference in K Functions, the spatial scan statistic, and q nearest neighbors of cases.
Inference techniques for Fay Herriot Model.
A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.
Data practitioners regularly use the 'R' and 'Python' programming languages to prepare
    data for analyses. Thus, they encode data preprocessing decisions in 'R' and 'Python' scripts.
    The 'smallsets' package subsequently decodes these decisions into a Smallset Timeline, a
    visualisation proposed in Lucchesi et al. (2022) <doi:10.1145/3531146.3533175>. A Smallset
    Timeline is a series of small data snapshots of different preprocessing steps. The 'smallsets'
    package builds this figure from a user's dataset and 'R'/'Python' preprocessing script, which
    contains structured comments with snapshot instructions.
Functions used in courses taught by Dr. Small at Drew University.
Animal movement models including moving-resting process
    with embedded Brownian motion according to
    Yan et al. (2014) <doi:10.1007/s10144-013-0428-8>,
    Pozdnyakov et al. (2017) <doi:10.1007/s11009-017-9547-6>,
    Brownian motion with measurement error according to
    Pozdnyakov et al. (2014) <doi:10.1890/13-0532.1>,
    and moving-resting-handling process with embedded Brownian motion,
    Pozdnyakov et al. (2018) <arXiv:1806.00849>.
Facilitates programmatic access to NASA Soil Moisture Active
    Passive (SMAP) data with R. It includes functions to search for, acquire,
    and extract SMAP data.
Primary data analysis for sequential multiple assignment randomization trial (SMART) and calibration tools for clinical trial planning purposes. \n 
             The methods used for this package include:  \n
             (1) Likelihood-based global test (hypothesis test, power calculation) by in Zhong X., Cheng, B., Qian M., Cheung Y.K. (2019) <doi: 10.1016/j.cct.2019.105830>. \n
             (2) IPWE-based global test (hypotehsis test, power calculation) by Ogbagaber S.B., Karp J., Wahed A.S. (2016) <doi:10.1002/sim.6747>. \n
             (3) G estimates (pairwise comparison, power calculation) by  Lavori R., Dawson P.W. (2012) <doi: 10.1093/biostatistics/kxr016>. \n
             (4) IPW estimates (pairwise comparison, power calculation) by Murphy S.A. (2005) <doi: 10.1002/sim.2022>. \n
             (5) SAMRT with adaptive randomization by Cheung Y.K. (2015) <doi: 10.1111/biom.12258>.
Permits determination of a set of 
    optimal dynamic treatment regimes and sample size for a SMART design 
    in the Bayesian setting with binary outcomes. Please see Artman (2020) <arXiv:2008.02341>.
SMART trial design, as described by He, J., McClish, D., Sabo, R. (2021)  <doi:10.1080/19466315.2021.1883472>, includes multiple stages of randomization, where participants are randomized to an initial treatment in the first stage and then subsequently re-randomized between treatments in the following stage.
Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.
Tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.
Preview spatial data as 'leaflet' maps with minimal
  effort. smartmap is optimized for interactive use and distinguishes itself 
  from similar packages because it does not need real spatial ('sp' or 'sf')
  objects an input; instead, it tries to automatically coerce everything that 
  looks like spatial data to sf objects or leaflet maps. It - for example -  
  supports direct mapping of: a vector containing a single coordinate pair,
  a two column matrix, a data.frame with longitude and latitude columns, or
  the path or URL to a (possibly compressed) 'shapefile'.
Methods for analysis of energy consumption data (electricity, gas, 
    water) at different data measurement intervals. The package provides feature extraction 
    methods and algorithms to prepare data for data mining and machine learning 
    applications. Deatiled descriptions of the methods and their application can be found 
    in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and 
    Energy Retailing" <doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9> 
    "Enhancing energy efficiency in the residential sector with smart meter data analytics".
Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+) <arXiv:1902.09386>.
A set of tools for determining the necessary sample size
    in order to identify the optimal dynamic treatment regime
    in a sequential, multiple assignment, randomized trial (SMART). 
    Utilizes multiple comparisons with the best methodology 
    to adjust for multiple comparisons.
    Designed for an arbitrary SMART design. Please see Artman (2018) <doi:10.1093/biostatistics/kxy064> for more details.
Fast computation of multivariate analyses of small (10s to 100s markers) to big (1000s to 100000s) genotype data. Runs Principal Component Analysis allowing for centering, z-score standardization and scaling for genetic drift, projection of ancient samples to modern genetic space and multivariate tests for differences in group location (Permutation-Based Multivariate Analysis of Variance) and dispersion (Permutation-Based Multivariate Analysis of Dispersion).
Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the 'irwsva.build' function of the 'sva' package and proposes a revision on it that achieves an order of magnitude faster running time while trading no accuracy loss in return.
Methods for fitting bivariate lines in
    allometry using the major axis (MA) or standardised major axis (SMA), and
    for making inferences about such lines. The available methods of inference
    include confidence intervals and one-sample tests for slope and elevation,
    testing for a common slope or elevation amongst several allometric lines,
    constructing a confidence interval for a common slope or elevation, and
    testing for no shift along a common axis, amongst several samples. 
    See Warton et al. 2012 <doi:10.1111/j.2041-210X.2011.00153.x> for methods description.
A set of functions to build a scoring model from beginning to end, leading the user
	to follow an efficient and organized development process, reducing significantly the time
	spent on data exploration, variable selection, feature engineering, binning and model selection 
	among other recurrent tasks. 
	The package also incorporates monotonic and customized binning, scaling capabilities that 
	transforms logistic coefficients into points for a better business understanding and 
	calculates and visualizes classic performance metrics of a classification model.
A canonical correlation based framework for constructing phenotype-specific multi-omics networks by integrating multiple omics data types and a quantitative phenotype of interest.
Implements multiple imputation of missing covariates by
    Substantive Model Compatible Fully Conditional Specification.
    This is a modification of the popular FCS/chained equations
    multiple imputation approach, and allows imputation of missing
    covariate values from models which are compatible with the user
    specified substantive model.
Data Sets for Kumar and Petersen (2012).
    Statistical Methods in Customer Relationship Management,
    Wiley: New York.
An R-package for Estimating Semiparametric PH and AFT Mixture Cure Models.
Computes standardized mean differences and confidence intervals for 
    multiple data types based on Yang, D., & Dalton, J. E. (2012) 
    <http://www.lerner.ccf.org/qhs/software/lib/stddiff.pdf>.
Contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.
A computing tool is developed to automated identify somatic mutation-driven immune cells. The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status..
Allows users to easily build custom 'docker images' <https://docs.docker.com/> from
    'Amazon Web Service Sagemaker' <https://aws.amazon.com/sagemaker/> using
    'Amazon Web Service CodeBuild' <https://aws.amazon.com/codebuild/>.
Implements statistical methods for analyzing the counts of areal data, with a focus on the detection of spatial clusters and clustering.  The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) <doi:10.1002/sim.4780140809> and Kulldorff (1997) <doi:10.1080/03610929708831995>.
Provides the filtering algorithms for the state space models on the Stiefel manifold as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel manifold.
Discovery of spatial patterns with Hidden Markov Random Field. This package is designed for spatial transcriptomic data and single molecule fluorescent in situ hybridization (FISH) data such as sequential fluorescence in situ hybridization (seqFISH) and multiplexed error-robust fluorescence in situ hybridization (MERFISH). The methods implemented in this package are described in Zhu et al. (2018) <doi:10.1038/nbt.4260>.
Code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second and third editions of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press.
Functions that provide statistical methods for interval-censored (grouped) data. The package supports the estimation of linear and linear mixed regression models with interval-censored dependent variables. Parameter estimates are obtained by a stochastic expectation maximization algorithm. Furthermore, the package enables the direct (without covariates) estimation of statistical indicators from interval-censored data via an iterative kernel density algorithm. Survey and Organisation for Economic Co-operation and Development (OECD) weights can be included into the direct estimation (see, Walter, P. (2019) <doi:10.17169/refubium-1621>).
Statistical models for specific coronavirus disease 2019 use cases at German local health authorities. All models of Statistical modelling for infectious disease management 'smidm' are part of the decision support toolkit in the 'EsteR' project. More information is published in Sonja Jäckle, Rieke Alpers, Lisa Kühne, Jakob Schumacher, Benjamin Geisler, Max Westphal "'EsteR' – A Digital Toolkit for COVID-19 Decision Support in Local Health Authorities" (2022) <doi:10.3233/SHTI220799> and Sonja Jäckle, Elias Röger, Volker Dicken, Benjamin Geisler, Jakob Schumacher, Max Westphal "A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions" (2021) <doi:10.3390/ijerph18179166>.
Provides functions to estimate, predict and interpolate areal
        data. For estimation and prediction we assume areal data is an average
        of an underlying continuous spatial process as in Moraga et
        al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020)
        <doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020)
        <doi:10.1093/biostatistics/kxy041>. The interpolation methodology is
        (mostly) based on Goodchild and Lam (1980, ISSN:01652273).
This tiny package contains one function smirnov() which
        calculates two scaled taxonomic coefficients, Txy (coefficient
        of similarity) and Txx (coefficient of originality). These two
        characteristics may be used for the analysis of similarities
        between any number of taxonomic groups, and also for assessing
        uniqueness of giving taxon. It is possible to use smirnov()
        output as a distance measure: convert it to distance by
        "as.dist(1 - smirnov(x))".
Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID).
  It allow sequence-space-time host and viral population data storage, indexation and querying.
Visualisation tools for 'SMITIDstruct' package.
  Allow to visualize host timeline, transmission tree, index diversities
  and variant graph using 'HTMLwidgets'. It mainly using 'D3JS' javascript framework.
Feature screening is a powerful tool in processing ultrahigh dimensional data. It attempts to screen out most irrelevant features in preparation for a more elaborate analysis. Xu and Chen (2014)<doi:10.1080/01621459.2013.879531> proposed an effective screening method SMLE, which naturally incorporates the joint effects among features in the screening process. This package provides an efficient implementation of SMLE-screening for high-dimensional linear, logistic, and Poisson models. The package also provides a function for conducting accurate post-screening feature selection based on an iterative hard-thresholding procedure and a user-specified selection criterion.
A pair of functions that allow for the generation and tracking of coordinate data clouds without a time dimension,
    primarily for use in super-resolution plant micro-tubule image segmentation.
Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.
Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. 
	For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. 
	Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and 
	Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. 
	Estimation and simulation of discrete-time k-th order Markov chains are also considered.
Efficient design matrix free procedure for solving a soft maximin problem for  large scale array-tensor structured models, see Lund, Mogensen and Hansen (2019) <arXiv:1805.02407>. Currently Lasso and SCAD penalized estimation is implemented.
Efficient procedure for solving the soft maximin problem for large scale heterogeneous data, see Lund, Mogensen and Hansen (2022) <doi:10.1111/sjos.12580>. Currently Lasso and SCAD penalized estimation is implemented. Note this package subsumes and replaces the SMMA package.
Performs parametric and non-parametric estimation and simulation 
    for multi-state discrete-time semi-Markov processes. For the parametric 
    estimation, several discrete distributions are considered for the sojourn 
    times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial.
    The non-parametric estimation concerns the sojourn time distributions, 
    where no assumptions are done on the shape of distributions. Moreover, the 
    estimation can be done on the basis of one or several sample paths, with or
    without censoring at the beginning or/and at the end of the sample paths. 
    Reliability indicators such as reliability, maintainability, availability, 
    BMP-failure rate, RG-failure rate, mean time to failure and mean time to 
    repair are available as well. The implemented methods are described in 
    Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, 
    Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and 
    Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>.
    Estimation and simulation of discrete-time k-th order Markov chains are 
    also considered.
In Switzerland, the landscape of municipalities is changing rapidly
  mainly due to mergers. The Swiss Municipal Data Merger Tool 
  automatically detects these mutations and maps municipalities over time, i.e. municipalities of an old state
  to municipalities of a new state. This functionality is helpful when working 
  with datasets that are based on different spatial references. The package's idea and use 
  case is discussed in the following article: <https://onlinelibrary.wiley.com/doi/full/10.1111/spsr.12487>.
Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions.
Fits flexible additive models to data on stream networks, taking account of flow-connectivity of the network.  Models are fitted using penalised least squares.
Fits a linear non-penalized phenotype (demographic) variables 
    and penalized groups of prognostic effect and predictive effect, by satisfying such
    hierarchy structures that if a predictive effect exists, its prognostic effect must 
    also exist. This package can deal with continuous, binomial or multinomial, and survival
    response variables, underlying the assumption of Gaussian, binomial (multinomial), and 
    Cox proportional hazard models, respectively. It is implemented by combining the iterative
    shrinkage-thresholding algorithm and the alternating direction method of multipliers
    algorithms. The main method is built in C++, and the complementary methods are
    written in R.
Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. 
    Sun Q., Li X., et al (2020) <doi:10.1038/s41467-019-14238-3>. 
    In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure.
Provides generators for a high number of both single- and multi-
    objective test functions which are frequently used for the benchmarking of
    (numerical) optimization algorithms. Moreover, it offers a set of convenient
    functions to generate, plot and work with objective functions.
Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting.
             The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>),
             Exponential Smoothing (Hyndman et al., 2008, <doi: 10.1007/978-3-540-71918-2>),
             SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>),
             Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi: 10.13140/RG.2.2.24986.29123>),
             Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi: 10.1080/00207543.2017.1380326>)
             and several simulation functions. It also allows dealing with intermittent demand based on the
             iETS framework (Svetunkov & Boylan, 2019, <doi: 10.13140/RG.2.2.35897.06242>).
The implemented method uses for smoothing bivariate thin plate
    splines, bivariate lasso-type regularization, and allows for both period
    and cohort effects. Thus the mortality rates are modelled as the sum of four
    components: a smooth bivariate function of age and time, smooth one-dimensional
    cohort effects, smooth one-dimensional period effects and random errors.
We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) <doi:10.1007/s10107-004-0552-5>), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) <doi:10.1101/2020.09.17.301788>). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates. A function to perform cross validation for selection of the regularization parameter is provided.
A collection of methods for smoothing numerical data, commencing with a port of the Matlab gaussian window smoothing function. In addition, several functions typically used in smoothing of financial data are included.
Estimation of two-state (survival) models and irreversible illness-
    death models with possibly interval-censored,left-truncated and right-censored
    data. Proportional intensities regression models can be specified to allow for
    covariates effects separately for each transition. We use either a parametric
    approach with Weibull baseline intensities or a semi-parametric approach with
    M-splines approximation of baseline intensities in order to obtain smooth
    estimates of the hazard functions. Parameter estimates are obtained by maximum
    likelihood in the parametric approach and by penalized maximum likelihood in the
    semi-parametric approach.
Provides flexible hazard ratio curves allowing non-linear
  relationships between continuous predictors and survival.
  To better understand the effects that each continuous covariate
  has on the outcome, results are ex pressed in terms of hazard
  ratio curves, taking a specific covariate value as reference.
  Confidence bands for these curves are also derived.
Implementation of the SIC epsilon-telescope method, either
    using single or distributional (multi-parameter) regression. Includes classical regression
    with normally distributed errors and robust regression, where the errors are from
    the Laplace distribution. The "smooth generalized normal distribution" is used,
    where the estimation of an additional shape parameter allows the user to move
    smoothly between both types of regression. See O'Neill and Burke (2022)
    "Robust Distributional Regression with Automatic Variable Selection" for more details.
    <arXiv:2212.07317>. This package also contains the data analyses from O'Neill and
    Burke (2023). "Variable selection using a smooth information criterion for distributional
    regression models". <doi:10.1007/s11222-023-10204-8>.
Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).
Some M-estimators for 1-dimensional location (Bisquare,
             ML for the Cauchy distribution, and the estimators from 
             application of the smoothing principle introduced in Hampel, 
             Hennig and Ronchetti (2011) to the above, the Huber
             M-estimator, and the median, main function is smoothm), 
             and Pitman estimator.
Tools for smoothing and tidying spatial features
    (i.e. lines and polygons) to make them more aesthetically pleasing.
    Smooth curves, fill holes, and remove small fragments from lines and
    polygons.
Computes smooth estimations for the Cumulative/Dynamic and Incident/Dynamic ROC curves, in presence of right censorship, based on the bivariate kernel density estimation of the joint distribution function of the Marker and Time-to-event variables.
Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0].
A list of methods for estimating a smooth tensor with an unknown permutation. It also contains several multi-variate functions for generating permuted signal tensors and corresponding observed tensors. For a detailed introduction for the model and estimation techniques, see the paper by Chanwoo Lee and Miaoyan Wang (2021) "Smooth tensor estimation with unknown permutations" <arXiv:2111.04681>.
The main function in the package utilizes a windowing function in the form of an exponential weighting function to linear models. The bandwidth and sharpness of the window are controlled by two parameters. Then, a series of tests are used to identify the right parameters of the window (see Hamed Haselimashhadi et al (2019) <https://www.biorxiv.org/content/10.1101/656678v1>).
The nonparametric trend and its derivatives in equidistant time 
    series (TS) with short-memory stationary errors can be estimated. The 
    estimation is conducted via local polynomial regression using an 
    automatically selected bandwidth obtained by a built-in iterative plug-in 
    algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel 
    smoother is also built-in as a comparison. With version 1.1.0, a linearity 
    test for the trend function, forecasting methods and backtesting 
    approaches are implemented as well.
    The smoothing methods of the package are described in Feng, Y., Gries, T., 
    and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.
A collection of various oversampling techniques developed from SMOTE is provided. SMOTE is a oversampling technique which synthesizes a new minority instance between a pair of one minority instance and one of its K nearest neighbor. (see <https://www.jair.org/media/953/live-953-2037-jair.pdf> for more information) Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.
Provides the SMOTE with Boosting (SMOTEWB) algorithm. See
      F. Sağlam, M. A. Cengiz (2022) <doi:10.1016/j.eswa.2022.117023>. Also 
      SMOTEWB provides faster versions of several resampling methods for 
      imbalanced datasets, including SMOTE with Boosting (SMOTEWB), ADASYN, 
      Borderline SMOTE (BLSMOTE), Random Over-Sampling (ROS), Random 
      Under-Sampling (RUS), Safe-Level SMOTE (SLSMOTE), Relocating Safe-Level 
      SMOTE (RSLSMOTE), and Random Over-Sampling Examples (ROSE).
Provides movies to help students to understand statistical 
  concepts.  The 'rpanel' package  <https://cran.r-project.org/package=rpanel> 
  is used to create interactive plots that move to illustrate key statistical 
  ideas and methods.  There are movies to: visualise probability distributions
  (including user-supplied ones); illustrate sampling distributions of the
  sample mean (central limit theorem), the median, the sample maximum 
  (extremal types theorem) and (the Fisher transformation of the) 
  product moment correlation coefficient; examine the influence of an 
  individual observation in simple linear regression; illustrate key concepts 
  in statistical hypothesis testing. Also provided are dpqr functions for the 
  distribution of the Fisher transformation of the correlation coefficient 
  under sampling from a bivariate normal distribution.
Creates images that are the proper size for social media. Beautiful
    plots, charts and graphs wither and die if they are not shared. Social media 
    is perfect for this but every platform has its own image dimensions. With 
    'smpic' you can easily save your plots with the exact dimensions needed for 
    the different platforms.
Contains the datasets and a few functions for use with 
        the practicals outlined in Appendix A of the book
        Statistical Models (Davison, 2003, Cambridge University Press).
        The practicals themselves can be found at
        <http://statwww.epfl.ch/davison/SM/>.
Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers.
Produce small area population estimates by fitting census data to
    survey data.
Provides estimations of the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) based on the two-stages mixed-subjects ROC curve estimator (Diaz-Coto et al. (2020) <doi:10.1515/ijb-2019-0097> and Diaz-Coto et al. (2020) <doi:10.1080/00949655.2020.1736071>).
Datasets used in "Statistical Methods for the Social Sciences"
    (SMSS) by Alan Agresti and Barbara Finlay.
Implements L0-constrained Multi-Task Learning and domain generalization algorithms. The algorithms are coded in Julia allowing for fast implementations of the coordinate descent and local combinatorial search algorithms. For more details, see a preprint of the paper: Loewinger et al., (2022) <arXiv:2212.08697>.
Implementation of the SMuRF algorithm of Devriendt et al. (2021) <doi:10.1016/j.insmatheco.2020.11.010> to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood.
Testing the mediation effect of multiple SNPs on an outcome through a mediator.
Implementation of the structural model for variances in order to
        detect differentially expressed genes from gene expression data.
Mosaic diagram, scatterplot matrix, Andrews curves, parallel coordinate diagram, radar diagram, 
   and Chernoff plots as a Shiny app, which allow the order of variables to be changed interactively. 
   The apps are intended as teaching examples.
Build and manipulate probability distributions of the skew-normal 
  family and some related ones, notably the skew-t and the SUN families. 
  For the skew-normal and the skew-t distributions, statistical methods are 
  provided for data fitting and model diagnostics, in the univariate and the 
  multivariate case.
A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.
'RStudio' addin which provides a GUI to visualize and analyse networks. 
    After finishing a session, the code to produce the plot is inserted in the current script.
    Alternatively, the function SNAhelperGadget() can be used directly from the console.
    Additional addins include the Netreader() for reading network files, Netbuilder() to create
    small networks via point and click, and the Componentlayouter() to layout networks with many components manually.
Implements snake in R as a programming example, see <https://en.wikipedia.org/wiki/Snake_(video_game_genre)>.
A consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.
Plays the game of Snakes and Ladders and has tools for analyses. The tools included allow you to find the average moves to win, frequency of each square, importance of the snakes and the ladders, the most common square and the plotting of the game played.
A simple wrapper to easily design vanilla deep neural networks using 'Tensorflow'/'Keras' backend for regression, classification and multi-label tasks, with some tweaks and tricks (skip shortcuts, embedding, feature selection and anomaly detection).
Collect  your data on digital marketing campaigns from Snapchat Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Geostatistical modeling and kriging with
    gridded data using spatially separable covariance functions (Kronecker
    covariances). Kronecker products in these models provide shortcuts for
    solving large matrix problems in likelihood and conditional mean,
    making 'snapKrig' computationally efficient with large grids. The package
    supplies its own S3 grid object class, and a host of methods including
    plot, print, Ops, square bracket replace/assign, and more. Our computational
    methods are described in Koch, Lele, Lewis (2020) <doi:10.7939/r3-g6qb-bq70>.
Functions for reading and writing Gadget N-body snapshots. The Gadget code is popular in astronomy for running N-body / hydrodynamical cosmological and merger simulations. To find out more about Gadget see the main distribution page at www.mpa-garching.mpg.de/gadget/
Efficient estimation of multivariate skew-normal distribution in closed form.
Fitting of non-parametric production frontiers for use in efficiency analysis.
    Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a 
    non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for 
    multiple inputs and a single output using constrained kernel smoothing as in 
    Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints 
    on the estimated frontier.
Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.
Create correlation networks using St. Nicolas House Analysis ('SNHA'). 
    The package can be used for visualizing multivariate data similar to Principal
    Component Analysis or Multidimensional Scaling using a ranking approach.
    In contrast to 'MDS' and 'PCA', 'SNHA' uses a network approach to explore
    interacting variables. 
    For details see 'Hermanussen et. al. 2021', <doi:10.3390/ijerph18041741>.
Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. This package also provides functions for computing the classification error and classification instability of a classification procedure.
Programmatic interface to the 'SNOTEL' snow data
  (<https://www.nrcs.usda.gov/wps/portal/wcc/home>). Provides easy downloads of snow 
  data into your R work space or a local directory. Additional post-processing 
  routines to extract snow season indexes are provided.
Support for simple parallel computing in R.
An R interface to the C 'libstemmer' library that implements
  Porter's word stemming algorithm for collapsing words to a common
  root to aid comparison of vocabulary. Currently supported languages are
  Arabic, Basque, Catalan, Danish, Dutch, English, Finnish, French, German, Greek,
  Hindi, Hungarian, Indonesian, Irish, Italian, Lithuanian, Nepali,
  Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Tamil
  and Turkish.
Functions for analysis of network objects, which are imported or simulated by the package. The non-parametric methods of analysis center on snowball and bootstrap sampling for estimating functions of network degree distribution. For other parameters of interest, see, e.g., 'bootnet' package.
Usability wrapper around snow for easier development of
        parallel R programs. This package offers e.g. extended error
        checks, and additional functions. All functions work in
        sequential mode, too, if no cluster is present or wished.
        Package is also designed as connector to the cluster management
        tool sfCluster, but can also used without it.
The function generates and plots random snowflakes. Each snowflake is defined by a given diameter, width of the crystal, color, and random seed. Snowflakes are plotted in such way that they always remain round, no matter what the aspect ratio of the plot is. Snowflakes can be created using transparent colors, which creates a more interesting, somewhat realistic, image. Images of the snowflakes can be separately saved as svg files and used in websites as static or animated images.
Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed. Dynamic cluster size is also available.
To investigate the functional characteristics of selected SNPs and their vicinity genomic region. Linked SNPs in moderate to high linkage disequilibrium (e.g. r2>0.50) with the corresponding index SNPs will be selected for further analysis.
Functions to perform most of the common analysis in genome 
    association studies are implemented. These analyses include descriptive 
    statistics and exploratory analysis of missing values, calculation of 
    Hardy-Weinberg equilibrium, analysis of association based on generalized
    linear models (either for quantitative or binary traits), and analysis
    of multiple SNPs (haplotype and epistasis analysis). Permutation test 
    and related tests (sum statistic and truncated product) are also 
    implemented. Max-statistic and genetic risk-allele score exact 
    distributions are also possible to be estimated. The methods are 
    described in Gonzalez JR et al., 2007 <doi: 10.1093/bioinformatics/btm025>.
Is designed to interactively and reproducibly visualize and filter SNP
	(single-nucleotide polymorphism) datasets. This R-based implementation of SNP
	and genotype filters facilitates 
	an interactive and iterative SNP filtering pipeline, which can be documented
	reproducibly via Rmarkdown. 'SNPfiltR' contains functions for visualizing 
	various quality and missing data metrics for a SNP dataset, and then filtering
	the dataset based on user specified cutoffs.
	All functions take 'vcfR' objects as input, which can easily be
	generated by reading standard vcf (variant call format) files into R using 
	the R package 'vcfR' (Knaus and Grünwald) (<doi:10.1111/1755-0998.12549>). 
	Each 'SNPfiltR' function can return a newly filtered vcfR object, which can then be
	written to a local directory in standard vcf format using the 'vcfR' package,
	for downstream population genetic and phylogenetic analyses.
Generate knockoffs for genetic data and hidden Markov models.
  For more information, see the website below and the accompanying papers: 
  "Gene hunting with hidden Markov model knockoffs", Sesia et al., Biometrika, 2019, (<doi:10.1093/biomet/asy033>).
  "Multi-resolution localization of causal variants across the genome", Sesia et al., bioRxiv, 2019, (<doi:10.1101/631390>).
Linkage disequilibrium visualizations of up to several hundreds of single nucleotide polymorphisms (SNPs), annotated with chromosomic positions and gene names. Two types of plots are available for small numbers of SNPs (<40) and for large numbers (tested up to 500). Both can be extended by combining other ggplots, e.g. association studies results, and functions enable to directly visualize the effect of SNP selection methods, as minor allele frequency filtering and TagSNP selection, with a second correlation heatmap. The SNPs correlations are computed on Genotype Data objects from the 'GWASTools' package using the 'SNPRelate' package, and the plots are customizable 'ggplot2' and 'gtable' objects and are annotated using the 'biomaRt' package. Usage is detailed in the vignette with example data and results from up to 500 SNPs of 1,200 scans are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>.
A set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example to export to a PLINK set.
Tools for performing variable selection in three-way data using N-PLS 
    in combination with L1 penalization, Selectivity Ratio and VIP scores. 
    The N-PLS model (Rasmus Bro, 1996 <DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) 
    is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries 
    to maximize the covariance between X and Y data arrays. The package also adds
    variable selection through L1 penalization, Selectivity Ratio and VIP scores.
Three functions to clean, summarize and prepare genomic datasets to Genome
    Selection and Genome Association analysis and to estimate population genetic parameters.
The goal of 'snpsettest' is to provide simple tools that perform
             set-based association tests (e.g., gene-based association tests)
             using GWAS (genome-wide association study) summary statistics. A
             set-based association test in this package is based on the
             statistical model described in VEGAS (versatile gene-based
             association study), which combines the effects of a set of SNPs
             accounting for linkage disequilibrium between markers. This package
             uses a different approach from the original VEGAS implementation to
             compute set-level p values more efficiently, as described in
             <https://github.com/HimesGroup/snpsettest/wiki/Statistical-test-in-snpsettest>.
Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided. Note: Formerly available versions of the MfUSampler can be obtained from the archive <https://cran.r-project.org/src/contrib/Archive/MfUSampler/>.
Scan statistics applied in social network data can be used to test the cluster characteristics among a social network.
The methods discussed in this package are new non-parametric methods
    based on sequential normal scores 'SNS' (Conover et al (2017)
    <doi:10.1080/07474946.2017.1360091>), designed for sequences of observations,
    usually time series data, which may occur singly or in batches,
    and may be univariate or multivariate. These methods are designed
    to detect changes in the process, which may occur as changes in location
    (mean or median), changes in scale (standard deviation, or variance), or
    other changes of interest in the distribution of the observations,
    over the time observed. They usually apply to large data sets,
    so computations need to be simple enough to be done in a reasonable
    time on a computer, and easily updated as each new observation
    (or batch of observations) becomes available. Some examples and more detail
    in 'SNS' is presented in the work by Conover et al (2019) <arXiv:1901.04443>.
Contains functions to perform various models and
    methods for test equating. It currently implements the traditional
    mean, linear and equipercentile equating methods. Both IRT observed-score 
    and true-score equating are also supported, as well as the mean-mean, 
    mean-sigma, Haebara and Stocking-Lord IRT linking methods.
    It also supports newest methods such that local equating, kernel
    equating (using Gaussian, logistic, Epanechnikov, uniform and adaptive 
    kernels) with presmoothing, and IRT parameter linking methods based on 
    asymmetric item characteristic functions. Functions to obtain both 
    standard error of equating (SEE) and standard error of equating differences 
    between two equating functions (SEED) are also implemented for the kernel method of
    equating.
Consolidated data simulation, sample size calculation and
    analysis functions for several snSMART (small sample sequential,
    multiple assignment, randomized trial) designs under one library.  See
    Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian
    analysis of small n sequential multiple assignment randomized trials
    (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732
    <doi:10.1002/sim.7900>.
Easily calculate precession and obliquity from an orbital solution (defaults to ZB18a from Zeebe and Lourens (2019) <doi:10.1126/science.aax0612>) and assumed or reconstructed values for tidal dissipation (Td) and dynamical ellipticity (Ed). This is a translation and adaptation of the C-code in the supplementary material to Zeebe and Lourens (2022) <doi:10.1029/2021PA004349>, with further details on the methodology described in Zeebe (2022) <doi:10.3847/1538-3881/ac80f8>. The name of the C-routine is snvec, which refers to the key units of computation: spin vector s and orbit normal vector n.
Allows objects to be stored on disc and automatically
       		recalled into memory, as required, by delayed assignment.
Creates stratum orthogonal arrays (also known as strong orthogonal arrays). These are arrays with more levels per column than the typical orthogonal array, and whose low order projections behave like orthogonal arrays, when collapsing levels to coarser strata. Details are described in Groemping (2022) "A unifying implementation of stratum (aka strong) orthogonal arrays" <http://www1.bht-berlin.de/FB_II/reports/Report-2022-002.pdf>.
Algorithm to estimate the Sobol indices using a non-parametric fit of the regression curve. The bandwidth is estimated using bootstrap to reduce the finite-sample bias. The package is based on the paper Solís, M. (2018) <arXiv:1803.03333>.
R implementation of S. Joe and F. Y. Kuo(2008)
        <DOI:10.1137/070709359>.
        The implementation is based on the data file new-joe-kuo-6.21201
        <http://web.maths.unsw.edu.au/~fkuo/sobol/>.
Specific and class specific multiple correspondence analysis on
    survey-like data. Soc.ca is optimized to the needs of the social scientist and
    presents easily interpretable results in near publication ready quality.
Functions for evaluating tournament predictions, simulating results from individual soccer matches and tournaments. See <http://sandsynligvis.dk/2018/08/03/world-cup-prediction-winners/> for more information.
A set of functions to quantify and visualise 
    social autocorrelation.
Tools developed to facilitate the establishment of the rank and social hierarchy 
             for gregarious animals by the Si method developed by 
             Kondo & Hurnik (1990)<doi:10.1016/0168-1591(90)90125-W>. It is
             also possible to determine the number of agonistic interactions between two 
             individuals, sociometric and dyadics matrix from dataset obtained through 
             electronic bins. In addition, it is possible plotting the results using a bar plot, box plot, and sociogram.
Provides methods for sampling contact matrices from diary
    data for use in infectious disease modelling, as discussed in Mossong
    et al. (2008) <doi:10.1371/journal.pmed.0050074>.
Provides to sociologists (and related scientists) a toolbox to facilitate the construction of social position indicators from survey data. Social position indicators refer to what is commonly known as social class and social status. There exists in the sociological literature many theoretical conceptualisation and empirical operationalization of social class and social status. This first version of the package offers tools to construct the International Socio-Economic Index of Occupational Status (ISEI) and the Oesch social class schema. It also provides tools to convert several occupational classifications (PCS82, PCS03, and ISCO08) into a common one (ISCO88) to facilitate data harmonisation work, and tools to collapse (i.e. group) modalities of social position indicators.
The notion of power index has been widely used in literature to evaluate the influence of individual players (e.g., voters, political parties, nations, stockholders, etc.) involved in a collective decision situation like an electoral system, a parliament, a council, a management board, etc., where players may form coalitions. Traditionally this ranking is determined through numerical evaluation. More often than not however only ordinal data between coalitions is known. The package 'socialranking' offers a set of solutions to rank players based on a transitive ranking between coalitions, including through CP-Majority, ordinal Banzhaf or lexicographic excellence solution summarized by Tahar Allouche, Bruno Escoffier, Stefano Moretti and Meltem Öztürk (2020, <doi:10.24963/ijcai.2020/3>).
Social risks are increasingly becoming a critical component of health
    care research. One of the most common ways to identify social needs is by using
    ICD-10-CM "Z-codes." This package identifies social risks using varying taxonomies
    of ICD-10-CM Z-codes from administrative health care data. The conceptual
    taxonomies come from:
    Centers for Medicare and Medicaid Services (2021) <https://www.cms.gov/files/document/zcodes-infographic.pdf>,
    Reidhead (2018) <https://web.mhanet.com/>,
    A Arons, S DeSilvey, C Fichtenberg, L Gottlieb (2018) <https://sirenetwork.ucsf.edu/tools-resources/resources/compendium-medical-terminology-codes-social-risk-factors>.
Accesses raw data via API and calculates social
    determinants of health measures for user-specified locations in the
    US, returning them in tidyverse- and sf-compatible data frames.
Supporting materials for a course and book on data visualization. It contains utility functions for graphs and several sample data sets. See Healy (2019) <ISBN 978-0691181622>.
Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.
Bindings to 'libsodium' <https://doc.libsodium.org/>: a modern, 
    easy-to-use software library for encryption, decryption, signatures, password
    hashing and more. Sodium uses curve25519, a state-of-the-art Diffie-Hellman 
    function by Daniel Bernstein, which has become very popular after it was 
    discovered that the NSA had backdoored Dual EC DRBG.
Provides an interface to the 'NoSQL' database 'CouchDB'
    (<http://couchdb.apache.org>). Methods are provided for managing
    databases within 'CouchDB', including creating/deleting/updating/transferring,
    and managing documents within databases. One can connect with a local
    'CouchDB' instance, or a remote 'CouchDB' databases such as 'Cloudant'. 
    Documents can be inserted directly from vectors, lists, data.frames, 
    and 'JSON'. Targeted at 'CouchDB' v2 or greater.
Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.
Software that leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. Circos is a novel software that addresses the challenges in visualizing genetic data by creating circular ideograms composed of tracks of heatmaps, scatter plots, line plots, histograms, links between common markers, glyphs, text, and etc. Please see <http://www.circos.ca>.
Implements the SoftBart model of described by Linero and Yang (2018) <doi:10.1111/rssb.12293>, with the optional use of a sparsity-inducing prior to allow for variable selection. For usability, the package maintains the same style as the 'BayesTree' package.
Detect libraries used in a project and automatically create software bibliographies in 'PDF', 'Word', 'Rmarkdown', and 'BibTeX' formats.
An extension of sensitivity, specificity, positive and negative
    predictive value to continuous predicted and reference memberships in
    [0, 1].
It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.
Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an 'EM' flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components).
'SOHPIE' (pronounced as SOFIE) is a novel pseudo-value regression approach for differential co-abundance network analysis of microbiome data, which can include additional clinical covariate in the model. The full methodological details can be found in Ahn S and Datta S (2023) <arXiv:2303.13702v1>.
Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+).
Soil assessment builds information for improved decision in
    soil management. It analyzes soil conditions with regard to
    agriculture crop suitability requirements [such as those given by FAO
    <https://www.fao.org/land-water/databases-and-software/crop-information/en/>]
    soil fertility classes, soil erosion, and soil salinity classification
    [<doi:10.1002/ldr.4211>]. Suitability requirements are for
    crops grouped into cereal crops, nuts, legumes, fruits, vegetables,
    industrial crops, and root crops.
Facilitates basic and equation-based analyses of some important soil properties related to soil chemical environment and nutrient availability to plants. Freundlich H (1907). <doi:10.1515/zpch-1907-5723>. Datta SP, Bhadoria PBS (1999). <doi:10.1002%2F%28SICI%291522-2624%28199903%29162%3A2%3C183%3A%3AAID-JPLN183%3E3.0.CO%3B2-A>."Boron adsorption and desorption in some acid soils of West Bengal, India". Langmuir I (1918). <doi:10.1021/ja02242a004> "The adsorption of gases on plane surfaces of glass, mica, and platinum". Khasawneh FE (1971). <doi:10.2136/sssaj1971.03615995003500030029x> "Solution ion activity and plant growth".
A collection of functions for reading soil data from U.S. Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and National Cooperative Soil Survey (NCSS) databases.
Analyzing soil food webs or any food web measured at equilibrium. The package
  calculates carbon and nitrogen fluxes and stability properties using methods
  described by Hunt et al. (1987) <doi:10.1007/BF00260580>, de Ruiter et al. 
  (1995) <doi:10.1126/science.269.5228.1257>, Holtkamp et al. (2011)
  <doi:10.1016/j.soilbio.2010.10.004>, and Buchkowski and Lindo (2021) 
  <doi:10.1111/1365-2435.13706>. The package can also manipulate the structure 
  of the food web as well as simulate food webs away from equilibrium and run 
  decomposition experiments.
Provides functions for (1) soil water retention (SWC) and unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (vGM or vG) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc) [8]), (2) fitting of parameter for SWC  and/or Ku using Shuffled Complex Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties (Ku and soil water contents) based on the simplified evaporation method (SEM) [6, 7].
    Main references:
    [1] van Genuchten (1980) <doi:10.2136/sssaj1980.03615995004400050002x>, 
    [2] Mualem (1976) <doi:10.1029/WR012i003p00513>, 
    [3] Peters (2013) <doi:10.1002/wrcr.20548>, 
    [4] Iden and Durner (2013) <doi:10.1002/2014WR015937>, 
    [5] Peters (2014) <doi:10.1002/2014WR015937>,
    [6] Wind G. P. (1966),
    [7] Peters and Durner (2008) <doi:10.1016/j.jhydrol.2008.04.016> and
    [8] Brooks and Corey (1964).
Provides functions for efficiently estimating properties of the Van Genuchten-Mualem model for soil hydraulic parameters from possibly sparse soil water retention and hydraulic conductivity data by multi-response parameter estimation methods (Stewart, W.E., Caracotsios, M. Soerensen, J.P. (1992) "Parameter estimation from multi-response data" <doi:10.1002/aic.690380502>). Parameter estimation is simplified by exploiting the fact that residual and saturated water contents and saturated conductivity are conditionally linear parameters (Bates, D. M.  and Watts, D. G. (1988) "Nonlinear Regression Analysis and Its Applications" <doi:10.1002/9780470316757>). Estimated parameters are optionally constrained by the evaporation characteristic length (Lehmann, P., Bickel, S., Wei, Z. and Or, D. (2020) "Physical Constraints for Improved Soil Hydraulic Parameter Estimation by Pedotransfer Functions" <doi:10.1029/2019WR025963>) to ensure that the estimated parameters are physically valid. Common S3 methods and further utility functions allow to process, explore and visualise estimation results.
Basic and model-based soil physical analyses.
Functions for modeling Soil Organic Matter decomposition
        in terrestrial ecosystems with linear and nonlinear models.
Taxonomic dictionaries, formative element lists, and functions related to the maintenance, development and application of U.S. Soil Taxonomy. 
   Data and functionality are based on official U.S. Department of Agriculture sources including the latest edition of the Keys to Soil Taxonomy. Descriptions and metadata are obtained from the National Soil Information System or Soil Survey Geographic databases. Other sources are referenced in the data documentation. 
   Provides tools for understanding and interacting with concepts in the U.S. Soil Taxonomic System. Most of the current utilities are for working with taxonomic concepts at the "higher" taxonomic levels: Order, Suborder, Great Group, and Subgroup.
A compilation of functions designed to assist users on the correlation analysis of crop yield and soil test values. Functions to estimate crop response patterns to soil nutrient availability and critical soil test values using various approaches such as: 1) the modified arcsine-log calibration curve (Correndo et al. (2017) <doi:10.1071/CP16444>); 2) the graphical Cate-Nelson quadrants analysis (Cate & Nelson (1965)), 3) the statistical Cate-Nelson quadrants analysis (Cate & Nelson (1971) <doi:10.2136/sssaj1971.03615995003500040048x>), 4) the linear-plateau regression (Anderson & Nelson (1975) <doi:10.2307/2529422>), 5) the quadratic-plateau regression (Bullock & Bullock (1994) <doi:10.2134/agronj1994.00021962008600010033x>), and 6) the Mitscherlich-type exponential regression (Melsted & Peck (1977) <doi:10.2134/asaspecpub29.c1>). The package development stemmed from ongoing work with the Fertilizer Recommendation Support Tool (FRST) and Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) projects.
Testing of soil for the contents of organic carbon, and available macro- and micro-nutrients is a crucial part of soil fertility assessment. This package computes some routinely tested soil properties viz. organic carbon (C), total nitrogen (N), available N, mineral N, available phosphorus (P), available potassium (K), available iron (Fe), available zinc (Zn), available manganese (Mn), available copper (Cu), and available nickel (Ni) in soil based on laboratory analysis data obtained by most commonly followed protocols. Besides, it can also draw standard curves based on absorption/emission vs. concentration data, and give out unknown concentrations from absorption/emission readings.
"The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui().
It implements parametric formulas of soil
    water retention or conductivity curve. At the moment, only Van Genuchten
    (for soil water retention curve) and Mualem (for hydraulic conductivity)
    were implemented. 
    See reference (<http://en.wikipedia.org/wiki/Water_retention_curve>).
Stores objects (e.g. neural networks) that are needed for
    using Sojourn accelerometer methods. For more information, see
    Lyden K, Keadle S, Staudenmayer J, & Freedson P (2014)
    <doi:10.1249/MSS.0b013e3182a42a2d>, Ellingson LD, Schwabacher IJ,
    Kim Y, Welk GJ, & Cook DB (2016) <doi:10.1249/MSS.0000000000000915>,
    and Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018)
    <doi:10.1249/MSS.0000000000001486>.
Interactively play a game of sokoban ,which has nine game levels.Sokoban is a type of transport puzzle, in which the player pushes boxes or crates around in a warehouse, trying to get them to storage locations.
Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.
Calculation of solar zenith and azimuth angles.
Provide utilities to work with solar time, 
  i.e. where noon is exactly when sun culminates.
  Provides functions for computing sun position and times of sunrise and sunset.
Isolation forest is anomaly detection method introduced by the paper Isolation based Anomaly Detection (Liu, Ting and Zhou <doi:10.1145/2133360.2133363>).
For surface energy models and estimation of solar positions and components with varying topography, time and locations. The functions calculate solar top-of-atmosphere, open, diffuse and direct components, atmospheric transmittance and diffuse factors, day length, sunrise and sunset, solar azimuth, zenith, altitude, incidence, and hour angles, earth declination angle, equation of time, and solar constant. Details about the methods and equations are explained in Seyednasrollah, Bijan, Mukesh Kumar, and Timothy E. Link. 'On the role of vegetation density on net snow cover radiation at the forest floor.' Journal of Geophysical Research: Atmospheres 118.15 (2013): 8359-8374, <doi:10.1002/jgrd.50575>.
Provides a set of functions for querying and parsing data
    from 'Solr' (<https://solr.apache.org/>) 'endpoints' (local and
    remote), including search, 'faceting', 'highlighting', 'stats', and
    'more like this'. In addition, some functionality is included for
    creating, deleting, and updating documents in a 'Solr' 'database'.
R language bindings for SolveBio's API.
    SolveBio is a biomedical knowledge hub that enables life science
    organizations to collect and harmonize the complex, disparate
    "multi-omic" data essential for today's R&D and BI needs.
    For more information, visit <https://www.solvebio.com>.
Universal and robust algorithm for solving the total alkalinity-pH equation presented in G. Munhoven (2013) <doi:10.5194/gmd-6-1367-2013> and G. Munhoven (2021) <doi:10.5194/gmd-2020-447>. The total alkalinity-pH equation relates total alkalinity and pH for a given set of acid-base concentrations in a given water sample, among which carbonic acid. This package is particularly useful in marine chemistry involving dissolved inorganic carbon. Original package in Fortran can be found at <doi:10.5281/zenodo.4328965>.
Self-Organizing Map (with application in gene clustering).
A topological version of k-NN: An abstract model is build
             as 2-dimensional self-organising map. Samples of unknown
             class are predicted by mapping them on the SOM and analysing
             class membership of neurons in the neighbourhood.
An R implementation of the Self-Organising Migrating Algorithm, a general-purpose, stochastic optimisation algorithm. The approach is similar to that of genetic algorithms, although it is based on the idea of a series of ``migrations'' by a fixed set of individuals, rather than the development of successive generations. It can be applied to any cost-minimisation problem with a bounded parameter space, and is robust to local minima.
Load and export 'SomaScan' data via the
    'SomaLogic Operating Co., Inc.' proprietary text file
    called an ADAT ('*.adat'). For file format see
    <https://github.com/SomaLogic/SomaLogic-Data/blob/master/README.md>.
    The package also exports auxiliary functions for
    manipulating, wrangling, and extracting relevant
    information from an ADAT object once in memory.
The stochastic (also called on-line) version of the Self-Organising
             Map (SOM) algorithm is provided. Different versions of the
             algorithm are implemented, for numeric and relational data and for
             contingency tables as described, respectively, in Kohonen (2001)
             <isbn:3-540-67921-9>, Olteanu & Villa-Vialaneix (2005)
             <doi:10.1016/j.neucom.2013.11.047> and Cottrell et al (2004)
             <doi:10.1016/j.neunet.2004.07.010>. The package also contains many
             plotting features (to help the user interpret the results), can 
             handle (and impute) missing values and is delivered with a 
             graphical user interface based on 'shiny'.
It's a collection of functions for Multiplicity Correction and Multiple Testing.
Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on 'kohonen' and 'openair' packages implemented by functions embedding Vesanto et al. (2001) <http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf>  heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) <doi:10.4209/aaqr.2019.08.0414> are provided.
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.
Application of the Self-Organizing Maps technique for spatial classification of time series. The package uses spatial data, point or gridded, to create clusters with similar characteristics. The clusters can be further refined to a smaller number of regions by hierarchical clustering and their spatial dependencies can be presented as complex networks. Thus, meaningful maps can be created, representing the regional heterogeneity of a single variable. More information and an example of implementation can be found in Markonis and Strnad (2020, <doi:10.1177/0959683620913924>).
Formulas for calculating sound velocity, water pressure, depth, 
    density, absorption and sonar equations.
Simulates the cultural evolution of quantitative traits of bird song. 'SongEvo' is an individual- (agent-) based model. 'SongEvo' is spatially-explicit and can be parameterized with, and tested against, measured song data. Functions are available for model implementation, sensitivity analyses, parameter optimization, model validation, and hypothesis testing.
Estimate the abundance of cell clones from the
	     distribution of lengths of DNA fragments (as created by
	     sonication, whence `sonicLength').  The algorithm in
	     "Estimating abundances of retroviral insertion sites from
	     DNA fragment length data" by Berry CC, Gillet NA, Melamed
	     A, Gormley N, Bangham CR, Bushman FD. Bioinformatics;
	     2012 Mar 15;28(6):755-62 is implemented.  The
	     experimental setting and estimation details are described
	     in detail there. Briefly, integration of new DNA in a
	     host genome (due to retroviral infection or gene therapy)
	     can be tracked using DNA sequencing, potentially allowing
	     characterization of the abundance of individual cell
	     clones bearing distinct integration sites. The locations
	     of integration sites can be determined by fragmenting the
	     host DNA (via sonication or fragmentase), breaking the
	     newly integrated DNA at a known sequence, amplifying the
	     fragments containing both host and integrated DNA,
	     sequencing those amplicons, then mapping the host
	     sequences to positions on the reference genome. The
	     relative number of fragments containing a given position
	     in the host genome estimates the relative abundance of
	     cells hosting the corresponding integration site, but
	     that number is not available and the count of amplicons
	     per fragment varies widely.  However, the expected number
	     of distinct fragment lengths is a function of the
	     abundance of cells hosting an integration site at a given
	     position and a certain nuisance parameter. The algorithm
	     implicitly estimates that function to estimate the
	     relative abundance.
Provides basic tools for manipulating sound files for bioacoustic analysis, and preparing analyses these for publication. The package validates that values are physically possible wherever feasible.
Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired.
Generalised additive P-spline regression models estimation using the separation of overlapping precision matrices (SOP) method. Estimation is based on the equivalence between P-splines and linear mixed models, and variance/smoothing parameters are estimated based on restricted maximum likelihood (REML). The package enables users to estimate P-spline models with overlapping penalties. Based on the work described in Rodriguez-Alvarez et al. (2015) <doi:10.1007/s11222-014-9464-2>; Rodriguez-Alvarez et al. (2019) <doi:10.1007/s11222-018-9818-2>, and Eilers and Marx (1996) <doi:10.1214/ss/1038425655>.
The sparse online principal component can not only process the online data set, but also obtain a sparse solution of the online data set. The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.
Provides functions to non-parametrically estimate the off-pulse interval of a source
    function originating from a pulsar. The technique is based on a sequential application of P-values
    obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov,
    Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.
Computes sequential A-, MV-, D- and E-optimal or near-optimal block and 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 possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly.
Used to construct the URLs and parameters of 'Socrata Open Data API' <https://dev.socrata.com> calls, using the API's 'SoQL' parameter format. Has method-chained and sensical syntax. Plays well with pipes.
Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.  The first regression is a logistic regression, which uses a known ratio (the probability of being sampled given that the subject/observation was referred divided by the probability of being sampled given that the subject/observation was no referred) as an offset to estimate the probability of being referred given outcome and covariates.  The second regression uses this estimated probability to calculate the mean population response given covariates.
Can be used to model the fate of soil organic carbon and soil organic nitrogen and to calculate N mineralisation rates. Provides a framework that numerically solves differential equations of soil organic carbon models based on first-order kinetics and extends these models to include the nitrogen component. The name 'sorcering' is an acronym for 'Soil ORganic Carbon & CN Ratio drIven Nitrogen modellinG framework'.
A Bayesian semiparametric Dirichlet process mixtures to estimate correlated receiver operating characteristic (ROC) surfaces and the associated volume under the surface (VUS) with stochastic order constraints. The reference paper is:Zhen Chen, Beom Seuk Hwang, (2018) "A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints". Biometrics, 75, 539-550. <doi:10.1111/biom.12997>.
Provides tools to efficiently analyze and visualize laboratory data from aqueous static adsorption experiments. The package provides functions to plot Langmuir, Freundlich, and Temkin isotherms and functions to determine the statistical conformity of data points to the Langmuir, Freundlich, and Temkin adsorption models through statistical characterization of the isothermic least squares regressions lines. Scientific Reference: Dada, A.O, Olalekan, A., Olatunya, A. (2012) <doi:10.9790/5736-0313845>.
Enables drag-and-drop behaviour in Shiny apps, by exposing the 
    functionality of the 'SortableJS' <https://sortablejs.github.io/Sortable/> 
    JavaScript library as an 'htmlwidget'. 
    You can use this in Shiny apps and widgets, 'learnr' tutorials as well as 
    R Markdown. In addition, provides a custom 'learnr' question type - 
    'question_rank()' - that allows ranking questions with drag-and-drop.
Implements the estimation and inference methods for sorted causal effects and 
    classification analysis as in Chernozhukov, Fernandez-Val and Luo (2018) <doi:10.3982/ECTA14415>.
Misc support functions for rOpenGov and open data downloads.
Search contributed R packages, sort by package.
Access statistical information on welfare and health in Finland 
    from the Sotkanet open data portal <https://sotkanet.fi/sotkanet/fi/index>.
The President of the United States is constitutionally obligated to provide
  a report known as the 'State of the Union'. The report summarizes the current challenges
  facing the country and the president's upcoming legislative agenda. While historically
  the State of the Union was often a written document, in recent decades it has always
  taken the form of an oral address to a joint session of the United States Congress.
  This package provides the raw text from every such address with the intention of
  being used for meaningful examples of text analysis in R. The corpus is well suited
  to the task as it is historically important, includes material intended to be read
  and material intended to be spoken, and it falls in the public domain. As the corpus
  spans over two centuries it is also a good test of how well various methods hold up
  to the idiosyncrasies of historical texts. Associated data about each address, such
  as the year, president, party, and format, are also included.
Basic functions for dealing with wav files and sound samples.
Provides an all-in-one solution for automatic classification of 
    sound events using convolutional neural networks (CNN). The main purpose 
    is to provide a sound classification workflow, from annotating sound events
    in recordings to training and automating model usage in real-life
    situations. Using the package requires a pre-compiled collection of 
    recordings with sound events of interest and it can be employed for: 
    1) Annotation: create a database of annotated recordings, 
    2) Training: prepare train data from annotated recordings and fit CNN models, 
    3) Classification: automate the use of the fitted model for classifying 
    new recordings. By using automatic feature selection and a user-friendly GUI
    for managing data and training/deploying models, this package is intended 
    to be used by a broad audience as it does not require specific expertise in 
    statistics, programming or sound analysis. Please refer to the vignette for
    further information.
    Gibb, R., et al. (2019) <doi:10.1111/2041-210X.13101>
    Mac Aodha, O., et al. (2018) <doi:10.1371/journal.pcbi.1005995>
    Stowell, D., et al. (2019) <doi:10.1111/2041-210X.13103>
    LeCun, Y., et al. (2012) <doi:10.1007/978-3-642-35289-8_3>.
A set of tools that can be used in computer-aided analysis of
    sound correspondences between languages, plus several helper functions.
    Analytic functions range from purely qualitative analysis, through
    statistic methods yielding qualitative results, to an entirely
    quantitative approach.
Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.
The SoundexBR package provides an algorithm for decoding names
    into phonetic codes, as pronounced in Portuguese. The goal is for
    homophones to be encoded to the same representation so that they can be
    matched despite minor differences in spelling. The algorithm mainly encodes
    consonants; a vowel will not be encoded unless it is the first letter. The
    soundex code resultant consists of a four digits long string composed by
    one letter followed by three numerical digits: the letter is the first
    letter of the name, and the digits encode the remaining consonants.
Performs parametric synthesis of sounds with harmonic and noise 
    components such as animal vocalizations or human voice. Also offers tools 
    for audio manipulation and acoustic analysis, including pitch tracking, 
    spectral analysis, audio segmentation, pitch and formant shifting, etc. 
    Includes four interactive web apps for synthesizing and annotating audio, 
    manually correcting pitch contours, and measuring formant frequencies. 
    Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.
Implement a promising, and yet little explored protocol for bioacoustical analysis, the eigensound method by MacLeod, Krieger and Jones (2013) <doi:10.4404/hystrix-24.1-6299>. Eigensound is a multidisciplinary method focused on the direct comparison between stereotyped sounds from different species. 'SoundShape', in turn, provide the tools required for anyone to go from sound waves to Principal Components Analysis, using tools extracted from traditional bioacoustics (i.e. 'tuneR' and 'seewave' packages), geometric morphometrics (i.e. 'geomorph' package) and multivariate analysis (e.g. 'stats' package). For more information, please see Rocha and Romano (2021) and check 'SoundShape' repository on GitHub for news and updates <https://github.com/p-rocha/SoundShape>.
Quantify, profile and remove ambient mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments.  Implements the method described in Young et al. (2018) <doi:10.1101/303727>.
The algorithm pursues the identification of the set of variables driving the differences in two different experimental conditions (i.e., the primary genes) within a graphical model context. It uses the idea of simultaneously looking for the differences between two multivariate normal distributions in all marginal and conditional distributions associated with a decomposable graph, which represents the pathway under exam. The implementation accommodates genomics specific issues (low sample size and multiple testing issues) and provides a number of functions offering numerical and visual summaries to help the user interpret the obtained results. In order to use the (optional) 'Cytoscape' functionalities, the suggested 'r2cytoscape' package must be installed from the 'GitHub' repository ('devtools::install_github('cytoscape/r2cytoscape')'). More details in Salviato et al., (2020) <doi:10.1371/journal.pcbi.1007357> and Djordjilovic et al., (2022) <doi:10.1016/j.jmva.2022.104973>.
Tools for the reading and tokenization of R code. The
    'sourcetools' package provides both an R and C++ interface for the tokenization
    of R code, and helpers for interacting with the tokenized representation of R
    code.
A set of tools for state-dependent 
  empirical analysis through both VAR- and local projection-based 
  state-dependent forecasts, impulse response functions, 
  historical decompositions, and forecast error variance decompositions.
Efficient procedures for fitting and cross-validating the structurally-regularized time-dependent Cox models. The penalty term is a weighted sum of infinity norms of (overlapping) groups of coefficients, which can select variables following a user-specified grouping structure.
Genomic and multi-environmental soybean data. Soybean Nested
    Association Mapping (SoyNAM) project dataset funded by the United Soybean Board
    (USB). BLUP function formats data for genome-wide prediction and association analysis.
Data sets used by 'Krause et al. (2022)'  <doi:10.1101/2022.04.11.487885>. It comprises phenotypic records obtained from the USDA Northern Region Uniform Soybean Tests from 1989 to 2019 for maturity groups II and III. In addition, soil and weather variables are provided for the 591 observed environments (combination of locations and years).
Classes and methods for spatial
  data; the classes document where the spatial location information
  resides, for 2D or 3D data. Utility functions are provided, e.g. for
  plotting data as maps, spatial selection, as well as methods for
  retrieving coordinates, for subsetting, print, summary, etc. From this
  version, evolution status is set to '2L' using 'sf' in place of 'rgdal',
  see <https://r-spatial.org/r/2023/05/15/evolution4.html> for details.
  Retiring packages 'maptools', 'rgdal' and 'rgeos' will be dropped by
  October 2023.
A programmatic interface to <http://sp2000.org.cn>, re-written based on an accompanying 'Species 2000' API. Access tables describing catalogue of the Chinese known species of animals, plants, fungi, micro-organisms, and more. This package also supports access to catalogue of life global <http://catalogueoflife.org>, China animal scientific database <http://zoology.especies.cn> and catalogue of life Taiwan <https://taibnet.sinica.edu.tw/home_eng.php>. The development of 'SP2000' package were supported by Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment, China <2019HJ2096001006>,Yunnan University's "Double First Class" Project <C176240405> and Yunnan University's Research Innovation Fund for Graduate Students <2019227>.
Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.
Miscellaneous functions for analysing species association
        and niche overlap.
Genomic Region Detection via Spatial Convex Clustering. See <https://arxiv.org/abs/1611.04696> for details.
Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol',  'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from "Constructing Sobol sequences with better two-dimensional projections" (2012) <doi:10.1137/070709359> S. Joe and F. Y. Kuo, "Progressive Multi-Jittered Sample Sequences" (2018) <https://graphics.pixar.com/library/ProgressiveMultiJitteredSampling/paper.pdf> Christensen, P., Kensler, A. and Kilpatrick, C., and "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, <doi:10.1145/3306307.3328191>.
Social network analysis is becoming commonplace in many social 
    science disciplines, but access to useful network data, especially 
    among marginalized populations, still remains a formidable challenge. 
    This package mitigates that problem by providing tools to simulate spatial 
    Bernoulli networks as proposed in Carter T. Butts 
    (2002, ISBN:978-0-493-72676-2), "Spatial models of large-scale interpersonal            
    networks." Using this package, network analysts can simulate a spatial point 
    process or sequence with a given number of nodes inside a geographical 
    boundary and estimate the probability of a tie formation between all node 
    pairs. When simulating a network, an analyst can choose between five spatial
    interaction functions. The package also enables quick comparison of summary             
    statistics for simulated networks and provides simple to use plotting 
    methods for its classes that return plots which can be further refined with 
    the 'ggplot2' package.
Latent space models for multivariate networks (multiplex) estimated via MCMC algorithm. See D Angelo et al. (2018) <arXiv:1803.07166> and D Angelo et al. (2018) <arXiv:1807.03874>.
Standard RGB spaces included are sRGB, 'Adobe' RGB, 'ProPhoto' RGB, BT.709, and others.  User-defined RGB spaces are also possible.  There is partial support for ACES Color workflows.
Functions for converting among CIE XYZ, xyY, Lab, and Luv.
      Calculate Correlated Color Temperature (CCT) and the Planckian and daylight loci.  
      The XYZs of some standard illuminants and some standard linear chromatic adaptation transforms (CATs) are included.
      Three standard color difference metrics are included.
Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal matching or aggregation, retrieving coordinates, print, summary, etc.
Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields.
An R wrapper to the 'Python' 'spaCy' 'NLP' library,
    from <http://spacy.io>.
Provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the 'mapproj' package.
A set of RStudio addins that are designed to be used in
             combination with user-defined RStudio keyboard shortcuts. These
             addins either:
             1) insert text at a cursor position (e.g. insert
             operators %>%, <<-, %$%, etc.),
             2) replace symbols in selected pieces of text (e.g., convert
             backslashes to forward slashes which results in stings like
             "c:\data\" converted into "c:/data/") or
             3) enclose text with special symbols (e.g., converts "bold" into
             "**bold**") which is convenient for editing R Markdown files.
Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.
Provides GIS and map utilities, plus additional modeling tools for
    developing cellular automata, dynamic raster models, and agent based models
    in 'SpaDES'.
    Included are various methods for spatial spreading, spatial agents, GIS
    operations, random map generation, and others.
    See '?SpaDES.tools' for an categorized overview of these additional tools.
    The suggested package 'NLMR' can be installed with 
    'install.packages("NLMR", repos = "https://PredictiveEcology.r-universe.dev")'.
Implements methods for anticipating the emergence and eradication
    of infectious diseases from surveillance time series. Also provides support
    for computational experiments testing the performance of such methods.
Set of functions for sparse matrix algebra.
    Differences with other sparse matrix packages are:
    (1) we only support (essentially) one sparse matrix format,
    (2) based on transparent and simple structure(s),
    (3) tailored for MCMC calculations within G(M)RF.
    (4) and it is fast and scalable (with the extension package spam64).
    Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>; see 'citation("spam")' for details.
Provides the Fortran code of the R package 'spam'
    with 64-bit integers. Loading this package together with the R package
    spam enables the sparse matrix class spam to handle huge sparse matrices
    with more than 2^31-1 non-zero elements.
    Documentation is provided in Gerber, Moesinger and Furrer (2017) <doi:10.1016/j.cageo.2016.11.015>.
Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted  likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.
Fits multivariate Bayesian spatial regression models for large datasets using Spatial Multivariate Trees (SpamTrees). 
    The methods in this package are detailed in Peruzzi and Dunson (2020) <arXiv:2012.00943>. 
    Funded by ERC grant 856506 and NIH grant R01ES028804.
Character vector to numerical translation in Euros from Spanish
    spelled monetary quantities. Reverse translation from integer to Spanish.
    Upper limit is up to the millions range. Geocoding via Cadastral web site.
Tools for reading, visualising and processing Magnetic Resonance
    Spectroscopy data. The package includes methods for spectral fitting: Wilson
    (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018)
    <DOI:10.1002/mrm.27605>.
Implements the sparse clustering methods of Witten and
        Tibshirani (2010): "A framework for feature selection in
        clustering"; published in Journal of the American Statistical
        Association 105(490): 713-726.
Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark' from R. 'Apache Spark' is an open source cluster computing framework available at <http://spark.apache.org>. This R package uses the 'spark-sas7bdat' 'Spark' package (<https://spark-packages.org/package/saurfang/spark-sas7bdat>) to import and process 'SAS' data in parallel using 'Spark'. Hereby allowing to execute 'dplyr' statements in parallel on top of 'SAS' data.
Load Avro Files into 'Apache Spark' using 'sparklyr'. This
    allows to read files from 'Apache Avro' <https://avro.apache.org/>.
A 'sparklyr' extension package providing an integration with Google 'BigQuery'.
  It supports direct import/export where records are directly streamed from/to 'BigQuery'.
  In addition, data may be imported/exported via intermediate data extracts on Google 'Cloud Storage'.
'Hail' is an open-source, general-purpose, 'python' based data analysis
    tool with additional data types and methods for working with genomic data, see
    <https://hail.is/>. 'Hail' is built to scale and has first-class support for 
    multi-dimensional structured data, like the genomic data in a genome-wide 
    association study (GWAS). 'Hail' is exposed as a 'python' library, using primitives
    for distributed queries and linear algebra implemented in 'scala', 'spark', and 
    increasingly 'C++'. The 'sparkhail' is an R extension using 'sparklyr' package. 
    The idea is to help R users to use 'hail' functionalities with the well-know
    'tidyverse' syntax, see <https://www.tidyverse.org/>.
Include interactive sparkline charts
    <http://omnipotent.net/jquery.sparkline> in 
    all R contexts with the convenience of 'htmlwidgets'.
R interface to Apache Spark, a fast and general
    engine for big data processing, see <https://spark.apache.org/>. This
    package supports connecting to local and remote Apache Spark clusters,
    provides a 'dplyr' compatible back-end, and provides an interface to
    Spark's built-in machine learning algorithms.
This sparklyr extension makes 'Flint' time series
    library functionalities (<https://github.com/twosigma/flint>) easily
    accessible through R.
A 'sparklyr' extension adding the capability to work easily with nested data.
Generate syntax for use with the sparklines package for
        LaTeX.
A 'sparklyr' extension that enables reading and writing 'TensorFlow'
  TFRecord files via 'Apache Spark'.
Load WARC (Web ARChive) files into Apache Spark using 'sparklyr'. This
    allows to read files from the Common Crawl project <http://commoncrawl.org/>.
A 'sparklyr' <https://spark.rstudio.com/> extension that provides an R
  interface for 'XGBoost' <https://github.com/dmlc/xgboost> on 'Apache Spark'. 'XGBoost' is an
  optimized distributed gradient boosting library.
Provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.
The SPARRA risk score (Scottish Patients At Risk of admission and Re-Admission) estimates yearly risk of emergency hospital admission using electronic health records on a monthly basis for most of the Scottish population. This package implements a suite of functions used to analyse the behaviour and performance of the score, focusing particularly on differential performance over demographically-defined groups. It includes useful utility functions to plot receiver-operator-characteristic, precision-recall and calibration curves, draw stock human figures, estimate counterfactual quantities without the need to re-compute risk scores, to simulate a semi-realistic dataset.
Calculate the statistical power to detect clusters using kernel-based 
        spatial relative risk functions that are estimated using the 'sparr' package.
        Details about the 'sparr' package methods can be found in the tutorial: Davies
        et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation 
        can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More 
        information about relative risk functions using kernel density estimation can 
        be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics  23(4):985-1008.
'HJ-Biplot' is a multivariate method that allow represent multivariate data on a subspace of low dimension, in such a way that most of the variability of the information is captured in a few dimensions. This package implements three new techniques and constructs in each case the 'HJ-Biplot', adapting restrictions to reduce weights and / or produce zero weights in the dimensions, based on the regularization theories. It implements three methods of regularization: Ridge, LASSO and Elastic Net.
A set of tools for representing and estimating sparse Bayesian networks from continuous and discrete data, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>.
'C++' implementation of sparse LDL decomposition of symmetric matrices and solvers described by Timothy A. Davis (2016) 
  <https://fossies.org/linux/SuiteSparse/LDL/Doc/ldl_userguide.pdf>. Provides a set of C++ classes for basic sparse
  matrix specification and linear algebra, and a class to implement sparse LDL decomposition and solvers. See <https://github.com/samuel-watson/SparseChol>
  for details.
Implements the algorithm described in 
    Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential
    clustering algorithm for tracing cell type changes via single-cell
    RNA-sequencing data", Nucleic Acids Research, gkx1113,
    <doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two
    different populations, links the clusters across the conditions and
    identifies marker genes for these changes. The package was designed for
    scRNA-Seq data but is also applicable to many other data types, just
    replace cells with samples and genes with variables. The package also
    contains functions for estimating the parameters for SparseDC as outlined
    in the paper. We recommend that users further select their marker genes
    using the magnitude of the cluster centers.
Implementation of various estimation methods for dynamic factor models (DFMs) including principal components analysis (PCA) Stock and Watson (2002) <doi:10.1198/016214502388618960>, 2Stage Giannone et al. (2008) <doi:10.1016/j.jmoneco.2008.05.010>, expectation-maximisation (EM) Banbura and Modugno (2014) <doi:10.1002/jae.2306>, and the novel EM-sparse approach for sparse DFMs Mosley et al. (2023) <arXiv:2303.11892>. Options to use classic multivariate Kalman filter and smoother (KFS) equations from Shumway and Stoffer (1982) <doi:10.1111/j.1467-9892.1982.tb00349.x> or fast univariate KFS equations from Koopman and Durbin (2000) <doi:10.1111/1467-9892.00186>, and options for independent and identically distributed (IID) white noise or auto-regressive (AR(1)) idiosyncratic errors. Algorithms coded in 'C++' and linked to R via 'RcppArmadillo'.
A collection of sparse and regularized discriminant analysis
    methods intended for small-sample, high-dimensional data sets. The package
    features the High-Dimensional Regularized Discriminant Analysis classifier
    from Ramey et al. (2017) <arXiv:1602.01182>. Other classifiers include
    those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et
    al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012)
    <doi:10.1093/bioinformatics/btr690>.
Computation of sparse eigenvectors of a matrix (aka sparse PCA)
    with running time 2-3 orders of magnitude lower than existing methods and
    better final performance in terms of recovery of sparsity pattern and 
    estimation of numerical values. Can handle covariance matrices as well as 
    data matrices with real or complex-valued entries. Different levels of 
    sparsity can be specified for each individual ordered eigenvector and the 
    method is robust in parameter selection. See vignette for a detailed 
    documentation and comparison, with several illustrative examples. 
    The package is based on the paper:
    K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA 
    and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on 
    Signal Processing <doi:10.1109/TSP.2016.2605073>.
Multidimensional scaling provides a means of uncovering a latent structure underlying observed data, while estimating the number of latent dimensions.  This package presents a means for scaling binary and count data, for example the votes and word counts for legislators.  Future work will include an EM implementation and extend this work to ordinal and continuous data.
Estimation of functional linear mixed models for irregularly or
    sparsely sampled data based on functional principal component analysis.
Provides a general framework for performing sparse functional
    clustering as originally described in Floriello and Vitelli (2017)
    <doi:10.1016/j.jmva.2016.10.008>, with the possibility of jointly handling
    data misalignment (see Vitelli, 2019, <doi:10.48550/arXiv.1912.00687>).
Fits sparse frequentist GAMs (SF-GAM) for continuous and discrete responses in the exponential dispersion family with the group lasso, group smoothly clipped absolute deviation (SCAD), and group minimax concave (MCP) penalties <doi:10.1007/s11222-013-9424-2>. Also fits sparse Bayesian generalized additive models (SB-GAM) with the spike-and-slab group lasso (SSGL) penalty of Bai et al. (2021) <doi:10.1080/01621459.2020.1765784>. B-spline basis functions are used to model the sparse additive functions. Stand-alone functions for group-regularized negative binomial regression, group-regularized gamma regression, and group-regularized regression in the exponential dispersion family with the SSGL penalty are also provided.
Efficient implementation of sparse group lasso with optional
    bound constraints on the coefficients. It supports the use of a sparse
    design matrix as well as returning coefficient estimates in a sparse
    matrix. Furthermore, it correctly calculates the degrees of freedom to
    allow for information criteria rather than cross-validation with very
    large data. Finally, the interface to compiled code avoids unnecessary
    copies and allows for the use of long integers.
SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de
Estimates Hessian of a scalar-valued function, and returns it
    in a sparse Matrix format. The sparsity pattern must be known in advance. The
    algorithm is especially efficient for hierarchical models with a large number of
    heterogeneous units.  See Braun, M. (2017) <doi:10.18637/jss.v082.i10>.
Computation of sparse portfolios for financial index tracking, i.e., joint
    selection of a subset of the assets that compose the index and computation
    of their relative weights (capital allocation). The level of sparsity of the
    portfolios, i.e., the number of selected assets, is controlled through a
    regularization parameter. Different tracking measures are available, namely,
    the empirical tracking error (ETE), downside risk (DR), Huber empirical
    tracking error (HETE), and Huber downside risk (HDR). See vignette for a
    detailed documentation and comparison, with several illustrative examples.
    The package is based on the paper:
    K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional
    Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1,
    pp. 155-170, Jan. 2018. <doi:10.1109/TSP.2017.2762286>.
Creates a wrapper for the 'SuiteSparse' routines 
    that execute the Takahashi equations. These equations compute the
    elements of the inverse of a sparse matrix at locations where the
    its Cholesky factor is structurally non-zero. The resulting matrix is known as a 
	sparse inverse subset. Some helper functions are also implemented. 
	Support for spam matrices is currently limited and will be implemented 
	in the future. See Rue and Martino (2007) <doi:10.1016/j.jspi.2006.07.016> 
	and Zammit-Mangion and Rougier (2018) <doi:10.1016/j.csda.2018.02.001> for the 
	application of these equations to statistics.
Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models.
Models the nonnegative entries of a rectangular adjacency matrix using a sparse latent position model, as illustrated in Rastelli, R. (2018) "The Sparse Latent Position Model for nonnegative weighted networks" <arXiv:1808.09262>.
Provides an S4 class for representing and
    interacting with sparse plus rank matrices. At the moment the
    implementation is quite spare, but the plan is eventually subclass
    Matrix objects.
Use RcppEigen to fit least trimmed squares
    regression models with an L1 penalty in order to obtain
    sparse models.
Some basic linear algebra functionality for sparse matrices is
  provided:  including Cholesky decomposition and backsolving as well as 
  standard R subsetting and Kronecker products.
The 'sparseMatEst' package provides functions for estimating sparse 
  covariance and precision matrices with error control. A false positive
  rate is fixed corresponding to the probability of falsely including a matrix 
  entry in the support of the estimator.  It uses the binary search
  method outlined in Kashlak and Kong (2019) <arXiv:1705.02679> and in 
  Kashlak (2019) <arXiv:1903.10988>.
Implements the algorithm described in
    Barron, M., and Li, J. (Not yet published). This algorithm clusters
    samples from multiple ordered populations, links the clusters across the
    conditions and identifies marker genes for these changes. The package was
    designed for scRNA-Seq data but is also applicable to many other data
    types, just replace cells with samples and genes with variables. The
    package also contains functions for estimating the parameters for SparseMDC
    as outlined in the paper. We recommend that users further select their
    marker genes using the magnitude of the cluster centers.
Implementation of spatially-explicit, stochastic disease models with customizable time windows that describe how parameter values fluctuate during outbreaks (e.g., in response to public health or conservation interventions).
Implements the routines and algorithms developed and analysed in "Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists" Chan, L, Silverman, B. W., Vincent, K (2019) <arXiv:1902.05156>. This package explicitly handles situations where there are pairs of lists which have no observed individuals in common.  It deals correctly with parameters whose estimated values can be considered as being negative infinity.  It also addresses other possible issues of non-existence and non-identifiability of maximum likelihood estimates.
Computes multivariate normal (MVN) densities, and
    samples from MVN distributions, when the covariance or
    precision matrix is sparse.
Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <DOI: 10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) <arXiv:1804.00341>.
Provides interface to 'sparsepp' - fast, memory efficient hash map. 
    It is derived from Google's excellent 'sparsehash' implementation.
    We believe 'sparsepp' provides an unparalleled combination of performance and memory usage, 
    and will outperform your compiler's unordered_map on both counts. 
    Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater 
    memory usage (especially when the final size of the map is not known in advance).
An implementation of ranked sparsity methods, including
    penalized regression methods such as the sparsity-ranked lasso, its
    non-convex alternatives, and elastic net, as well as the sparsity-ranked
    Bayesian Information Criterion. As described in Peterson and
    Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked
    sparsity is a philosophy with methods primarily useful for
    variable selection in the presence of prior informational
    asymmetry, which occurs in the context of trying to perform variable
    selection in the presence of interactions and/or polynomials. Ultimately,
    this package attempts to facilitate dealing with cumbersome interactions
    and polynomials while not avoiding them entirely. Typically, models
    selected under ranked sparsity principles will also be more transparent,
    having fewer falsely selected interactions and polynomials than other
    methods.
Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects.  This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots.  The method can implement sparse linear regression and sparse probit regression.  Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to  models with truncated outcomes, propensity score, and instrumental variable analysis.
Provides elastic net regularized maximum likelihood for structural equation models (SEM) in inferring network structure (topology) and estimating causal effects. The package implements `lasso` and `elastic net` (l1/l2) penalized SEM and estimates the model parameters with an efficient block coordinate ascent algorithm that maximizes the penalized likelihood of the SEM.  Hyperparameters are inferred from cross-validation (CV).  A Stability Selection (STS) function is also available to provide accurate causal effect selection.
Implements the SparseStep model for solving regression
    problems with a sparsity constraint on the parameters. The SparseStep
    regression model was proposed in Van den Burg, Groenen, and Alfons (2017)
    <arXiv:1701.06967>. In the model, a regularization term is added to the
    regression problem which approximates the counting norm of the parameters.
    By iteratively improving the approximation a sparse solution to the
    regression problem can be obtained.  In this package both the standard
    SparseStep algorithm is implemented as well as a path algorithm which uses
    golden section search to determine solutions with different values for the
    regularization parameter.
Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix.
             Currently, only sparse real matrices in Matrix package format are supported.
Fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization.
Computes sparse vector autoregressive 				coefficients and precision matrices for time series 		chain graphical models. Fentaw Abegaz and Ernst Wit (2013)	<doi:10.1093/biostatistics/kxt005>.
A wrapper for sparse VAR/VECM time series models estimation
             using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped 
             Absolute Deviation) and MCP (Minimax Concave Penalty). 
             Based on the work of Sumanta Basu and George Michailidis 
             <doi:10.1214/15-AOS1315>.
Implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (2020) <doi:10.1080/01621459.2020.1847121> and Kolyan Ray, Botond Szabo, and Gabriel Clara (2020) <arXiv:2010.11665>.
The sparse vector field consensus 
		(SparseVFC) algorithm (Ma et al., 2013 <doi:10.1016/j.patcog.2013.05.017>) for robust vector 
		field learning. Largely translated from the Matlab functions in <https://github.com/jiayi-ma/VFC>.
Fast 'SVMlight' reader and writer. 
    'SVMlight' is most commonly used format for storing 
    sparse matrices (possibly with some target variable) on disk.
    For additional information about 'SVMlight' format see <http://svmlight.joachims.org/>.
Fast Multiplication and Marginalization of Sparse Tables.
Statistical pattern recognition and dating using archaeological artefacts assemblages.
    Package of statistical tools for archaeology.
    hclustcompro(perioclust): Bellanger Lise, Coulon Arthur, Husi Philippe (2021, ISBN:978-3-030-60103-4).
    mapclust: Bellanger Lise, Coulon Arthur, Husi Philippe (2021) <doi:10.1016/j.jas.2021.105431>.
    seriograph: Desachy Bruno (2004) <doi:10.3406/pica.2004.2396>.
    cerardat: Bellanger Lise, Husi Philippe (2012) <doi:10.1016/j.jas.2011.06.031>.
This is an implementation of the algorithm described in Section 3 of Hosszejni and Frühwirth-Schnatter (2022) <doi:10.48550/arXiv.2211.00671>. The algorithm is used to verify that the counting rule CR(r,1) holds for the sparsity pattern of the transpose of a factor loading matrix. As detailed in Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances are generically identified. If CR(r,1) does not hold, then we do not know whether the idiosyncratic variances are identified or not.
The Stratified-Petersen Analysis System (SPAS) is designed
    to estimate abundance in two-sample capture-recapture experiments 
    where the capture and recaptures are stratified. This is a generalization
    of the simple Lincoln-Petersen estimator.
    Strata may be defined in time or in space or both, 
    and the s strata in which marking takes place 
    may differ from the t strata in which recoveries take place.
    When s=t, SPAS reduces to the method described by 
    Darroch (1961) <https://www.jstor.org/stable/2332748>.
    When s<t, SPAS implements the methods described in
    Plante, Rivest, and Tremblay (1988) <https://www.jstor.org/stable/2533994>.
    Schwarz and Taylor (1998) <https://cdnsciencepub.com/doi/10.1139/f97-238> describe
    the use of SPAS in estimating return of salmon stratified by
    time and geography. 
    A related package, BTSPAS, deals with temporal stratification where 
    a spline is used to model the distribution of the population 
    over time as it passes the second capture location.
    This is the R-version of the (now obsolete) standalone Windows 
    program available at <https://home.cs.umanitoba.ca/~popan/spas/spas_home.html>.
Sample size estimation and blinded sample size reestimation in Adaptive Study Design.
Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. See Sigrist, Kuensch, and Stahel (2015) <doi:10.1111/rssb.12061> for more information on the methodology. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.
The heterogeneity of spatial data presenting a finite number of categories can be measured via computation of spatial entropy. Functions are available for the computation of the main entropy and spatial entropy measures in the literature. They include the traditional version of Shannon's entropy (Shannon, 1948 <doi:10.1002/j.1538-7305.1948.tb01338.x>), Batty's spatial entropy (Batty, 1974 <doi:10.1111/j.1538-4632.1974.tb01014.x>), O'Neill's entropy (O'Neill et al., 1998 <doi:10.1007/BF00162741>), Li and Reynolds' contagion index (Li and Reynolds, 1993 <doi:10.1007/BF00125347>), Karlstrom and Ceccato's entropy (Karlstrom and Ceccato, 2002 <urn:nbn:se:kth:diva-61351>), Leibovici's entropy (Leibovici, 2009 <doi:10.1007/978-3-642-03832-7_24>), Parresol and Edwards' entropy (Parresol and Edwards, 2014 <doi:10.3390/e16041842>) and Altieri's entropy (Altieri et al., 2018, <doi:10.1007/s10651-017-0383-1>). Full references for all measures can be found under the topic 'SpatEntropy'. The package is able to work with lattice and point data. The updated version works with the updated 'spatstat' package (>= 3.0-2).
Performs score test using saddlepoint approximation to estimate the null distribution. Also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results. For the latest version, please check <https://github.com/leeshawn/SPAtest>.
The implementation to perform the geometric spatial point analysis developed in Hernández & Solís (2022) <doi:10.1007/s00180-022-01244-1>. It estimates the geometric goodness-of-fit index for a set of variables against a response one based on the 'sf' package. The package has methods to print and plot the results.
Graphs (or networks) and graph component
        calculations for spatial locations in 1D, 2D, 3D etc.
A generic tool for manifold analysis. It allows to infer a relevant transition or evolutionary path which can highlights the features involved in a specific process. 'spathial' can be useful in all the scenarios where the temporal (or pseudo-temporal) evolution is the main problem (e.g. tumor progression). The algorithm for finding the principal path is described in: Ferrarotti et al., (2019) <doi:10.1109/TNNLS.2018.2884792>."
Functions for kriging and point pattern analysis.
Provides a set of spatial accessibility measures from a set of locations 
             (demand) to another set of locations (supply). It aims, among others, 
             to support research on spatial accessibility to health care facilities. 
             Includes the locations and some characteristics of major public hospitals in Greece.
Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) <doi:10.1093/biomet/asz079>.
Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units.  This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration.
Deconvolution of spatial transcriptomics data using deconvolution models based on deep neural networks and single-cell RNA-seq data. These models are able to make accurate estimates of the cell composition of spots in spatial transcriptomics datasets from the same context using the advances provided by deep learning and the meaningful information provided by single-cell RNA-Seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> to get an overview of the method, but its application to spatial transcriptomics data will be available soon.
Utilities to support spatial data manipulation, query, sampling
    and modelling in ecological applications. Functions include models for species 
	population density, spatial smoothing, multivariate separability, point process 
	model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and 
	analysis, auto-logistic modeling, sampling models, cluster optimization, statistical 
	exploratory tools and raster-based metrics.
Methods and data for cluster detection and disease mapping.
Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.
Finite element modeling (FEM) uses meshes of triangles to define surfaces.  
  A surface within a triangle may be either linear or quadratic. 
  In the order one case each node in the mesh is associated with a basis 
  function and the basis is called the order one finite element basis.
  In the order two case each edge mid-point is also associated with a basis
  function.  Functions are provided for smoothing, density function estimation
  point evaluation and plotting results.  Two papers illustrating the 
  finite element data analysis are Sangalli, L.M., Ramsay, J.O., 
  Ramsay, T.O. (2013)<http://www.mox.polimi.it/~sangalli> and
  Bernardi, M.S, Carey, M., Ramsay, J. O., Sangalli, L. 
  (2018)<http://www.mox.polimi.it/~sangalli>. Modelling 
  spatial anisotropy via regression with partial differential 
  regularization Journal of Multivariate Analysis, 167, 15-30.
Multivariate modelling of geostatistical (point), lattice (areal) and point pattern data in a unifying spatial fusion framework. Details are given in Wang and Furrer (2021) <doi:10.1016/j.csda.2021.107240>. Model inference is done using either 'Stan' <https://mc-stan.org/> or 'INLA' <https://www.r-inla.org/>.
Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder 'TMB' in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in 'TMB' by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2021) <arXiv:2110.07051>.
Provision of the S4 SpatialGraph class built on top of objects provided by 'igraph' and 'sp' packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg().
Calculate Kernel Density Estimation (KDE) for spatial data. 
  The algorithm is inspired by the tool 'Heatmap' from 'QGIS'. The method is described by:
  Hart, T., Zandbergen, P. (2014) <doi:10.1108/PIJPSM-04-2013-0039>, 
  Nelson, T. A., Boots, B. (2008) <doi:10.1111/j.0906-7590.2008.05548.x>,
  Chainey, S., Tompson, L., Uhlig, S.(2008) <doi:10.1057/palgrave.sj.8350066>.
Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), <doi:10.1137/19M1261195>). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has four main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference map with a list of other maps), compareAll() (to compute a matrix of distances between a list of maps), and focusArea() (to compute the KWD distance within a focus area). In non-convex maps, the helper functions first build the convex-hull of the input bins and pad the weights with zeros.
Implements a spatial extension of the random forest algorithm 
             (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>).
             Allows for a geographically weighted random forest regression 
             including a function to find the optical bandwidth. (Georganos 
             and Kalogirou (2022) <https://www.mdpi.com/2220-9964/11/9/471>).
Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) <doi:10.1214/088342304000000558> and Oja (2010) <doi:10.1007/978-1-4419-0468-3>.
Tools to assess the association between two spatial processes. Currently,
  several methodologies are implemented: A modified t-test to perform hypothesis testing
  about the independence between the processes, a suitable nonparametric correlation
  coefficient, the codispersion coefficient, and an F test for assessing the multiple
  correlation between one spatial process and several others. Functions for image
  processing and computing the spatial association between images are also provided.
  Functions contained in the package are intended to accompany Vallejos, R., Osorio, F.,
  Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables:
  With Applications in R. Springer, Cham <doi:10.1007/978-3-030-56681-4>.
A spatial population can be generated based on spatially varying regression model under the assumption that observations are collected from a uniform two-dimensional grid consist of (m * m) lattice points with unit distance between any two neighbouring points. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).<DOI:10.1080/10485252.2018.1499907>.  This spatially generated data can be used to test different issues related to the statistical analysis of spatial data.
 This generated spatial data can be utilized in geographically weighted regression analysis for studying the spatially varying relationships among the variables.
Computes spatial position models: the potential model as defined
    by Stewart (1941) <doi:10.1126/science.93.2404.89> and catchment areas as
    defined by Reilly (1931) or Huff (1964) <doi:10.2307/1249154>.
Bayesian Estimation of Spatial Probit and Tobit Models.
A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) <doi:10.1080/01621459.1975.10480272>. The models are further described by 'Anselin' (1988) <doi:10.1007/978-94-015-7799-1>. Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) <doi:10.1023/A:1007707430416> and (1999) <doi:10.1111/1468-2354.00027> are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) <doi:10.1201/9781420064254> are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) <doi:10.1111/gean.12008>, and model fitting methods by 'Bivand' and 'Piras' (2015) <doi:10.18637/jss.v063.i18>; both of these articles include extensive lists of references. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) <doi:10.3390/math9111276>. 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'.
A collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process.
Automatic generation and selection of spatial predictors for spatial regression with Random Forest. Spatial predictors are surrogates of variables driving the spatial structure of a response variable. The package offers two methods to generate spatial predictors from a distance matrix among training cases: 1) Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>): computed as the eigenvectors of a weighted matrix of distances; 2) RFsp (Hengl et al. <DOI:10.7717/peerj.5518>): columns of the distance matrix used as spatial predictors. Spatial predictors help minimize the spatial autocorrelation of the model residuals and facilitate an honest assessment of the importance scores of the non-spatial predictors. Additionally, functions to reduce multicollinearity, identify relevant variable interactions, tune random forest hyperparameters, assess model transferability via spatial cross-validation, and explore model results via partial dependence curves and interaction surfaces are included in the package. The modelling functions are built around the highly efficient 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>).
Methods for spatial risk calculations. It offers an efficient approach to determine the sum of all observations within a 
     circle of a certain radius. This might be beneficial for insurers who are required (by a recent European Commission regulation) to determine 
     the maximum value of insured fire risk policies of all buildings that are partly or fully located within a circle of a radius of 200m. See 
     Church (1974) <doi:10.1007/BF01942293> for a description of the problem.
Provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).
Functions and classes for spatial resampling to use with the
    'rsample' package, such as spatial cross-validation (Brenning, 2012)
    <doi:10.1109/IGARSS.2012.6352393>. The scope of 'rsample' and
    'spatialsample' is to provide the basic building blocks for creating
    and analyzing resamples of a spatial data set, but neither package
    includes functions for modeling or computing statistics. The resampled
    spatial data sets created by 'spatialsample' do not contain much
    overhead in memory.
Visualization and analysis  of Vectra Immunoflourescent
    data. Options for calculating both the univariate and bivariate Ripley's K
    are included. Calculations are performed using a permutation-based 
    approach presented by Wilson et al.  <doi:10.1101/2021.04.27.21256104>.
Tools for spatial data analysis.  Emphasis on kriging.  Provides functions for prediction and simulation.  Intended to be relatively straightforward, fast, and flexible.
Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, <arXiv:1809.06418>). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006.
Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field.  Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) <doi: 10.1175/2009WAF2222269.1> and for a tutorial on some of the main functions available here, see Gilleland (2022) <doi: 10.5065/4px3-5a05>.
Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation on raster data sets. EWS are metrics derived from the observed spatial structure of an ecosystem -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) <doi:10.1111/2041-210X.13058>).
Many packages use 'htmlwidgets' <https://CRAN.R-project.org/package=htmlwidgets>
    for interactive plotting of spatial data.
    This package provides functions for converting R objects, such as simple features, 
    into structures suitable for use in 'htmlwidgets' mapping libraries.
Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.
Detects spatial and temporal groups in GPS relocations 
    (Robitaille et al. (2020) <doi:10.1111/2041-210X.13215>). 
    It can be used to convert GPS relocations to 
    gambit-of-the-group format to build proximity-based social networks 
    In addition, the randomizations function provides data-stream 
    randomization methods suitable for GPS data.
Contains all the datasets for the 'spatstat' family of packages.
Functionality for exploratory data analysis and nonparametric analysis of
	     spatial data, mainly spatial point patterns,
	     in the 'spatstat' family of packages.
	     (Excludes analysis of spatial data on a linear network,
	     which is covered by the separate package 'spatstat.linnet'.)
	     Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Defines spatial data types and supports geometrical operations
	     on them. Data types include point patterns, windows (domains),
	     pixel images, line segment patterns, tessellations and hyperframes.
	     Capabilities include creation and manipulation of data
	     (using command line or graphical interaction),
	     plotting, geometrical operations (rotation, shift, rescale,
	     affine transformation), convex hull, discretisation and
	     pixellation, Dirichlet tessellation, Delaunay triangulation,
	     pairwise distances, nearest-neighbour distances,
	     distance transform, morphological operations
	     (erosion, dilation, closing, opening), quadrat counting,
	     geometrical measurement, geometrical covariance,
	     colour maps, calculus on spatial domains,
	     Gaussian blur, level sets of images, transects of images,
	     intersections between objects, minimum distance matching.
	     (Excludes spatial data on a network, which are supported by
	     the package 'spatstat.linnet'.)
Extension to the 'spatstat' package, containing
	     interactive graphics capabilities.
Extension to the 'spatstat' family of packages, for analysing
	     large datasets of spatial points on a network. The geometrically-
	     corrected K function is computed using a memory-efficient
	     tree-based algorithm described by Rakshit, Baddeley and Nair (2019).
Defines types of spatial data on a linear network
	     and provides functionality for geometrical operations,
	     data analysis and modelling of data on a linear network,
	     in the 'spatstat' family of packages.
	     Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks.
	     Data types defined on a network include point patterns, pixel images, functions, and tessellations.
	     Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
	Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood.
	Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
	Random point patterns on a network can be generated using a variety of models.
Extension to the 'spatstat' package, enabling the user
	     to fit point process models to point pattern data
	     by local composite likelihood ('geographically weighted
	     regression').
Functionality for parametric statistical modelling and inference for spatial data,
	     mainly spatial point patterns, in the 'spatstat' family of packages.
	     (Excludes analysis of spatial data on a linear network,
	     which is covered by the separate package 'spatstat.linnet'.)
	     Supports parametric modelling, formal statistical inference, and model validation.
	     Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.
Functionality for random generation of spatial data in the 'spatstat' family of packages.
	     Generates random spatial patterns of points according to many simple rules (complete spatial randomness,
	     Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns
	     (thinning, random shift, jittering),  simulated realisations of random point processes including
	     simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes
	     (using direct, Brix-Kendall, or hybrid algorithms),
	     log-Gaussian Cox processes, product shot noise cluster processes
	     and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm,
	     alternating Gibbs sampler, or coupling-from-the-past perfect simulation).
	     Also generates random spatial patterns of line segments,
	     random tessellations, and random images (random noise, random mosaics).
	     Excludes random generation on a linear network,
	     which is covered by the separate package 'spatstat.linnet'.
Defines sparse three-dimensional arrays
	     and supports standard operations on them.
	     The package also includes utility functions for
	     matrix calculations that are common in
	     statistics, such as quadratic forms.
Contains utility functions for the 'spatstat' family of packages
             which may also be useful for other purposes.
Bayesian inference for parametric proportional hazards spatial
    survival models; flexible spatial survival models. See Benjamin M. Taylor, Barry S. Rowlingson (2017) <doi:10.18637/jss.v077.i04>.
Provides a simple progress bar to use for basic and advanced users that suits all those who prefer procedural programming. It is especially useful for integration into markdown files thanks to the progress bar's customisable appearance.
Tools to convert from specific formats to more general forms of 
    spatial data. Using tables to store the actual entities present in spatial
    data provides flexibility, and the functions here deliberately 
    minimize the level of interpretation applied, leaving that for specific 
    applications. Includes support for simple features,  round-trip for 'Spatial' classes and long-form 
    tables, analogous to 'ggplot2::fortify'. There is also a more 'normal form' representation
    that decomposes simple features and their kin to tables of objects, parts, and unique coordinates.
Fits univariate and multivariate spatio-temporal
        random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley and Banerjee <doi:10.1016/j.envsoft.2019.104608>.
Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.
Implements a spatial Bayesian non-parametric factor analysis model 
    with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). 
    Spatial correlation is introduced in the columns of the factor loadings 
    matrix using a Bayesian non-parametric prior, the probit stick-breaking 
    process. Areal spatial data is modeled using a conditional autoregressive 
    (CAR) prior and point-referenced spatial data is treated using a Gaussian 
    process. The response variable can be modeled as Gaussian, probit, Tobit, or
    Binomial (using Polya-Gamma augmentation). Temporal correlation is 
    introduced for the latent factors through a hierarchical structure and can 
    be specified as exponential or first-order autoregressive. Full details of 
    the package can be found in the accompanying vignette. Furthermore, the 
    details of the package can be found in "Bayesian Non-Parametric Factor 
    Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019), 
    <arXiv:1911.04337>. The paper is in press at the journal Bayesian Analysis.
Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Pantalone F, Benedetti R, and Piersimoni F (2022) <doi:10.18637/jss.v103.c02>, Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194>, and Benedetti R and Piersimoni F (2017) <arXiv:1710.09116>. The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'.
Evaluation of control charts by means of
        the zero-state, steady-state ARL (Average Run Length) and RL quantiles.
        Setting up control charts for given in-control ARL. The control charts
        under consideration are one- and two-sided EWMA, CUSUM, and
        Shiryaev-Roberts schemes for monitoring the mean or variance of normally
        distributed independent data. ARL calculation of the same set of schemes under drift (in the mean) are added.
        Eventually, all ARL measures for the multivariate EWMA (MEWMA) are provided.
Provides statistical process control tools for stochastic 
    textured surfaces. The current version supports the following tools:
    (1) generic modeling of stochastic textured surfaces.
    (2) local defect monitoring and diagnostics in stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018a) 
        <doi:10.1080/00401706.2017.1302362>.
    (3) global change monitoring in the nature of stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018b) 
        <doi:10.1080/00224065.2018.1507559>.
    (4) computation of dissimilarity matrix of stochastic textured 
        surface images, which was proposed by Bui and Apley (2019b)
        <doi:10.1016/j.csda.2019.01.019>.
Calibration of thresholds of control charts such as
    CUSUM charts based on past data, taking estimation error into account.
A new reduced-rank LDA method which works for high dimensional multi-class data.
Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) <arXiv:1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.
Programs to find the sample size or power of studies using the 
    Sequential Parallel Comparison Design (SPCD) and programs to analyze 
    such studies. This is a clinical trial design where 
    patients initially on placebo who did not respond are re-randomized between 
    placebo and active drug in a second phase and the results of the two phases are 
    pooled. The method of analyzing binary data with this design is described in 
    Fava,Evins, Dorer and Schoenfeld(2003) <doi:10.1159/000069738>, and the 
    method of analyzing continuous data is described in Chen, Yang, 
    Hung and Wang (2011) <doi:10.1016/j.cct.2011.04.006>.
Fast computation of the required sample size or the
    achieved power, for GWAS studies with different types of covariate
    effects and different types of covariate-gene dependency structure.
    For the detailed description of the methodology, see Zhang (2022)
    "Power and Sample Size Computation for Genetic Association Studies
     of Binary Traits: Accounting for Covariate Effects" <arXiv:2203.15641>.
Spatial coverage sampling and random sampling from compact
        geographical strata created by k-means. See Walvoort et al. (2010) 
        <doi:10.1016/j.cageo.2010.04.005> for details.
Provides a covariance estimator for multivariate normal
        data that is sparse and positive definite.  Implements the
        majorize-minimize algorithm described in Bien, J., and
        Tibshirani, R. (2011), "Sparse Estimation of a Covariance
        Matrix," Biometrika. 98(4). 807--820.
Implements a spatially varying change point model with 
    unique intercepts, slopes, variance intercepts and slopes, and 
    change points at each location. Inference is within the 
    Bayesian setting using Markov chain Monte Carlo (MCMC). The 
    response variable can be modeled as Gaussian (no nugget), 
    probit or Tobit link and the five spatially varying parameter
    are modeled jointly using a multivariate conditional 
    autoregressive (MCAR) prior. The MCAR is a unique process that
    allows for a dissimilarity metric to dictate the local spatial 
    dependencies. Full details of the package can be found in the accompanying vignette.
    Furthermore, the details of the package can be found in the corresponding paper on arXiv
    by Berchuck et al (2018): "A spatially varying change points model for monitoring glaucoma 
    progression using visual field data", <arXiv:1811.11038>.
The sparse principal component regression is computed. The regularization parameters are optimized by cross-validation.
The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior.
Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. 
    It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep').
    Unlike other spatial data packages such as 'rnaturalearth' and 'maps', 
    it also contains data stored in a range of file formats including GeoJSON, ESRI Shapefile and GeoPackage. 
    Some of the datasets are designed to illustrate specific analysis techniques.
    cycle_hire() and cycle_hire_osm(), for example, is designed to illustrate point pattern analysis techniques.
Inspired by space-time regressions often performed to assess
    the expansion of the Neolithic from the Near East to Europe (Pinhasi et
    al. 2005 <doi:10.1371/journal.pbio.0030410>). Test for significant
    correlations between the (earliest) radiocarbon dates of archaeological
    sites and their respective distances from a hypothetical center of origin.
    Both ordinary least squares (OLS) and reduced major axis (RMA) methods
    are supported (Russell et al. 2014 <doi:10.1371/journal.pone.0087854>).
    It is also possible to iterate over many sites to identify the most likely
    origin.
A collection of functions to create spatial weights matrix
  objects from polygon 'contiguities', from point patterns by distance and
  tessellations, for summarizing these objects, and for permitting their
  use in spatial data analysis, including regional aggregation by minimum
  spanning tree; a collection of tests for spatial 'autocorrelation',
  including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord'
  (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel'
  general cross product statistic, Empirical Bayes estimates and
  'Assunção/Reis' (1999) <doi:10.1002/(SICI)1097-0258(19990830)18:16%3C2147::AID-SIM179%3E3.0.CO;2-I> Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992)
  <doi:10.1111/j.1538-4632.1992.tb00261.x> and multicoloured
  join count statistics, 'APLE' ('Li 'et al.' )
  <doi:10.1111/j.1538-4632.2007.00708.x>, local 'Moran's I', 'Gearys C' 
  ('Anselin' 1995) <doi:10.1111/j.1538-4632.1995.tb00338.x> and
  'Getis/Ord' G ('Ord' and 'Getis' 1995)
  <doi:10.1111/j.1538-4632.1995.tb00912.x>,
  'saddlepoint' approximations ('Tiefelsdorf' 2002)
  <doi:10.1111/j.1538-4632.2002.tb01084.x> and exact tests
  for global and local 'Moran's I' ('Bivand et al.' 2009)
  <doi:10.1016/j.csda.2008.07.021> and 'LOSH' local indicators
  of spatial heteroscedasticity ('Ord' and 'Getis')
  <doi:10.1007/s00168-011-0492-y>. The implementation of most of
  the measures is described in 'Bivand' and 'Wong' (2018)
  <doi:10.1007/s11749-018-0599-x>, with further extensions in 'Bivand' (2022)
  <doi:10.1111/gean.12319>.
  From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions
  previously present in this package are defunct in 'spdep' and may be found
  in 'spatialreg'.
Logging functions in 'RcppSpdlog' provide access to the logging
 functionality from the 'spdlog' 'C++' library. This package offers shorter convenience
 wrappers for the 'R' functions which match the 'C++' functions, namely via, say,
 'spdl::debug()' at the debug level. The actual formatting is done by the
 'fmt::format()' function from the 'fmtlib' library (that is also 'std::format()'
 in 'C++20' or later).
Spatial downscaling of climate data (Global Circulation Models/Regional Climate Models) using quantile-quantile bias correction technique.
An implementation of split-population duration regression models. 
    Unlike regular duration models, split-population duration models are
    mixture models that accommodate the presence of a sub-population that is 
    not at risk for failure, e.g. cancer patients who have been cured by 
    treatment. This package implements Weibull and Loglogistic forms for the 
    duration component, and focuses on data with time-varying covariates. 
    These models were originally formulated in Boag (1949) 
    <http://www.jstor.org/stable/2983694> and Berkson and Gage (1952) 
    <http://www.jstor.org/stable/2281318>, and extended in Schmidt and Witte 
    (1989) <doi:10.1016/0304-4076(89)90034-1>.
A spatio-dynamic modelling package that focuses on three
    characteristic wetland plant communities in a semiarid Mediterranean
    wetland in response to hydrological pressures from the catchment. The
    package includes the data on watershed hydrological pressure and the
    initial raster maps of plant communities but also allows for random initial
    distribution of plant communities. For more detailed info see: Martinez-Lopez et al. (2015) <doi:10.1016/j.ecolmodel.2014.11.024>.
It allows running 'Praat' scripts from R and it provides some
    wrappers for basic plotting. It also adds support for literate markdown
    tangling. The package is designed to bring reproducible phonetic research
    into R.
Makes Nuclear Magnetic Resonance spectroscopy (NMR spectroscopy) data analysis as easy as possible by only requiring a small set of functions to perform an entire analysis. 'speaq' offers the possibility of raw spectra alignment and quantitation but also an analysis based on features whereby the spectra are converted to peaks which are then grouped and turned into features. These features can be processed with any number of statistical tools either included in 'speaq' or available elsewhere on CRAN. More details can be found in Vu et al. (2011) <doi:10.1186/1471-2105-12-405> and Beirnaert et al. (2018) <doi:10.1371/journal.pcbi.1006018>.
Functions for conducting jackknife Euclidean / empirical likelihood inference for Spearman's rho (de Carvalho and Marques (2012) <10.1080/10920277.2012.10597644>).
Creates a data specification that describes the columns of a 
 table (data.frame). Provides methods to read, write, and update the 
 specification. Checks whether a table matches its specification. See
 specification.data.frame(),read.spec(), write.spec(), as.csv.spec(),
 respecify.character(), and %matches%.data.frame().
Calculate change point based on spectral clustering with the option to automatically calculate the number of clusters if this information is not available.
Utility functions for spectroscopy. 1. Functions to simulate
    spectra for use in teaching or testing. 2. Functions to process files created by
    'LoggerPro' and 'SpectraSuite' software.
Implementation of various methods in estimation of species richness or diversity in Wang (2011)<doi:10.18637/jss.v040.i09>.
A simple method to display and characterise the multidimensional ecological niche of a species. The method also estimates the optimums and amplitudes along each niche dimension. Give also an estimation of the degree of niche overlapping between species. See Kleparski and Beaugrand (2022) <doi:10.1002/ece3.8830> for further details.
Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for the RRR is further detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et al., (2009) <arXiv:0909.4061>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.
A set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images.
Provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson (2020, <doi: 10.1038/s41562-020-0912-z>) or multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, <doi: 10.1177/1745691616658637>) including functions to setup, run, evaluate, and plot all specifications.
Implementation of SPECS, your favourite Single-Equation Penalized Error-Correction Selector developed in
    Smeekes and Wijler (2020) <arXiv:1809.08889>. SPECS provides a fully automated estimation procedure for large and potentially
    (co)integrated datasets. The dataset in levels is converted to a conditional error-correction model, either by the user or
    by means of the functions included in this package, and various specialised forms of penalized regression can be applied to
    the model. Automated options for initializing and selecting a sequence of penalties, as well as the construction of penalty
    weights via an initial estimator, are available. Moreover, the user may choose from a number of pre-specified deterministic
    configurations to further simplify the model building process.
A collection of forecast verification routines developed for the SPECS
    FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.
Stores and eases the manipulation of spectra and associated data,
    with dedicated classes for spatial and soil-related data.
Provides interface to the 'Spectator Earth' API <https://api.spectator.earth/>, 
    mainly for obtaining the acquisition plans and satellite overpasses for Sentinel-1, 
    Sentinel-2, Landsat-8 and Landsat-9 satellites. Current position and trajectory can 
    also be obtained for a much larger set of satellites. It is also possible to search 
    the archive for available images over the area of interest for a given (past) period, 
    get the URL links to download the whole image tiles, or alternatively to download the 
    image for just the area of interest based on selected spectral bands.
Provides a consistent interface to use various methods to calculate
  the periodogram and estimate the period of a rhythmic time-course. Methods
  include Lomb-Scargle, fast Fourier transform, and three versions of the
  chi-square periodogram. See Tackenberg and Hughey (2021)
  <doi:10.1371/journal.pcbi.1008567>.
On discrete data spectral analysis is performed by Fourier and Hilbert
    transforms as well as with model based analysis called Lomb-Scargle method.
    Fragmented and irregularly spaced data can be processed in almost all methods. Both,
    FFT as well as LOMB methods take multivariate data and return standardized PSD. 
    For didactic reasons an analytical approach for deconvolution of noise spectra and 
    sampling function is provided.
    A user friendly interface helps to interpret the results.
Infrared, near-infrared and Raman spectroscopic data measured during chemical reactions, provide structural fingerprints by which molecules can be identified and quantified. The application of these spectroscopic techniques as inline process analytical tools (PAT), provides the pharmaceutical and chemical industry with novel tools, allowing to monitor their chemical processes, resulting in a better process understanding through insight in reaction rates, mechanistics, stability, etc.
    Data can be read into R via the generic spc-format, which is generally supported by spectrometer vendor software. Versatile pre-processing functions are available to perform baseline correction by linking to the 'baseline' package; noise reduction via the 'signal' package; as well as time alignment, normalization, differentiation, integration and interpolation. Implementation based on the S4 object system allows storing a pre-processing pipeline as part of a spectral data object, and easily transferring it to other datasets. Interactive plotting tools are provided based on the 'plotly' package. 
    Non-negative matrix factorization (NMF) has been implemented to perform multivariate analyses on individual spectral datasets or on multiple datasets at once. NMF provides a parts-based representation of the spectral data in terms of spectral signatures of the chemical compounds and their relative proportions.
    See 'hNMF'-package for references on available methods. The functionality to read in spc-files was adapted from the 'hyperSpec' package.
Performs cluster analysis of mixed-type data using Spectral Clustering, see F. Mbuga and, C. Tortora (2022) <doi:10.3390/stats5010001>.
Routines for creating, manipulating, and performing 
 Bayesian inference about Gaussian processes in 
 one and two dimensions using the Fourier basis approximation: 
 simulation and plotting of processes, calculation of 
 coefficient variances, calculation of process density, 
 coefficient proposals (for use in MCMC).  It uses R environments to
 store GP objects as references/pointers.
In the era of big data and hyperconnectivity, learning
    high-dimensional structures such as graphs from data has become a prominent
    task in machine learning and has found applications in many fields such as
    finance, health care, and networks. 'spectralGraphTopology' is an open source,
    documented, and well-tested R package for learning graphs from data. It
    provides implementations of state of the art algorithms such as Combinatorial
    Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation
    based on Majorization-Minimization (GLE-MM), and Graph Estimation based on
    Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph
    learning has been widely employed for clustering, where specific algorithms
    are available in the literature. To this end, we provide an implementation of
    the Constrained Laplacian Rank (CLR) algorithm.
Implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps.
Tools for obtaining, processing, and visualizing spectral reflectance data for the user-defined land or water surface classes for visual exploring in which wavelength the classes differ. Input should be a shapefile with polygons of surface classes (it might be different habitat types, crops, vegetation, etc.). The Sentinel-2 L2A satellite mission optical bands pixel data are obtained through the Google Earth Engine service (<https://earthengine.google.com/>) and used as a source of spectral data.
Predict regional community composition at a fine spatial 
    resolution using only sparse biological and environmental data. The package 
    is based on the DynamicFOAM algorithm described 
    in Mokany et al. (2011) <doi:10.1111/j.1461-0248.2011.01675.x>.
Spectra viewer, organizer, data preparation and property blocks
        from within R or stand-alone. Binary (application) part is 
        installed separately using spnInstallApp() from spectrino package.
Input/Output, processing and visualization of spectra taken with different spectrometers, including SVC (Spectra Vista), ASD and PSR (Spectral Evolution). Implements an S3 class 'spectra' that other packages can build on. Provides methods to access, plot, manipulate, splice sensor overlap, vector normalize and smooth spectra.
A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.
From output files obtained from the software 'ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.
Converts the floor speeches of Uruguayan legislators, extracted from the 
    parliamentary minutes, to tidy data.frame where each observation is the intervention of a single legislator.
Scrap speech text and speaker informations of speeches of House of 
          Representatives of Brazil, and transform in a cleaned tibble.
Fitting linear models and generalized linear models to large data sets by updating algorithms, according to the method described in Enea (2009, ISBN: 9788861294257).
Label, recode, rename, and convert datasets and ASCII files more efficiently. 'speedycode' automates the code necessary for labeling variables with the 'labelled' package, recoding and renaming variables with 'dplyr' syntax, and converting ASCII files with the 'readroper' package. Most functions require only the name of the dataset and the code will be automatically written. Some convenience functions useful for converting ASCII files are also included.
Functions for fitting semiparametric regression models for
        panel count survival data. An overview of the package can be found 
        in Wang and Yan (2011) <doi:10.1016/j.cmpb.2010.10.005> and
	Chiou et al. (2018) <doi:10.1111/insr.12271>.
Performs estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative, dichotomous, or right-censored time-to-event endpoint. The method improves efficiency by leveraging baseline predictors of the endpoint. The inverse probability weighting technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used to provide unbiased estimation when the endpoint is missing at random.
A set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).
Corrects the spelling of a given word in English 
    using a modification of Peter Norvig's spell correct 
    algorithm (see <http://norvig.com/spell-correct.html>) 
    which handles up to three edits. The algorithm tries to 
    find the spelling with maximum probability of intended
    correction out of all possible candidate corrections from
    the original word.
Spell checking common document formats including latex, markdown, manual pages,
    and description files. Includes utilities to automate checking of documentation and 
    vignettes as a unit test during 'R CMD check'. Both British and American English are 
    supported out of the box and other languages can be added. In addition, packages may
    define a 'wordlist' to allow custom terminology without having to abuse punctuation.
This implementation of the Empirical Mode Decomposition (EMD) works in 2 dimensions simultaneously, and 
    can be applied on spatial data. It can handle both gridded or un-gridded datasets.
Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.
Implements spatial error estimation and
    permutation-based variable importance measures for predictive models
    using spatial cross-validation and spatial block bootstrap.
Performs non-parametric tests of parametric specifications.
    Five tests are available. 
    Specific bandwidth and kernel methods can be chosen along with many other options. 
    Allows parallel computing to quickly compute p-values based on the bootstrap. 
    Methods implemented in the package are H.J. Bierens (1982) <doi:10.1016/0304-4076(82)90105-1>,
    J.C. Escanciano (2006) <doi:10.1017/S0266466606060506>,
    P.L. Gozalo (1997) <doi:10.1016/S0304-4076(97)86571-2>,
    P. Lavergne and V. Patilea (2008) <doi:10.1016/j.jeconom.2007.08.014>,
    P. Lavergne and V. Patilea (2012) <doi:10.1198/jbes.2011.07152>,
    J.H. Stock and M.W. Watson (2006) <doi:10.1111/j.1538-4616.2007.00014.x>,
    C.F.J. Wu (1986) <doi:10.1214/aos/1176350142>,
    J. Yin, Z. Geng, R. Li, H. Wang (2010) <https://www.jstor.org/stable/24309002>
    and J.X. Zheng (1996) <doi:10.1016/0304-4076(95)01760-7>.
Functions to produce a fully fledged 'geo-spatial' object extent as a
    'SpatialPolygonsDataFrame'. Also included are functions to generate polygons
    from raster data using 'quadmesh' techniques, a round number buffered extent, and
    general spatial-extent and 'raster-like' extent helpers missing from the originating
    packages. Some latitude-based tools for polar maps are included.
Estimation, scoring, and plotting functions for the semi-parametric factor model proposed by Liu & Wang (2022) <doi:10.1007/s11336-021-09832-8> and Liu & Wang (2023) <arXiv:2303.10079>. Both the conditional densities of observed responses given the latent factors and the joint density of latent factors are estimated non-parametrically. Functional parameters are approximated by smoothing splines, whose coefficients are estimated by penalized maximum likelihood using an expectation-maximization (EM) algorithm. E- and M-steps can be parallelized on multi-thread computing platforms that support 'OpenMP'. Both continuous and unordered categorical response variables are supported.
Implements a group-bridge penalized function-on-scalar regression
    model proposed by Wang et al. (2020) <arXiv:2006.10163>, to simultaneously
    estimate functional coefficient and recover the local sparsity.
Tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.
Efficient estimation of spatial econometric models of origin-destination flows, which may exhibit spatial autocorrelation in the dependent variable, the explanatory variables or both.
    The model is the one proposed by LeSage and Pace (2008) <doi:10.1111/j.1467-9787.2008.00573.x>, who develop a matrix formulation that exploits the relational structure of flow data.
    The estimation procedures follow most closely those outlined by Dargel (2021) (preprint available at <https://www.tse-fr.eu/fr/publications/revisiting-estimation-methods-spatial-econometric-interaction-models>).
An implementation of feature selection, weighting and ranking via simultaneous perturbation
    stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of
    features that yield the best predictive performance using some error measures such as mean 
    squared error (for regression problems) and accuracy rate (for classification problems).
Estimation and Prediction Functions Using Bayesian Hierarchical Spatial Finlay-Wilkinson Model for Analysis of Multi-Environment Field Trials.
A collection of functions to deal with spatial and spatiotemporal autoregressive conditional heteroscedasticity (spatial ARCH and GARCH models) by Otto, Schmid, Garthoff (2018, Spatial Statistics) <arXiv:1609.00711>: simulation of spatial ARCH-type processes (spARCH, exponential spARCH, complex spARCH); quasi-maximum-likelihood estimation of the parameters of spARCH models and spatial autoregressive models with spARCH disturbances, diagnostic checks, visualizations.
A collection of statistical hypothesis tests and other 
	techniques for identifying certain spatial relationships/phenomena in 
	DNA sequences. In particular, it provides tests and graphical methods for determining 
	whether or not DNA sequences comply with Chargaff's second parity rule 
	or exhibit purine-pyrimidine parity. In addition, there are functions for 
	efficiently simulating discrete state space Markov chains and testing 
	arbitrary symbolic sequences of symbols for the presence of first-order 
	Markovianness.
	Also, it has functions for counting words/k-mers (and cylinder patterns) in 
	arbitrary symbolic sequences. Functions which take a DNA sequence as input 
	can handle sequences stored as SeqFastadna objects from the 'seqinr' package.
Functions for computing geographically weighted
  regressions are provided, based on work by Chris
  Brunsdon, Martin Charlton and Stewart Fotheringham.
A simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) <doi:10.1016/j.jspi.2020.07.001>.
Fitting dimension reduction methods to data lying on two-dimensional sphere.
    This package provides principal geodesic analysis, principal circle, principal curves 
    proposed by Hauberg, and spherical principal curves. Moreover, it offers the method of locally   
    defined principal geodesics which is underway. The detailed procedures 
    are described in Lee, J., Kim, J.-H. and Oh, H.-S. (2021) <doi:10.1109/TPAMI.2020.3025327>. 
    Also see Kim, J.-H., Lee, J. and Oh, H.-S. (2020) <arXiv:2003.02578>.
Various functions for creating spherical coordinate system plots via extensions to rgl.
Performs Delaunay and Voronoï tessellations on spheres and
    provides some functions to plot them. The algorithms are mainly performed by
    the 'C++' library 'CGAL' (<https://www.cgal.org/>).
Provides several methods to integrate functions over the unit
 sphere and ball in n-dimensional Euclidean space.  Routines for converting to/from
 multivariate polar/spherical coordinates are also provided.
Spherical K-function for point-pattern analysis on the sphere.
Functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables.
Implementation of uniformity tests on the circle and
    (hyper)sphere. The main function of the package is unif_test(), which
    conveniently collects more than 30 tests for assessing uniformity on
    S^{p-1}={x in R^p : ||x||=1}, p >= 2. The test statistics are implemented
    in the unif_stat() function, which allows computing several statistics to
    several samples within a single call, thus facilitating Monte Carlo
    experiments. Furthermore, the unif_stat_MC() function allows
    parallelizing them in a simple way. The asymptotic null distributions of
    the statistics are available through the function unif_stat_distr(). The
    core of 'sphunif' is coded in C++ by relying on the 'Rcpp' package.
    The package also provides several novel datasets and gives the
    reproducibility for the data application in García-Portugués,
    Navarro-Esteban and Cuesta-Albertos (2020) <arXiv:2008.09897>.
The SNP Panel Identification Assay (SPIA) is a package that enables an accurate determination of cell line identity from the genotype of single nucleotide polymorphisms (SNPs). The SPIA test allows to discern when two cell lines are close enough to be called similar and when they are not. Details about the method are available at "Demichelis et al. (2008) SNP panel identification assay (SPIA): a genetic-based assay for the identification of cell lines. Nucleic Acids Res., 3, 2446-2456".
A set of functions allowing to implement the 'SpiceFP' approach
  which is iterative. It involves transformation of functional predictors into
  several candidate explanatory matrices (based on contingency tables), to which
  relative edge matrices with contiguity constraints are associated. Generalized
  Fused Lasso regression are performed in order to identify the best candidate
  matrix, the best class intervals and related coefficients at each iteration.
  The approach is stopped when the maximal number of iterations is reached or
  when retained coefficients are zeros. Supplementary functions allow to get
  coefficients of any candidate matrix or mean of coefficients of many candidates.
  The methods in this package are describing in Girault Gnanguenon Guesse, 
  Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021)
  "An exploratory penalized regression to identify combined effects of functional
  variables -Application to agri-environmental issues" 
  <https://hal.archives-ouvertes.fr/hal-03298977>.
Analysis of species limits and DNA barcoding data. Included are functions for generating important summary statistics from DNA barcode data, assessing specimen identification efficacy, testing and optimizing divergence threshold limits, assessment of diagnostic nucleotides, and calculation of the probability of reciprocal monophyly. Additionally, a sliding window function offers opportunities to analyse information across a gene, often used for marker design in degraded DNA studies. Further information on the package has been published in Brown et al (2012) <doi:10.1111/j.1755-0998.2011.03108.x>.
The 'Robots Exclusion Protocol' <https://www.robotstxt.org/orig.html> documents
    a set of standards for allowing or excluding robot/spider crawling of different areas of
    site content. Tools are provided which wrap The 'rep-cpp' <https://github.com/seomoz/rep-cpp>
    C++ library for processing these 'robots.txt' files.
Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML).  For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.
Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.
Spike and slab for prediction and variable selection in linear regression models. Uses a generalized elastic net for variable selection.
Bayesian variable selection, model choice, and regularized
    estimation for (spatial) generalized additive mixed regression models
    via stochastic search variable selection with spike-and-slab priors.
A user-friendly tool for estimating both total and directional connectedness spillovers based on Diebold and Yilmaz (2009, 2012). It also provides the user with rolling estimation for total and net indices. User can find both orthogonalized and generalized versions for each kind of measures. See Diebold and Yilmaz (2009, 2012) find them at  <doi:10.1111/j.1468-0297.2008.02208.x> and <doi:10.1016/j.ijforecast.2011.02.006>.
Calculates constant structure parameters of endocrine homeostatic systems from equilibrium hormone concentrations. Methods and equations have been described in Dietrich et al. (2012) <doi:10.1155/2012/351864> and Dietrich et al. (2016) <doi:10.3389/fendo.2016.00057>.
Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) <doi:10.1007/s10260-022-00655-0>) for the semiparametric setting introduced in Drost et al. (2009) <doi:10.1111/j.1467-9868.2008.00687.x>, different procedures to bootstrap INAR data (Jentsch, C. and Weiß, C.H. (2017) <doi:10.3150/18-BEJ1057>) and flexible simulation of INAR data.
Many complex diseases are known to be affected by the interactions between 
   genetic variants and environmental exposures beyond the main genetic and environmental 
   effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are 
   challenged by the high-dimensional nature of the study and the complexity of environmental 
   influences. We have developed a novel and powerful semi-parametric Bayesian variable 
   selection method that can accommodate linear and nonlinear G×E interactions simultaneously 
   (Ren et al. (2019) <arXiv:1906.01057>). Furthermore, the proposed method can conduct 
   structural identification by distinguishing nonlinear interactions from main effects only 
   case within Bayesian framework. Spike-and-slab priors are incorporated on both individual 
   and group level to shrink coefficients corresponding to irrelevant main and interaction 
   effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and 
   alternative methods are  efficiently implemented in C++.
Functions for spatial methods based on generalized estimating equations (GEE) and
  wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR),
  conducting multi-model inference, and stepwise model selection. Further, contains functions 
  for spatially corrected model accuracy measures.
Data visualization tours animates linear projection 
  of multivariate data as its basis (ie. orientation) changes. The 'spinifex' 
  packages generates paths for manual tours by manipulating the contribution of 
  a single variable at a time Cook & Buja (1997) 
  <doi:10.1080/10618600.1997.10474754>. Other types of tours, such as grand 
  (random walk) and guided (optimizing some objective function) are available 
  in the 'tourr' package Wickham et al. <doi:10.18637/jss.v040.i02>. 
  'spinifex' builds on 'tourr' and can render tours with 'gganimate' and 
  'plotly' graphics, and allows for exporting as an .html widget and as an .gif, 
  respectively. This work is fully discussed in Spyrison & Cook (2020) 
  <doi:10.32614/RJ-2020-027>.
Proposes a 'torch' implementation of Graph Net architecture allowing different options for message passing and feature embedding.
Implements a generative model that uses a
    spike-and-slab like prior distribution obtained by multiplying a
    deterministic binary vector. Such a model allows an EM algorithm,
    optimizing a type-II log-likelihood.
It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>, makes so-called spiral graph or spiral chart. 
    It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high 
    resolution. 2. It is efficient for time series data to reveal periodic patterns.
Contains an R Markdown template for a clinical trial 
    protocol adhering to the SPIRIT statement. The SPIRIT (Standard Protocol 
    Items for Interventional Trials) statement outlines recommendations for a 
    minimum set of elements to be addressed in a  clinical trial protocol. 
    Also contains functions to create a xml document from the template and 
    upload it to clinicaltrials.gov<https://www.clinicaltrials.gov/> for 
    trial registration.
Import, process, summarize and visualize raw data from 
    metabolic carts. See Robergs, Dwyer, and Astorino (2010) <doi:10.2165/11319670-000000000-00000> for more details on data processing.
The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".
This program calculates bioclimatic indices and fluxes (radiation, 
    evapotranspiration, soil moisture) for use in studies of ecosystem function, 
    species distribution, and vegetation dynamics under changing climate 
    scenarios. Predictions are based on a minimum of required inputs: latitude, 
    precipitation, air temperature, and cloudiness. 
    Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>.
The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators. It supports one-step (Pinkse and Slade, 1998) <doi:10.1016/S0304-4076(97)00097-3> and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) <doi:10.1198/073500107000000188>. It also allows for either Probit or Logit model and compute the average marginal effects.
An extension to the individual claim simulator called 'SynthETIC'
    (on CRAN), to simulate the evolution of case estimates of incurred losses
    through the lifetime of an insurance claim. The transactional simulation
    output now comprises key dates, and both claim payments and revisions of
    estimated incurred losses. An initial set of test parameters, designed to
    mirror the experience of a real insurance portfolio, were set up and applied
    by default to generate a realistic test data set of incurred histories (see
    vignette). However, the distributional assumptions used to generate this
    data set can be easily modified by users to match their experiences.
    Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss
    and Incurred Cost Experience Simulator" <arXiv:2109.04058>.
Constructs basis matrix of B-splines, M-splines,
    I-splines, convex splines (C-splines), periodic M-splines,
    natural cubic splines, generalized Bernstein polynomials,
    and their integrals (except C-splines) and derivatives
    of given order by close-form recursive formulas.
    It also contains a C++ head-only library integrated with Rcpp.
    See Wang and Yan (2021) <doi:10.6339/21-JDS1020> for details.
Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.
Splines are efficiently represented through their Taylor expansion at the knots. The representation accounts for the support sets and is thus suitable for sparse functional data. Two cases of boundary conditions are considered: zero-boundary or periodic-boundary for all derivatives except the last. The periodical splines are represented graphically using polar coordinates. The B-splines and orthogonal bases of splines that reside on small total support are implemented. The orthogonal bases are referred to as 'splinets' and are utilized for functional data analysis. Random spline generator is implemented as well as  all fundamental algebraic and calculus operations on splines.  The optimal, in the least square sense, functional fit by 'splinets' to data consisting of sampled values of functions as well as splines build over another set of knots is obtained and used for functional data analysis.  <arXiv:2102.00733>, <doi:10.1016/j.cam.2022.114444>,  <arXiv:2302.07552>.
Procedure to optimally split a dataset for training and testing. 
    'SPlit' is based on the method of support points, which is independent of modeling methods.
    Please see Joseph and Vakayil (2021) <doi:10.1080/00401706.2021.1921037> for details.
    This work is supported by U.S. National Science Foundation grant DMREF-1921873.
An implementation of the majorization-minimization (MM) algorithm introduced by
    Xu, Chi, Yang, and Lange (2017) <arXiv:1612.05614> for solving multi-set split feasibility problems. In the multi-set split feasibility problem, we seek to find a point x
    in the intersection of multiple closed sets and whose image under a mapping also must fall in the intersection of several closed sets.
Some R functions, such as optim(), require a function
  its gradient passed as separate arguments. When these are
  expensive to calculate it may be much faster to calculate
  the function (fn) and gradient (gr) together since they often share
  many calculations (chain rule). This package allows the user
  to pass in a single function that returns both the function
  and gradient, then splits (hence 'splitfngr') them
  so the results can be accessed
  separately. The functions provided allow this to be done with
  any number of functions/values, not just for functions and gradients.
Functions to compute split generalized linear models. The approach fits 
             generalized linear models that split the covariates into groups. The 
             optimal split of the variables into groups and the regularized estimation 
             of the coefficients are performed by minimizing an objective function 
             that encourages sparsity within each group and diversity among them.
             Example applications can be found in Christidis et al. (2021)
             <arXiv:2102.08591>.
Estimate the internal consistency of your tasks with a permutation based split-half reliability approach.
    Unofficial release name: "I eat stickers all the time, dude!".
Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. The 'splithalfr' supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, and the option to sub-sample data.
Split Knockoff is a data adaptive variable selection framework for controlling the
             (directional) false discovery rate (FDR) in structural sparsity, where variable 
             selection on linear transformation of parameters is of concern. This proposed scheme
             relaxes the linear subspace constraint to its neighborhood, often known as variable
             splitting in optimization.
             Simulation experiments can be reproduced following the Vignette. We include data
             (both .mat and .csv format) and application with our method of Alzheimer's Disease 
             study in this package.
             'Split Knockoffs' is first defined in Cao et al. (2021) <arXiv:2103.16159>.
Functions for computing split regularized estimators defined in Christidis, Lakshmanan, 
             Smucler and Zamar (2019) <arXiv:1712.03561>. The approach fits linear regression models that
             split the set of covariates into groups. The optimal split of the variables into groups and the 
             regularized estimation of the regression coefficients are performed by minimizing  an objective 
             function that encourages sparsity within each group and diversity among them. 
             The estimated coefficients are then pooled together to form the final fit.
Functions to generate or sample from all possible splits of features or variables
             into a number of specified groups. Also computes the best split selection estimator 
             (for low-dimensional data) as defined in Christidis, Van Aelst and Zamar (2019) 
             <arXiv:1812.05678>.
Allows to produce and use classification trees with soft (probability) splits,
	as described in: Dvořák, J. (2019), <doi:10.1007/s00180-019-00867-1>.
Online data collection tools like Google Forms often export
    multiple-response questions with data concatenated in cells. The
    concat.split (cSplit) family of functions splits such data into separate 
    cells. The package also includes functions to stack groups of columns and 
    to reshape wide data, even when the data are "unbalanced"---something 
    which reshape (from base R) does not handle, and which melt and dcast from 
    reshape2 do not easily handle.
Fast, lightweight toolkit for data splitting. Data sets can
    be partitioned into disjoint groups (e.g. into training, validation,
    and test) or into (repeated) k-folds for subsequent cross-validation.
    Besides basic splits, the package supports stratified, grouped as well
    as blocked splitting. Furthermore, cross-validation folds for time
    series data can be created. See e.g. Hastie et al. (2001)
    <doi:10.1007/978-0-387-84858-7> for the basic background on data
    partitioning and cross-validation.
ML and GM estimation and diagnostic testing of econometric models for spatial panel data.
Contains functions that fit linear mixed-effects models
        for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection. 
        The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation
        is based on the R package 'lmmlasso'. 
        Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis.
Automates common plotting tasks to ease data exploration.
  Makes density plots (potentially overlaid on histograms),
  scatter plots with prediction lines, or bar or line plots with error bars.
  For each type, y, or x and y variables can be plotted at levels of other variables,
  all with minimal specification.
Provides functions for fitting a sparse
        partial least squares (SPLS) regression and classification
        (Chun and Keles (2010) <doi:10.1111/j.1467-9868.2009.00723.x>).
Currently there are many functions in S-PLUS that are
  missing in R. To facilitate the conversion of S-PLUS packages to R packages,
  this package provides some missing S-PLUS functionality in R.
Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) <https:www.ga.gov.au/metadata-gateway/metadata/record/gcat_71407>
  Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) <doi:10.1016/j.csr.2011.05.015>
  Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) <doi:10.1016/j.envsoft.2011.07.004>
  Li, J., Potter, A., Huang, Z. and Heap, A. (2012) <https:www.ga.gov.au/metadata-gateway/metadata/record/74030>.
An updated and extended version of 'spm' package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.
Build custom Europe SpatialPolygonsDataFrame, if you don't know what is
    a SpatialPolygonsDataFrame see SpatialPolygons() in 'sp', by example for mapLayout() in 'antaresViz'. 
    Antares is a powerful software developed by RTE to simulate and study electric power systems 
    (more information about 'Antares' here: <https://antares-simulator.org/>).
A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016) <doi:10.1016/j.cageo.2016.06.001>.
Implements the method of general semiparametric maximum likelihood estimation for logistic models in case-mother control-mother designs.
Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.
Functions for estimating spatial varying coefficient models, mixed models, and other spatial regression models for Gaussian and non-Gaussian data. Moran eigenvectors are used to an approximate Gaussian process modeling which is interpretable in terms of the Moran coefficient. The GP is used for modeling the spatial processes in residuals and regression coefficients. For details see Murakami (2021) <arXiv:1703.04467>.
Identify statistically significant flow clusters using the local spatial network autocorrelation statistic G_ij* 
    proposed by 'Berglund' and 'Karlström' (1999) <doi:10.1007/s101090050013>. 
    The metric, an extended statistic of 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>, 
    detects a group of flows having similar traits in terms of directionality. 
    You provide OD data and the associated polygon to get results 
    with several parameters, some of which are defined by spdep package.
Facilitates the rendering of networks for which nodes have a specific position on a map (cities, participants in a political debate, etc.). Map data and network data are stored together in a single object which handles the match between network nodes and their respective position on the map. The plot method renders both the map and the network data. Several networks can be plot simultaneously. The graphic is highly customisable and the legend is automatically printed. Map data have to be supplied as 'SpatialPolygons' objects (from the 'sp' package) and network data as 'named matrix'.
Perform spatial analysis on network.
    Implement several methods for spatial analysis on network: Network Kernel Density estimation, 
    building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation 
    for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation
    References: Okabe et al (2019) <doi:10.1080/13658810802475491>;
    Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).
Non-negative Matrix Factorization(NMF) is a powerful tool for identifying the key features of microbial communities and a dimension-reduction method. When we are interested in the differences between the structures of two groups of communities, supervised NMF(Yun Cai, Hong Gu and Tobby Kenney (2017),<doi:10.1186/s40168-017-0323-1>) provides a better way to do this, while retaining all the advantages of NMF -- such as interpretability, and being based on a simple biological intuition.
Scale invariant version of the original PNN proposed by Specht (1990) <doi:10.1016/0893-6080(90)90049-q> with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations.
Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Banerjee (2022) <doi:10.18637/jss.v103.i05>, Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) <doi:10.1080/10618600.2018.1537924>, and Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091>.
A programmatic interface to many species occurrence data sources,
    including Global Biodiversity Information Facility ('GBIF'), 'iNaturalist',
    'eBird', Integrated Digitized 'Biocollections' ('iDigBio'), 'VertNet', 
    Ocean 'Biogeographic' Information System ('OBIS'), and 
    Atlas of Living Australia ('ALA'). Includes functionality for retrieving
    species occurrence data, and combining those data.
Fits single-species, multi-species, and integrated non-spatial and spatial occupancy models using Markov Chain Monte Carlo (MCMC). Models are fit using Polya-Gamma data augmentation detailed in Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>. Spatial models are fit using either Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large spatial datasets. Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2020) <arXiv:2001.09111>. Provides functionality for data integration of multiple single-species occupancy data sets using a joint likelihood framework. Details on data integration are given in Miller, Pacifici, Sanderlin, and Reich (2019) <doi:10.1111/2041-210X.13110>. Details on single-species and multi-species models are found in MacKenzie, Nichols, Lachman, Droege, Royle, and Langtimm (2002) <doi:10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2> and Dorazio and Royle <doi:10.1198/016214505000000015>, respectively.
It can be useful to temporarily hide some text or other HTML elements 
    in 'Shiny' applications. Building on 'Spoiler-Alert.js', it is possible to select the
    elements to hide at startup, to partially reveal them by hovering them, and to 
    completely show them when clicking on them.
Convert text (and text in R objects) to Mocking SpongeBob case 
             <https://knowyourmeme.com/memes/mocking-spongebob> and show them 
             off in fun ways. 
             CoNVErT TexT (AnD TeXt In r ObJeCtS) To MOCkINg SpoNgebOb CAsE
             <https://knowyourmeme.com/memes/mocking-spongebob> aND shOw tHem 
             OFf IN Fun WayS.
Transform a Movie into a Synthetic Picture. A frame every 10 seconds is summarized into one colour, then every generated colors are stacked together.
Proposes application of spectral analysis and jack-knife resampling for multivariate sequence forecasting. The application allows for a fast random search in a compact space of hyper-parameters composed by Sequence Length and Jack-Knife Leave-N-Out.
The 'spork' syntax describes
 label formatting concisely, supporting
 mixed nesting of subscripts and superscripts
 to arbitrary depth. It intends to be easy
 to read and write in plain text, and easy
 to convert to equivalent presentations
 in 'plotmath', 'latex', and 'html'.  Greek symbols
 and a multiplication symbol are explicitly
 supported. See ?as_spork and ?as_previews.
Calculates ratings for two-player or 
  multi-player challenges. Methods included in package such as are able to 
  estimate ratings (players strengths) and their evolution in time, also able to 
  predict output of challenge. Algorithms are based on Bayesian Approximation 
  Method, and they don't involve any matrix inversions nor likelihood estimation. 
  Parameters are updated sequentially, and computation doesn't require any 
  additional RAM to make estimation feasible. Additionally, base of the package 
  is written in C++ what makes sport computation even faster. Methods used in the 
  package refers to Mark E. Glickman (1999) 
  <http://www.glicko.net/research/glicko.pdf>; 
  Mark E. Glickman (2001) <doi:10.1080/02664760120059219>; 
  Ruby C. Weng, Chih-Jen Lin (2011) 
  <http://jmlr.csail.mit.edu/papers/volume12/weng11a/weng11a.pdf>; 
  W. Penny, Stephen J. Roberts (1999) <doi:10.1109/IJCNN.1999.832603>.
A time series causal inference model for Randomized Controlled Trial (RCT) under spillover effect. 'SPORTSCausal' (Spillover Time Series Causal Inference) separates treatment effect and spillover effect from given responses of experiment group and control group by predicting the response without treatment. It reports both effects by fitting the Bayesian Structural Time Series (BSTS) model based on 'CausalImpact', as described in Brodersen et al. (2015) <doi:10.1214/14-AOAS788>.
Use of Knock Out and Round Robin Techniques in preparing tournament fixtures as discussed in the Book Health and Physical Education by 'Dr. V K Sharma'(2018,ISBN:978-93-5272-134-4).
Create scaled 'ggplot' representations of playing surfaces.
    Playing surfaces are drawn pursuant to rule-book specifications.
    This package should be used as a baseline plot for displaying any type of
    tracking data.
A set of tools for model-based optimization and tuning of
    algorithms (hyperparameter tuning respectively hyperparameter optimization). It includes surrogate models, optimizers, and design of experiment
    approaches. The main interface is spot, which uses sequentially updated
    surrogate models for the purpose of efficient optimization. The main goal is
    to ease the burden of objective function evaluations, when a single evaluation
    requires a significant amount of resources.
Providing convenience functions to connect R with the 'Spotify' application programming interface ('API'). At first it aims to help setting up 
  the OAuth2.0 Authentication flow. The default output of the get_*() functions is tidy, but optionally the functions could return the raw response from the 
  'API' as well. The search_*() and get_*() functions can be combined. See the vignette for more information and examples and the official Spotify for 
  Developers website <https://developer.spotify.com/documentation/web-api/> for information about the Web 'API'.
An R wrapper for pulling data from the 'Spotify' Web API 
  <https://developer.spotify.com/documentation/web-api/> in bulk, or post items on a
  'Spotify' user's playlist.
Implements additional models, simulation tools, and interfaces as extensions to 'SPOT'.
    It provides tools for hyperparameter tuning via 'keras/tensorflow', interfacing 'mlr', for performing Markov chain simulations, 
    and for sensitivity analysis based on sequential bifurcation methods as described in Bettonvil and Kleijnen (1996). 
    Furthermore, additional plotting functions for output from 'SPOT' runs are implemented.
    Bartz-Beielstein T, Lasarczyk C W G, Preuss M (2005) <doi:10.1109/CEC.2005.1554761>.
    Bartz-Beielstein T, Zaefferer M, Rehbach F (2021) <arXiv:1712.04076>.
    Bartz-Beielstein T, Rehbach F, Sen A, Zaefferer M <arXiv:2105.14625>.
    Bettonvil, B, Kleijnen JPC (1996) <doi:10.1016/S0377-2217(96)00156-7>.
An algorithm to cluster satellite hot spot data spatially and temporally.
In spatial data, information of two neighboring units are generally very similar. 
  For spatial sampling, it is therefore more efficient to select samples that are well spread out in space. Often, the interest lies not only in estimating a measure at one point in time, but rather in estimating several points in time to also study the evolution. 
  Three new methods called Orfs (Optimal Rotation with Fixed sample Size), Orsp (Optimal Rotation with Spread sample), and Spot (Spatial and Optimally Temporal Sampling) are implemented in this package. Orfs allows to select temporal samples with fixed size. Orsp selects spatio-temporal samples with random size that are well spread out in space at each point in time. And Spot generates spread sample with fixed sample size at each wave. These methods provide an optimal time rotation of the selected units using the systematic sampling.
Summary ellipses superimposed on a scatter plot contain all bi-variate summary
            statistics for regression analysis. Furthermore, the outer ellipse flags potential
            outliers. Multiple groups can be compared in terms of centers and spreads as illustrated
            in the examples.
We propose a novel two-step procedure to combine epidemiological
    data obtained from diverse sources with the aim to quantify risk factors
    affecting the probability that an individual develops certain disease such as
    cancer. See  Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford,
    Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach
    for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical
    Association, 109:505, 11-23,  <doi:10.1080/01621459.2013.870904>.
The developed package can be used to generate a spatial population for different
             levels of relationships among the dependent and auxiliary variables along with spatially
             varying model parameters. A spatial layout is designed as a [0,k-1]x[0,k-1] square region on which observations
             are collected at (k x k) lattice points with a unit distance between any two neighbouring points along the horizontal
             and vertical axes. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).<doi:10.1080/10485252.2018.1499907>.
             The generated spatial population can be utilized in Geographically Weighted Regression model based analysis for studying the 
             spatially varying relationships among the variables. Furthermore, various statistical analysis can be performed on this spatially 
             generated data.
Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in <doi:10.1007/s10109-009-0100-1> and <doi:10.1080/13658816.2011.586327>.
Methods for flexible estimation of conditional density and quantile function, as well as model agnostic tools for analyzing quantile covariate effect and variable importance. The estimation method implements the semi-parametric quantile regression model described in Xu and Reich (2021) <doi:10.1111/biom.13576>, and the model agnostic tools extend accumulative local effects (ALE) to quantile regression setting.
Sparse arrays interpreted as multivariate polynomials.  Uses 'disordR' discipline (Hankin, 2022, <doi:10.48550/ARXIV.2210.03856>).  To cite the package in publications please use Hankin (2022) <doi:10.48550/ARXIV.2210.10848>.
The notion of spreading activation is a prevalent metaphor in the cognitive sciences. This package provides the tools for cognitive scientists and psychologists to conduct computer simulations that implement spreading activation in a network representation. The algorithmic method implemented in 'spreadr' subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (2011, Frontiers), who viewed activation as a fixed cognitive resource that could spread among nodes that were connected to each other via edges or connections (i.e., a network). See Vitevitch, M. S., Ercal, G., & Adagarla, B. (2011). Simulating retrieval from a highly clustered network: Implications for spoken word recognition. Frontiers in Psychology, 2, 369. <doi:10.3389/fpsyg.2011.00369> and Siew, C. S. Q. (2019). spreadr: A R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910-929. <doi: 10.3758/s13428-018-1186-5>.
The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.
Provides a function for the estimation of parameters 
		in a binary regression with the skew-probit link function. 
		Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented,
		as described in DongHyuk Lee and Samiran Sinha (2019+) <doi:10.1080/00949655.2019.1590579>.
Processing and analysis of field collected or simulated sprinkler 
    system catch data (depths) to characterize irrigation uniformity and efficiency using
    standard and other measures. Standard measures include the Christiansen coefficient
    of uniformity (CU) as found in Christiansen, J.E.(1942, ISBN:0138779295,
    "Irrigation by Sprinkling"); and distribution uniformity (DU), potential
    efficiency of the low quarter (PELQ), and application efficiency of the low quarter (AELQ)
    that are implementations of measures of the same notation in Keller, J. and Merriam, 
    J.L. (1978) "Farm Irrigation System Evaluation: A Guide for Management"
    <https://pdf.usaid.gov/pdf_docs/PNAAG745.pdf>. spreval::DU.lh is similar to spreval::DU
    but is the distribution uniformity of the low half instead of low quarter as in DU.
    spreval::PELQT is a version of spreval::PELQ adapted for traveling systems instead
    of lateral move or solid-set sprinkler systems. The function spreval::eff is
    analogous to the method used to compute application efficiency for furrow irrigation
    presented in Walker, W. and Skogerboe, G.V. (1987,ISBN:0138779295, "Surface
    Irrigation: Theory and Practice"),that uses piecewise integration of infiltrated
    depth compared against soil-moisture deficit (SMD), when the argument "target"
    is set equal to SMD.  The other functions contained in the package provide 
    graphical representation of sprinkler system uniformity, and other standard
    univariate parametric and non-parametric statistical measures as applied to
    sprinkler system catch depths. A sample data set of field test data spreval::catchcan
    (catch depths) is provided and is used in examples and vignettes. Agricultural systems
    emphasized, but this package can be used for landscape irrigation evaluation, and a
    landscape (turf) vignette is included as an example application.
Calculate species richness functions for rarefaction and
    extrapolation.
Recently, regularized variable selection has emerged as a powerful tool to identify and dissect gene-environment interactions. Nevertheless, in longitudinal studies with high dimensional genetic factors, regularization methods for G×E interactions have not been systematically developed. In this package, we provide the implementation of sparse group variable selection, based on both the quadratic inference function (QIF) and generalized estimating equation (GEE), to accommodate the bi-level selection for longitudinal G×E studies with high dimensional genomic features. Alternative methods conducting only the group or individual level selection have also been included. The core modules of the package have been developed in C++.
Computes the extended spring indices (SI-x) and false spring 
    exposure indices (FSEI). The SI-x indices are standard indices used for 
    analysis in spring phenology studies. In addition, the FSEI is also from 
    research on the climatology of false springs and adjusted to include an 
    early and late false spring exposure index. The indices  include the 
    first leaf index, first bloom index, and false spring exposure indices, 
    along with all calculations for all functions needed to calculate 
    each index. The main function returns all indices, but each function can 
    also be run separately. 
    Allstadt et al. (2015) <doi: 10.1088/1748-9326/10/10/104008> 
    Ault et al. (2015) <doi: 10.1016/j.cageo.2015.06.015> 
    Peterson and Abatzoglou (2014) <doi: 10.1002/2014GL059266>
    Schwarz et al. (2006) <doi: 10.1111/j.1365-2486.2005.01097.x> 
    Schwarz et al. (2013) <doi: 10.1002/joc.3625>.
An R data package containing setlists from all Bruce Springsteen concerts over 1973-2021. 
    Also includes all his song details such as lyrics and albums. Data extracted from: 
    <http://brucebase.wikidot.com/>.
An implementation of a computationally efficient method to fit large-scale interaction models based on the reluctant interaction selection principle. The method and its properties are described in greater depth in Yu, G., Bien, J., and Tibshirani, R.J. (2019) "Reluctant interaction modeling", which is available at <arXiv:1907.08414>.
The seq_ttest() function is the implementation of Abraham
    Wald’s (1947) <doi:10.2134/agronj1947.00021962003900070011x> Sequential Probability Ratio Test (SPRT) for the test of
    a normal mean (difference) with unknown variance in R (R Core Team, 2018).
    It performs sequential t tests developed by Rushton (1950) <doi:10.2307/2332385>, Rushton (1952) <doi:10.2307/2334026> and
    Hajnal (1961) <doi:10.2307/2333131>, based on the SPRT. Specifically, seq_ttest() performs
    one-sample, two-sample, and paired t tests for testing one- and
    two-sided hypotheses.  The test is to be applied to the data during
    the sampling process, ideally after each observation. At any stage, it
    will return a decision to either continue sampling or terminate and
    accept one of the specified hypotheses. For more information on the
    SPRT t test, see Schnuerch & Erdfelder (2019) <doi:10.1037/met0000234>.
Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998, ISSN:0282-423X), as well as other order sample designs by Rosén (1997, <doi:10.1016/S0378-3758(96)00186-3>), and generate appropriate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012, <doi:10.1111/j.1751-5823.2011.00166.x>).
The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package.
Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models.  All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale.
Estimates model parameters of soil hydraulic property functions by inverting measured data. A wide range of hydraulic models, weighting schemes,
    global optimization algorithms, Markov chain Monte Carlo samplers, and extended statistical analyses of results are provided.
	Prediction of soil hydraulic property model parameters and common soil properties using pedotransfer functions is facilitated. 
	Parameter estimation is based on identically and independentally distributed (weighted) model residuals, and simple model selection criteria (Hoege, M., Woehling, T., and Nowak, W. (2018) <doi:10.1002/2017WR021902>) can be calculated.
	The included models are the van Genuchten-Mualem in its unimodal, bimodal and trimodal form, the the Kosugi 2 parametric-Mualem model, and the Fredlund-Xing model.
	All models can be extended to account for non-capillary water storage and conductivity (Weber, T.K.D., Durner, W., Streck, T. and Diamantopoulos, E. (2019) <doi:10.1029/2018WR024584>. 
	The isothermal vapour conductivity (Saito, H., Simunek, J. and Mohanty, B.P. (2006) <doi:10.2136/vzj2006.0007>) is calculated based on 
	volumetric air space and a selection of different tortuosity models: 
	(Grable, A.R., Siemer, E.G. (1968) <doi:10.2136/sssaj1968.03615995003200020011x>, Lai, S.H., Tiedje J.M., Erickson, E. (1976) <doi:10.2136/sssaj1976.03615995004000010006x>,
	Moldrup, P., Olesen, T., Rolston, D.E., and Yamaguchi, T. (1997) <doi:10.1097/00010694-199709000-00004>, Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2004) <doi:10.2136/sssaj2004.7500>, 
	Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2005) <doi:10.1097/01.ss.0000196768.44165.1f>, Millington, R.J., Quirk, J.P. (1961) <doi:10.1039/TF9615701200>,
	Penman, H.L. (1940) <doi:10.1017/S0021859600048164>, and Xu, X, Nieber, J.L. Gupta, S.C. (1992) <doi:10.2136/sssaj1992.03615995005600060014x>).
Program uses method of polynomial of variable degrees to interpolate gridded data preserving monotonicity and/or convexity or none. Method is implemented for univariate and bivariate cases. If values of derivatives are provided, spline will fix them,if not program will estimate them numerically. Package written purely in R.
Provides basic functionality for labeling 
        iso- & anisotropic percolation clusters on 2D & 3D square lattices 
        with various lattice sizes, occupation probabilities, von Neumann & 
        Moore (1,d)-neighborhoods, and random variables weighting 
        the percolation lattice sites.
An implementation of the feature Selection procedure by Partitioning the entire Solution Paths
            (namely SPSP) to identify the relevant features rather than using a single tuning parameter. 
            By utilizing the entire solution paths, this procedure can obtain better selection accuracy than 
            the commonly used approach of selecting only one tuning parameter based on existing criteria, 
            cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) 
            <doi:10.1214/18-EJS1434>). It is more stable and accurate (low false positive and 
            false negative rates) than other variable selection approaches. In addition, it can be flexibly 
            coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized 
            estimators.
A collection of functions to test and estimate Seemingly 
    Unrelated Regression (usually called SUR) models, with spatial structure, by maximum 
    likelihood and three-stage least squares. The package estimates the 
    most common spatial specifications, that is, SUR with Spatial Lag of 
    X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), 
    SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), 
    SUR with Spatial Durbin Error Model (called SUR-SDEM), 
    SUR with Spatial Autoregressive terms and Spatial Autoregressive 
    Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X 
    regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM).
    The methodology of these models can be found in next references 
    Minguez, R., Lopez, F.A., and Mur, J. (2022) <doi:10.18637/jss.v104.i11>
    Mur, J., Lopez, F.A., and Herrera, M. (2010) <doi:10.1080/17421772.2010.516443> 
    Lopez, F.A., Mur, J., and Angulo, A. (2014) <doi:10.1007/s00168-014-0624-2>.
A set of reliable routines to ease semiparametric survival regression modeling based on Bernstein polynomials. 'spsurv' includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) <arXiv:2003.10548>.
A design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more. For additional details, see Dumelle et al. (2023) <doi:10.18637/jss.v105.i03>.
The systemPipeShiny (SPS) framework comes with many useful utility functions. However, installing the whole framework is heavy and takes some time. If you like only a few useful utility functions from SPS, install this package is enough.
A collection of algorithms related to Sierpinski 
  pedal triangle (SPT).
Fits, spatially predicts, and temporally forecasts space-time data using Gaussian Process (GP): (1) spatially varying coefficient process models and (2) spatio-temporal dynamic linear models. Bakar et al., (2016). Bakar et al., (2015).
Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) <doi:10.1002/sim.7622>, the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) <doi:10.1002/sim.8315>, the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) <doi:10.1007/s10463-021-00787-2>, the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) <doi:10.1002/sim.9150> that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2022) <doi:10.1080/00224065.2022.2081104>, and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) <doi:10.1080/24725854.2019.1696496>.
A set of functions that can be used to spatially thin
    species occurrence data. The resulting thinned data can be used in ecological
    modeling, such as ecological niche modeling.
Fits, spatially predicts and temporally forecasts large amounts of space-time data using  [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar and Sahu (2015) <doi:10.18637/jss.v063.i15>.
Implements semiparametric transformation model two-phase estimation using calibration weights. The method in Fong and Gilbert (2015) Calibration weighted estimation of semiparametric transformation models for two-phase sampling. Statistics in Medicine <DOI:10.1002/sim.6439>.
Performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) <doi:10.1007/s10651-007-0035-y>. The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package.
Uncertainty propagation analysis in spatial environmental  modelling following methodology
         described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> 
         and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions
         for examining the uncertainty propagation starting from input data and model parameters,
         via the environmental model onto model outputs. The functions include uncertainty model specification,
         stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques.
         Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled.
         Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for.
         The MC realizations may be used as input to the environmental models called from R, or externally.
Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson.  2014.  An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.
A set of tools for the peak filtering of mass spectrometry
  imaging data (MSI or IMS) based on spatial distribution of signal. Given a 
  region-of-interest (ROI), representing the spatial region where the informative
  signal is expected to be localized, a series of filters determine which peak
  signals are characterized by an implausible spatial distribution. The filters
  reduce the dataset dimensionality and increase its information vs noise ratio,
  improving the quality of the unsupervised analysis results, reducing data
  dimensionality and simplifying the chemical interpretation.
Data on the Spy vs. Spy comic strip of Mad magazine, created and
   written by Antonio Prohias.
The overall performance of soil ecosystem services and productivity greatly relies on soil health, making it a crucial indicator. The evaluation of soil physical, chemical, and biological parameters is necessary to determine the overall soil quality index. In our package, three commonly used methods, including linear scoring, regression-based, and principal component-based soil quality indexing, are employed to calculate the soil quality index. This package has been developed using concept of Bastida et al. (2008) and Doran and Parkin (1994) <doi:10.1016/j.geoderma.2008.08.007> <doi:10.2136/sssaspecpub35.c1>.
Runs 'SQL' statements on in-memory data frames within a temporary in-memory 'duckdb' data base.
The sqldf() function is typically passed a single argument which 
	is an SQL select statement where the table names are ordinary R data 
	frame names.  sqldf() transparently sets up a database, imports the 
	data frames into that database, performs the SQL select or other
	statement and returns the result using a heuristic to determine which 
	class to assign to each column of the returned data frame.  The sqldf() 
	or read.csv.sql() functions can also be used to read filtered files 
	into R even if the original files are larger than R itself can handle.
	'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported.
Includes functions for interacting with common meta data fields, 
  writing insert statements, calling functions, and more for 'T-SQL' and 'Postgresql'.
sqliter helps users, mainly data munging practioneers, to organize
    their sql calls in a clean structure. It simplifies the process of
    extracting and transforming data into useful formats.
A tool for working with 'SQLite' databases. 'SQLite' has some idiosyncrasies and limitations that impose some hurdles to the R developer who is using this database as a repository. For instance, 'SQLite' doesn't have a date type and 'sqliteutils' has some functions to deal with that.
The nature of working with structured query language ('SQL') scripts 
             efficiently often requires the creation of temporary tables and there 
             are few clean and simple 'R' 'SQL' execution approaches that allow 
             you to complete this kind of work with the 'R' environment. This 
             package seeks to give 'SQL' implementations in 'R' a little love 
             by deploying functions that allow you to deploy complex 'SQL' 
             scripts within a typical 'R' workflow.
Wrapper for the non-validating 'SQL' parser 'Python' module 'sqlparse' <https://github.com/andialbrecht/sqlparse>. It allows parsing, splitting, and formatting 'SQL' statements.
A rendering tool for parameterized SQL that also translates into
  different SQL dialects.  These dialects include 'Microsoft SQL Server', 'Oracle', 
  'PostgreSql', 'Amazon RedShift', 'Apache Impala', 'IBM Netezza', 'Google BigQuery', 'Microsoft PDW', 'Snowflake', 
  'Azure Synapse Analytics Dedicated', 'Apache Spark', and 'SQLite'.
Provides utilities for generating SQL queries (particularly CREATE
    TABLE statements) from R model objects. The most important use case is
    generating SQL to score a generalized linear model or related model
    represented as an R object, in which case the package handles parsing
    formula operators and including the model's response function.
Provides a helper function, to bulk read 'SQL' code from separate files and load it into an 'R' list, where the list elements contain the individual statements and queries as strings. This works by annotating the 'SQL' code with a name comment, which also will be the name of the list element.